The Commercial SaaS Blueprint

Overview

Master Blueprint: RWE Decision Support & Cognitive Acquisition

The strategy shifts from generic data science to selling RWE Decision Support Systems (CDSS) and Cognitive Acquisition Engines.

We bypass cloud PHI liabilities by running predictive mathematics locally, and we monetize independent verticals: Predictive RWE Engines, Psychometric APIs (PQM), and Immersive Language System (Veloz).

The Architect: Aaron Del Re, PhD (Background & Credentials)

I am an independent statistician with a PhD and a quantitative background spanning federal research (VHA, DoD) to independent stats consulting. My academic and research background is broad, encompassing meta-analysis methodology, longitudinal health services research, epidemiology, and psychotherapy research (my full publication history is available on my CV here: https://acdelre.github.io/CV).

In my consulting and technical work, I provide a wide range of services, including advanced statistical modeling (e.g., longitudinal linear mixed models [LLMM]), Real-World Evidence (RWE) analyses, and data architecture. I develop automated and reproducible R and Quarto pipelines that eliminate manual data-entry errors (saving many RA manual hrs), generating dynamic, audit-ready reports and complex visualizations for clinical and research data.

This portfolio represents my active development of interactive statistical applications and localized prediction engines (e.g., RWE Analytics Portal, Empirical Predictive Dashboard, PQM App, Compute.es, and Veloz).

Credentials & Full History (PDF Downloads):

View Industry Resume (Agent-Readable Text)

Aaron Del Re, PhD
San Diego, CA | +1 831.332.4829 | stats@acdelre.com | acdelre.github.io
Professional Summary
PhD Biostatistician with over 15 years of experience building predictive models, automated reporting pipelines,
and interactive data applications for behavioral and health data. Strong focus on Hierarchical Linear/Mixed-
Effects Models (Longitudinal), N-of-1 methodology, and software engineering. I specialize in turning raw
patient data into scalable clinical dashboards and empirical predictive engines for B2B SaaS.
Technical Skills
• Statistical Modeling: Hierarchical Linear Models/Mixed-Effects Models (Longitudinal), Meta-
Analysis (Multilevel, Network, Random Effects), N-of-1 Trial Design.
• Study Design & Causal Inference: Propensity Score Matching, Power Analysis, Simulation Studies.
• Programming & Software Engineering: R (Expert), Python, SQL, JavaScript, Web App Develop-
ment (PWA), Synthetic Data Generation.
• Reporting & Visualization: Quarto, Shiny, HTML/CSS, Git/GitHub, ggplot2.
• Psychometrics: CFA/EFA, Path Analysis, Scale Development.
Software & Application Development
• Empirical Predictive Engines: Architected decoupled statistical dashboards executing Longitudinal
Linear Mixed Models (LLMM) for B2B SaaS platforms.
• PQM (Practice Quality Mindfulness Tracker): Developed an application based on my validated
Practice Quality-Mindfulness scale (Del Re et al., 2013) to track the quality of mindfulness practice
and optimize mental health outcomes.
• Compute.ES Calculator: Built a privacy-first web application for calculating meta-analysis effect
sizes (𝑑, 𝑔, 𝑟, 𝑂𝑅) with batch CSV processing capabilities.
• Stanford C-Score Tool: Engineered a live web calculator that scrapes Google Scholar to compute
academic ranking percentiles based on the Ioannidis/Stanford methodology.
• Veloz PWA: Designed and deployed a scientifically-grounded, serverless Progressive Web App for
language acquisition utilizing the “Shadow Loop” method.
• R Packages (CRAN): Author and maintainer of compute.es, MAd, and MAc, which have amassed
thousands of downloads and over 600+ academic citations.
Professional Experience
Principal Biostatistician & Data Architect
Del Re Data Consulting | 2009–Present - Real-World Evidence (RWE) Automation: Built an automated
R/Quarto pipeline to analyze large-cohort and N-of-1 longitudinal data, producing personalized patient
efficacy reports for a B2B health application. - Grant Methodological Design: Lead Statistician for a
multi-site NIH grant proposal, designing the full analytic and statistical plan for a high-stakes occupational
health intervention. - EHR Data Pipelines: Designed the data cleaning and analysis pipeline to analyze
longitudinal patient outcomes for a major Behavioral Health EHR system.
- Predictive Algorithm
Engineering: Created the core predictive algorithm forecasting patient treatment trajectories based on
real-time longitudinal data for a psychotherapy outcomes platform.
Research Psychologist & Lead Statistician
University of Kassel/University of Zürich | 2020–Present - Directed multilevel meta-analysis projects analyzing
therapeutic alliance and psychological outcomes. - Wrote methodological reports and statistical analysis
plans for peer-reviewed medical journals.
1

Research Psychologist
Navy Health Research Center (NHRC) | 2018–2020 - Led large-scale epidemiological data analysis on
psychological readiness among active-duty military personnel. - Applied generalized linear models to massive
DoD datasets to track military health trends.
Lead Health Services Program Analyst
Palo Alto VA (Health Services Research) | 2013–2017 - Executed Health Services Research by building and
deploying SQL-based data dashboards used nationwide by VA administrators to monitor healthcare efficacy.
- Translated raw metrics into automated reporting systems for executive leadership.
Postdoctoral Fellow
Stanford University School of Medicine (Health Services Research) | 2011–2013 - Conducted advanced
biostatistical modeling, predictive analysis, and health services research for Stanford’s medical research teams.
Education
• PhD, Counseling Psychology (Minor in Quantitative Methods) – University of Wisconsin-Madison
| 2011
Selected Publications
• Academic Portfolio: 78+ peer-reviewed publications with 12,000+ citations.
• For a complete list of publications, interactive dashboards, and open-source code, please visit:
acdelre.github.io
2

View Full Academic CV (Agent-Readable Text)

A. C. Del Re, PhD
CV (04-2026)
Contact
Information
Del Re Data Consulting
+1 831.332.4829 T
Statistical Consulting Services
stats@acdelre.com B
San Diego, CA, USA
acdelre.com m
Scholar
ResearchGate
Publons
Twitter
Employment
Del Re Data and Statistical Consulting, San Diego, CA (2009-)
 Design and develop interactive statistical applications (e.g., com-
pute.es), Real-World Evidence (RWE) dashboards, and customized
predictive models to support clinical and organizational research.
 Implement reproducible, code-driven workflows (R, Quarto, SQL)
that process complex clinical datasets into publication-ready tables,
automated reports, and accurate data visualizations.
 Provide advanced methodological consulting and data analysis, spe-
cializing in longitudinal modeling, mixed-effects models, and meta-
analysis, for universities, hospitals, and international research teams.
 Upwork Top-Rated Freelancer with a proven record of delivering
rigorous statistical solutions and analytical tools that meet strict
academic and industry standards. See client testimonials.
Research Psychologist (remote), Kassel, Germany & Z¨urich, Switzer-
land (2020-)
Department of Psychology, University of Kassel/Z¨urich
 Lead and involved in several psychotherapy research projects, mostly
focused on therapeutic alliance, therapist effects, and statistical
methodology.
 Contribute to several phases of study life-cycle including initial re-
search ideas, data and statistical analyses, and co-authoring pub-
lished reports in peer-reviewed journals.
 Provide additional statistical support as needed.
Research Psychologist, San Diego, CA (2018-2020)
Health and Behavioral Sciences, Navy Health Research Center (NHRC)
 Involved in multiple epidemiological research projects focused on
psychological resilience and readiness among active duty military.
Contribute to all phases of study life-cycle including initial research
ideas, grant-writing, preparing IRB protocols, data analysis, and re-
porting of findings in multiple formats (co-authoring white papers
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and published reports in peer-reviewed journals). Consult as statis-
tician for multiple PI’s and provide in-house statistical support and
presentations/workshops for NHRC staff.
Health Services Program Analyst/Co-Investigator, Palo Alto, CA
(2013-2017)
Office of Mental Health Operations, U.S. Department of Veterans Af-
fairs
 Involved in (and took lead on) multiple program evaluation/research
projects, epidemiological, and quality improvement studies examin-
ing areas of mental health/substance abuse, and healthcare utiliza-
tion. Contributed to all phases of study life-cycle including grant-
writing, preparing IRB protocols, data analysis, and reporting of
findings in multiple formats. Designed, developed and implemented
quality metrics, decision support tools, reports and dashboards uti-
lized by VA providers and administrators nation-wide.
 Supervisors: Jodie Trafton, PhD & Alex Harris, PhD
Health Services Research Postdoctoral Fellow, Palo Alto, CA (2011
Education
- 2013 )
Center for Health Care Evaluation, Department of Veterans Affairs &
School of Medicine, Stanford University
 Mentors: Alex Harris, PhD & John Finney, PhD
University of Wisconsin-Madison, Madison, WI
PhD, Counseling Psychology, August 2011
 Dissertation topic:
Mindfulness Practice Quality:
An important
consideration in mindfulness intervention outcome.
 Advisor: Bruce E. Wampold, PhD
University of New Mexico, Albuquerque, NM
MA, Counselor Education, December 2004
 Advisor: Markus P. Bidell, PhD
 Area of Study: Community/Agency Counseling
BA, University Studies, June 2002
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Apps &
Dashboards
11. Del Re, A. C. (2026). Empirical Predictive Engines. An interactive
statistical application utilizing longitudinal mixed-effects models for
real-world evidence (RWE) tracking and outcome prediction. [link]
10. Del Re, A. C. (2026). PQM: Practice Quality Mindfulness Tracker.
A mobile application designed to measure and evaluate meditation
practice quality based on the Practice Quality-Mindfulness scale.
[link]
9. Del Re, A. C. (2026). Compute ES: Effect Size Calculator. An
open-access statistical web application based on the compute.es R
package for computing effect sizes (d, g, r, OR). [link]
8. Del Re, A. C. (2026). Stanford C-Score Ranking Tool. An in-
teractive application that computes composite citation impact scores
based on the Ioannidis ranking methodology. [link]
7. Del Re, A. C. (2025). Veloz: Language Acquisition via Language
Islands.
An interactive psycholinguistic tool implementing audio-
lingual techniques to facilitate language retention. [link]
6. Del Re, A. C. (2025). Interactive Slang Explorer. A data visual-
ization tool mapping the cultural context and regional variations of
colloquial MX Spanish. [link]
5. Del Re, A. C. (2015). Budget for VHA Grants: Calculate salary
needs. Developed in R Shiny. [link]
4. Del Re, A. C. (2015). Mental Health Provider Survey for VHA.
Developed in Microsoft’s Report Builder.
3. Del Re, A. C. (2015). Veteran Satisfaction for VHA. Developed in
Microsoft’s Report Builder.
2. Baldwin, S. A. & Del Re, A. C. (2015). Open Access Family Ther-
apy Meta-Analysis Web Applications in R. Developed in R Shiny.
[link]
1. Baldwin, S. A. & Del Re, A. C. (2015). Open Access Working
Alliance Meta-Analysis Web Applications in R. Developed in R Shiny.
[link]
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[Citations = 12,500+; h-index = 36; i10-index = 46]
Publications
65. Wu, Y. K., Watson, H. J., Del Re, A. C. , Finch, J. E., Hardin,
S. L., Dumain, A. S., . . . & Baker, J. H. (2025). Reply to Keeler, J.
L.; Steinh¨auser, J. L. Comment on “Wu et al. Peripheral Biomarkers
of Anorexia Nervosa: A Meta-Analysis. Nutrients 2024, 16, 2095”.
Nutrients, 17(17), 2874.
64. Zvorsky, I., Kulpa, J., Mechtler, L.L., Ralyea, C.C., Lombardo, J.,
Del Re, A. C. & Bonn-Miller, M.O. (2024). Urinalysis and per-
ceived effects following two-week use of a commercial broad spectrum
cannabidiol product. Experimental and Clinical Psychopharmacol-
ogy.doi:10.1037/pha0000747
63. Fl¨uckiger, C. & Del Re, A. C.. (2024). Do some theory-specific
psychotherapies outperform others (relative efficacy)?
A stepwise
approach to contextualizing the results of randomized controlled tri-
als with direct psychotherapy comparisons. In F. T. L. Leong, J.
L. Callahan, J. Zimmerman, M. J. Constantino, & C. F. Eubanks
(Eds.), APA handbook of psychotherapy: Evidence-based practice,
practice-based evidence, and contextual participant-driven practice (pp.
7-24).
American Psychological Association.
doi:10.1037/0000354-
002
62. Wu, Y. K., Watson, H. J., Del Re, A. C. , Finch, J. E., Hardin, S.
L., Dumain, A. S., ... & Baker, J. H. (2024). Peripheral Biomarkers
of Anorexia Nervosa: A Meta-Analysis.
Nutrients, 16(13), 2095.
doi:10.3390/nu16132095
61. Fl¨uckiger, C., Munder, T., Del Re, A. C. & Solomonov, N.. (2023).
Strength-based methods - A narrative review and comparative mul-
tilevel meta-analysis of positive interventions in clinical settings. Psy-
chotherapy Research, 33:7, 856-872, doi:10.1080/10503307.2023.2181718
60. Fl¨uckiger, C., Munder, T., Del Re, A. C. & Solomonov, N.. (In
Press). Strength- and Resilience-based Methods. In Hill & Norcross’
Psychotherapy Skills and Methods that Work. New York, NY: Oxford
University Press.
59. Del Re, A. C., Fl¨uckiger, C. & Goldberg, S. (2022). Mindfulness
Practice Quality: The PQ-M Scale. In: Medvedev, O.N., Kr¨ageloh,
C.U., Siegert, R.J., Singh, N.N. (eds) Handbook of Assessment in
Mindfulness Research. Springer, Cham. doi:10.1007/978-3-030-77644-
2 69-1
58. Fl¨uckiger, C. & Del Re, A. C.. (2023). Do some theory-specific
psychotherapies outperform others (relative efficacy)? A stepwise ap-
proach to contextualizing the results of randomized controlled trials
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with direct psychotherapy comparisons.
In the APA-Handbook of
psychotherapy. New York, NY: Oxford University Press.
57. Fl¨uckiger, C., Carratta, K., Del Re, A. C., Probst, G., Visla, A.,
Gomez Penedo, J. M. & Wampold, B. E. (2022). The relative efficacy
of bona fide cognitive behavioral therapy and applied relaxation for
generalized anxiety disorder at follow-up: A longitudinal multilevel
meta-analysis. Journal of Consulting and Clinical Psychology.
56. Del Re, A. C., Fl¨uckiger, C., Horvath, A., Wampold, B. E. (2021).
Examining therapist effects in the alliance-outcome relationship: A
multilevel meta-analysis.
Journal of Consulting and Clinical Psy-
chology.
doi:10.1037/ccp0000637
55. Walter, K. H., Otis, N. P., Del Re, A. C., Kohen, C. B., Glassman,
L. H., Ober, K. M., & Hose, M. K. (2021). The National Veter-
ans Summer Sports Clinic: Change and Duration of Psychological
Outcomes. Psychology of Sport and Exercise, 101939.
54. Miller, S. D., Chow, D., Wampold, B. E., Hubble, M. A., Del Re,
A. C., Maeschalck, C., & Bargmann, S. (2020). To be or not to be
(an expert)? Revisiting the role of deliberate practice in improving
performance. High Ability Studies, 31(1), 5-15.
53. Boltz, J., Del Re, A. C., Koenig, H., Schmied, E., McRoy, R. M.
& Yablonsky, A. M. (2020). Caregiver Health: An Epidemiological
Study of Active Duty Parents with Special Needs Children. Military
Behavioral Health.
52. Tannenbaum, K., Boltz, J., Del Re, A. C., Carino, S. R. & Yablon-
sky, A. M. (2020).
Short-Term Impact of a Stress Management
Course on Shipboard Sailors. NHRC Whitepaper.
51. Fl¨uckiger, C., Rubel, J., Del Re, A. C., Horvath, A., Wampold,
B. E., ... Barber, J. P. (2020). The reciprocal relationship between
alliance and early treatment symptoms: A two-stage individual par-
ticipant data meta-analysis. Journal of Consulting and Clinical Psy-
chology.
50. Van Horn, D., Goodman, J., Lynch, D., Bonn-Miller, M., Thomas,
T., Del Re, A. C., Babson, K. & McKay, J. (2020). The predictive
validity of the Progress Assessment, a clinician administered instru-
ment for use in measurement-based care approaches to substance use
disorders. Psychiatry Research
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49. Fl¨uckiger, C., Del Re, A. C., Wlodasch, D., Horvath, A., Solomonov,
N. & Wampold, B. E. (2020). Assessing the alliance-outcome asso-
ciation adjusted for patient characteristics and treatment processes:
A meta-analytic summary of direct comparisons. Journal Of Coun-
seling Psychology.
48. Belding, J. N., Koenig, H. G., McAnany, J. M., Del Re, A. C.,
Bonkowski, J.F. & Thomsen, C.J. (2020). In the trenches of military
epidemiological research: Lessons learned from large-scale archival
data projects. SAGE Research Methods Cases: Medicine & Health.
47. Fl¨uckiger, C., Del Re, A. C., Wampold, B. E. & Horvath, A. O.
(2019). The alliance in adult psychotherapy. In J. C. Norcross’ Psy-
chotherapy relationships that work: Volume 1: Evidence-Based Ther-
apist Contributions. New York, NY: Oxford University Press.
46. Miller, S. D., Chow, D., Wampold, B. E., Hubble, M. A., Del Re,
A. C., Maeschalck, C., & Bargmann, S. (2018). To be or not to be
(an expert)? Revisiting the role of deliberate practice in improving
performance. High Ability Studies.
45. Fl¨uckiger, C., Del Re, A. C., Barth, J. Hoyt, W. T., Levitt, H.,
Munder, T., Spielmans, G. I., Swift, J. K., Visla, A., Wampold,
B. E. (2018). Considerations of how to conduct meta-analyses in
psychological interventions. Psychotherapy Research.
doi:10.1080/10503307.2018.1430390
44. Hoyt, W. T. & Del Re, A. C. (2018). Effect size calculation in
meta-analyses of psychotherapy outcome research.
Psychotherapy
Research.
43. Fl¨uckiger, C., Del Re, A. C., Wampold, B. E. & Horvath, A. O.
(2018). The working alliance in psychotherapy Psychotherapy Re-
search.
42. Taylor, J., Carr-Lopez, S., Robinson, A., Malmstrom, R., Duncan,
K., Maniar, A., Del Re, A. C. & Carmichael, J. M. (2017). De-
terminants of treatment eligibility in veterans with hepatitis C viral
infection. Clinical therapeutics, 39(1), 130-137.
41. Wampold, B. E., Fl¨uckiger, C., Del Re, A. C., Yulish, N. E., Frost,
N. D., Pace, B. T. & Hilsenroth, M. J. (2017). In pursuit of truth: A
critical examination of meta-analyses of cognitive behavior therapy.
Psychotherapy Research, 27(1), 14-32.
40. Baldwin, S. A. & Del Re, A. C. (2016).
Open Access Meta-
Analysis for Psychotherapy Research. Journal of Counseling Psy-
chology, 63(3), 249.
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39. Fl¨uckiger, C. & Del Re, A. C. (2016). The sleeper effect between
psychotherapy orientations: a strategic argument of sustainability
of treatment effects at follow-up. Epidemiology and Psychiatric Sci-
ences. doi:10.1017/S2045796016000780
38. Del Re, A. C. & Fl¨uckiger, C. (2016). Meta-analysis. In Nor-
cross, John C. (Ed); VandenBos, Gary R. (Ed); Freedheim, Donald
K. (Ed); Olatunji, Bunmi O. (Ed), APA handbook of clinical psychol-
ogy: Theory and research (Vol. 2). APA handbooks in psychology.,
(pp. 479-491). Washington, DC: American Psychological Associa-
tion APA, xii, 525 pp. doi:10.1037/14773-022
37. Brennan, P. L., Del Re, A. C., Henderson, P. T., & Trafton, J.
A.(2016). Healthcare system-wide implementation of opioid-safety
guideline recommendations: the case of urine drug screening and
opioid-patient suicide-and overdose-related events in the Veterans
Health Administration. Translational Behavioral Medicine, 1-8.
36. Fl¨uckiger, C., Del Re, A. C. & Wampold, B. E. (2016). The Sleeper
Effect: Artifact or Phenomenon–A brief comment on ”Are the Parts
as Good as the Whole? A Meta-Analysis of Component Treatment
Studies (Bell, Marcus & Goodlad, 2015)”. Journal of Consulting and
Clinical Psychology. doi:10.1037/a0037220
35. Del Re, A. C. (2015). A Practical Tutorial on Meta-analysis in R.
The Quantitative Methods for Psychology, 11(1), 37-50.
34. Fl¨uckiger, C., Horvath, A. O., Del Re, A. C., & Symonds, D.
(2015). Wie wichtig ist die Arbeitsallianz in der Psychotherapie?
Eine meta analytische ¨ubersicht.
33. Fl¨uckiger, C., Horvath, A. O., Del Re, A. C., Symonds, D. &Holzer,
C. (2015). Bedeutung der Arbeitsallianz in der Psychotherapie. Psy-
chotherapeut, 60(3), 187-192.
32. Fl¨uckiger, C., Horvath, A. O., Del Re, A. C., Symonds, D. &Holzer,
C. (2015). Importance of working alliance in psychotherapy. Overview
of current meta-analyses. Psychotherapeut, 60(3), 187-192.
31. Del Re, A. C., Frayne, S. M. & Harris, A.H.S. (2014). Anti-obesity
Medication Use Across the Veterans Health Administration: Patient-
level Predictors of Receipt. Obesity. doi:10.1002/oby.20810
30. Harris, A.H.S., Bowe, T., Del Re, A. C., Finlay, A.K., Oliva, E.,
Rubinsky, A. (2014). Quasi-Experimental Effects of Extended Re-
lease Naltrexone in US Veteran Health Administration Patients with
Alcohol Use Disorders. ACER
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29. Fl¨uckiger, C., Holtforth, M. G., Del Re, A. C. & Lutz, W.(2014).
Exploration von Resilienzen und Ressourcen bei Ver¨anderungsspr¨ungen.
Pers¨onlichkeitsst¨orungen.
28. Fl¨uckiger, C., Del Re, A. C., Munder, T., Heer, S. & Wampold,
B. E. (2014). Enduring effects of evidence-based psychotherapies in
depression and anxiety versus treatment as usual at follow-up: A
longitudinal meta-analysis. Clinical Psychology Review, 34, 367-375.
doi:10.1016/j.cpr.2014.05.001
27. Goldberg, S. B., Del Re, A. C., Hoyt, W. T. & Davis, J. M. (2014),
The secret ingredient in mindfulness interventions? A case for prac-
tice quality over quantity. Journal of Counseling Psychology.
26. Blodgett, J. C., Del Re, A. C., Maisel, N. C. & Finney, J. W.
(2014).
A meta-analysis of topiramate for alcohol use disorders.
ACER.
25. Del Re, A. C., Maciejewski, M. & Harris, A.H.S. (2013). MOVE!
Weight Management Program Across the Veterans Health Adminis-
tration: Patient- and Facility-level Predictors of Utilization. BMC
Health Services Research. doi:10.1186/1472-6963-13-511
24. Del Re, A. C., Spielman, G. I., Fl¨uckiger, C. & Wampold, B. E.
(2013).
Efficacy of New Generation Antidepressants: Differences
seem Illusory. PLoS ONE 8(6): e63509.
doi:10.1371/journal.pone.0063509
23. Del Re, A. C., Maisel, N. C., Blodgett, J. C. & Finney, J. W.
(2013). Intention-to-Treat Analyses and Missing Data Approaches
in Pharmacotherapy Trials for Alcohol Use Disorders. BMJ Open.
22. Del Re, A. C., Maisel, N. C., Blodgett, J. C. & Finney, J. W.
(2013). The declining efficacy of naltrexone pharmacotherapy for
alcohol use disorders over time: A multivariate meta-analysis. Alco-
holism: Clinical and Experimental Research. doi:10.1111/acer.12067
21. Del Re, A. C., Gordon, A. J., Lembke, A. & Harris, A.H.S. (2013).
Utilization of Topiramate to Treat Alcohol Use Disorders in the Vet-
erans Health Administration Addiction Science & Clinical Practice.
doi:10.1186/1940-0640-8-12
20. Del Re, A. C., Maisel, N. C., Blodgett, J. C., Wilbourne, P. &
Finney, J. W. (2013). Placebo group improvement in trials of phar-
macotherapies for alcohol use disorders: A multivariate meta-analysis
examining change over time. Journal of Clinical Psychopharmacol-
ogy. doi:10.1097/JCP.0b013e3182983e73
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19. Babson, K. A., Del Re, A. C., Bonn-Miller, M. & Woodward, S.
(2013). The Comorbidity of Sleep Apnea and Mood, Anxiety, and
Substance Use Disorders among Obese Military Veterans within the
Veterans Health Administration. Journal of Clinical Sleep Medicine.
18. Fl¨uckiger, C., Del Re, A. C., Horvath, A. O., Symonds, D., Ack-
ert, M. & Wampold, B. E. (2013). Substance Use Disorders and
Racial/Ethnic Minorities Matter: A Meta-Analytic Examination of
the Relation Between Alliance and Outcome. Journal of Counseling
Psychology, 60, 610-616. doi:10.1037/a0033161
17. Panos, S. E., Del Re, A. C., Thames A. D., Arentsen, T., Patel, S.,
Castellon, S. A., Singer, E. J.& Hinkin, C. H. (2013). The impact of
neurobehavioral features on medication adherence in HIV: Evidence
from longitudinal models.
AIDS Care: Psychological and Socio-
medical Aspects of AIDS/HIV. doi:10.1080/09540121.2013.802275
16. Budge, S.L., Moore, J.T., Del Re, A. C., Nienhaus, J.B., Baard-
seth, T.P. & Wampold, B.E. (2013). The effectiveness of treatments
for personality disorders: A meta-analysis of direct comparisons.
15. Fl¨uckiger, C., Holtforth, M. G., Del Re, A. C. & Lutz, W.(2013).
Working along sudden gains - Responsiveness on small and subtle
early changes and exceptions. Psychotherapy.
14. Panos, S. E., Hinkin, C. H., Singer, E. J., Thames, A. D., Patel
S., Sinsheimer, J. S., Del Re, A. C., Gelman, B., Morgello, S.,
Moore, D. J. & Levine, A. J. (2013). Apolipoprotein-E genotype and
human immunodeficiency virus-associated neurocognitive disorder:
Modulating effects of older age and disease severity. Neurobehavioral
HIV Medicine.
13. Baardseth, T. P., Goldberg, S. B., Pace, B. T., Minami, T., Wis-
locki, A. P., Frost, N. D., Siddiqui, J. R.,Lindemann, A. M., Kiv-
lighan, D. M., Del Re, A. C., Laska, K. M. & Wampold, B. E.
(2013). Cognitive-Behavioral Therapy versus Other Therapies: Re-
dux. Clinical Psychology Review.
12. Del Re, A. C., Fl¨uckiger, C., Wampold, B. E. & Horvath, A. O.
(2012).
Therapist effects in the alliance-outcome relationship: A
restricted maximum likelihood meta-analysis.
Clinical Psychology
Review, 32, 642-649.
doi:10.1016/j.cpr.2012.07.002
11. Del Re, A. C., Fl¨uckiger, C., Goldberg, S. & Hoyt W. T. (2012).
Monitoring Mindfulness Practice Quality: An Important Considera-
tion in Mindfulness Practice. Psychotherapy Research,23(1), 54-66.
doi:10.1080/10503307.2012.729275
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10. Fl¨uckiger, C., Del Re, A. C., Wampold, B. E. & Horvath, A. O.
(2012). The therapeutic alliance in the context of empirically sup-
ported treatments: A longitudinal meta-analysis. Journal of Coun-
seling Psychology, 59(1), 10-17.
doi:10.1037/a0025749
9. Horvath, A.O., Del Re, A. C., Fl¨uckiger, C. & Symonds, D. (2012).
L’alleanza nella psicoterapia individuale. In J. C. Norcross’ Quando
la relazione psicoterapeutica funziona. Via Leon Pancaldo, Roma:
Sovera Edizioni.
8. Wampold, B. E., Budge, S. L., Laska, K. M., Del Re, A. C., Baard-
seth, T. P., Fl¨uckiger, C., Minami, T., Kivlighan, M. & Gunn, W.
(2011). Evidence-based treatments for depression and anxiety versus
’treatment as usual.’ Psychotherapy in Australia, 18(4), 68-77.
7. Thames, A. D., Panos, S., Del Re, A. C., Schembari, B., Levine, A.
J., Castellon, S. A., & Hinkin, C. H. (2011). Longitudinal Changes in
Medication Adherence Among HIV-Infected Adults: A Hierarchical
Linear Modeling Approach. Clinical Neuropsychologist, 25(4), 570-
570.
6. Horvath, A.O., Del Re, A. C., Fl¨uckiger, C. & Symonds, D. (2011).
The alliance.
In J. C. Norcross’ Psychotherapy relationships that
work. New York, NY: Oxford University Press.
5. Horvath, A.O., Del Re, A. C., Fl¨uckiger, C. & Symonds, D. (2011).
The working alliance in psychotherapy Psychotherapy Research.
4. Fl¨uckiger, C., Del Re, A. C., Znoj, H., Wampold, B. E, J¨arg, U.
& Caspar, F. (2011). Valuing patient’s perspective and the effects
on the therapeutic alliance: A randomized controlled adjunctive in-
struction. Journal of Counseling Psychology.
3. Wampold, B. E., Budge, S. L., Laska, K. M., Del Re, A. C.,
Baardseth, T. P., Fl¨uckiger, C., Kivlighan, M. & Gunn, W. (2011).
Evidence-based treatments for depression and anxiety:
Are they
more effective than treatment-as-usual? Clinical Psychology Review.
2. Horvath, A.O., Del Re, A. C., Fl¨uckiger, C. & Symonds, D. (2010).
Alliance in individual psychotherapy. In J. C. Norcross (Eds.), Evidence-
Based Therapy Relationships (Vol. National Registry of Evidence-
based Programs and Practices pp. 3).
Available from http://www.nrepp.samhsa.gov/Norcross.aspx
1. Wampold, B. E., Imel, Z. E., Laska, K. M., Benish, S. G., Miller,
S. D., Fl¨uckiger, C., Del Re, A. C., Baardseth, T. P. & Budge, S.
(2010). Determining what works in the treatment of PTSD. Clinical
10 of 20

Psychology Review, 30(8), 923-933.
doi:10.1016/j.cpr.2010.06.005
Statistical
Software
6. Del Re, A. C. (2013). RcmdrPlugin.MA: Graphical User Interface
for Meta-Analysis in R.
http://cran.r-project.org/web/packages/RcmdrPlugin.MA
5. Del Re, A. C. (2011). compute.es: Compute Effect Sizes.
http://CRAN.R-project.org/package=compute.es
4. Del Re, A. C. & Hoyt, W. T. (2010). MAc: Meta-Analysis with
Correlations.
http://CRAN.R-project.org/package=MAc
3. Del Re, A. C. & Hoyt, W. T. (2011). MAd: Meta-Analysis with
Mean Differences.
http://CRAN.R-project.org/package=MAd
2. Del Re, A. C. (2010).
RcmdrPlugin.MAc: Meta-Analysis with
Correlations (MAc) Rcmdr Plug-in. [Depricated]
1. Del Re, A. C. (2011).
RcmdrPlugin.MAd: Meta-Analysis with
Mean Differences (MAd) Rcmdr Plug-in. [Depricated]
Submitted
Manuscripts
4. Hoyt, W. T. & Del Re, A. C. (revise). Comparison of methods
for aggregating dependent effect sizes in meta-analysis.
Research
Synthesis Methods.
3. Boden, M., Babson, K. A., Del Re, A. C. & Bonn-Miller (in sub-
mission). The Comorbidity of Sleep Apnea and Medical Disorders
among Obese Military Veterans.
2. Hoyt, W. T., Del Re, A. C. & Larson, D. O. (revise). The efficacy
of grief treatment revisited: A meta-analysis. Psychology Bulletin
1. Panos, S.E., Hinkin, C.H., Singer, E.J., Thames, A.D., Patel, S.,
Sinsheimer, J.S., Del Re, A. C., Valdes-Sueiras, M., Levine, A.J.
(submitted). Older age and disease severity augment the effect of
APOE on cognitive functioning in HIV+ adults. Journal of Neurovi-
rology.
11 of 20

Workshops
7. Del Re, A. C. (2017, May).
Meta-analysis for psychology re-
searchers: A practical application of meta-analytic procedures in R.
Invited 2-day workshop at the University of Zurich, Zurich, Switzer-
land.
6. Del Re, A. C. (2013, May). Meta-analysis for psychotherapy re-
searchers: A practical application of basic and advanced meta-analytic
procedures using the graphical user interface for meta-analysis in R.
Invited 2-day workshop at the University of Zurich, Zurich, Switzer-
land.
5. Del Re, A. C. (2012, June).
Meta-analysis for psychotherapy
researchers: A practical application of basic and advanced meta-
analytic methods. Pre-conference workshop at the Society for Psy-
chotherapy Research, Virginia, USA.
4. Del Re, A. C. (2011, September). Introduction to meta-analysis
for psychology researchers: A practical application of basic and ad-
vanced meta-analytic procedures. Invited half-day workshop at the
University of Louisville, Louisville, KY.
3. Del Re, A. C. (2011, September). Meta-analysis for psychology re-
searchers: A practical application using the MAd meta-analysis pack-
age in R. Invited half-day workshop at the University of Wisconsin-
Madison, Madison, WI.
2. Del Re, A. C. (2011, September). Introduction to meta-analysis for
psychology researchers: A practical application using the MAd meta-
analysis package in R. Invited half-day workshop at the University of
Wisconsin-Madison, Madison, WI.
1. Del Re, A. C. & Fl¨uckiger, C. (2011, June).
Meta-analysis for
psychotherapy researchers: A practical application of basic and ad-
vanced meta-analytic procedures.
Pre-conference workshop at the
Society for Psychotherapy Research, Bern, Switzerland.
Presentations
34. Del Re, A. C.. (2018, May). Meta-analysis: What, when, why.
Presentation at NHRC, San Diego, CA
33. Del Re, A. C..
(2017, May).
Experience working as a health
services researcher in VHA. Guest colloquium at University of Zurich,
Zurich, Switzerland.
12 of 20

32. Horvath, A. O., Del Re, A. C., Fl¨uckiger, C. (2016, June). Integra-
tion across professional domains: The helping relationship. Presented
at the Society for the Exploration of Psychotherapy Integration. Trin-
ity College, Dublin, Ireland.
31. Harris, A.H.S., Bowe, T., Del Re, A. C., Finlay, A.K., Oliva, E.,
Rubinsky, A. (2014, October). Quasi-Experimental Effects of Ex-
tended Release Naltrexone in US Veteran Health Administration Pa-
tients with Alcohol Use Disorders. Paper presented at the Addiction
Health Services Research Conference, Boston, MA, USA.
30. Hoyt, W. T., Del Re, A. C., Larson, D. (2013, July). Effects of
analytic choices on conclusions in meta-analysis. Poster at American
Psychological Association annual conference, Honolulu, HI, USA.
29. Del Re, A. C..
(2013, May).
Enhancing positive outcomes in
psychotherapy: The patient and provider contribution. Guest collo-
quium at University of Zurich, Zurich, Switzerland.
28. Del Re, A. C., & Finney, J. (2012, June).
Placebo group im-
provement in trials of pharmacotherapies for alcohol use disorders:
A multivariate meta-analysis examining change over time. Sympo-
sium at the Society for Psychotherapy Research, Virginia Beach, VA,
USA.
27. Del Re, A. C., & Finney, J. (2012, June).
Placebo group im-
provement in trials of pharmacotherapies for alcohol use disorders:
A multivariate meta-analysis examining change over time. Poster at
Research Society on Alcoholism, San Francisco, CA, USA.
26. Panos, S. E., Hinkin, C.H., Thames, A.D., Del Re, A. C., Patel,
S.M., Valdes-Sueiras, M., Mathisen, G., Donovan, S., Singer, E.J.,
& Levine, A. J. (2012). Longitudinal effects of APOE e4 on HIV-
Associated Neurocognitive Dysfunction. Poster presented at the An-
nual Meeting of the International Neuropsychological Society.
25. Panos, S. E., Hinkin, C.H., Thames, A.D., Del Re, A. C., Patel,
S.M., Valdes-Sueiras, M., Mathisen, G., Donovan, S., Singer, E.J.,
& Levine, A. J. (2012). Cross-section effects of APOE E4 by Dis-
crete Neurocognitive Domain. Poster presented at the 40th Annual
Conference of the International Neuropsychological Society.
24. Del Re, A. C., Fl¨uckiger, C., Horvath, A. O., Hoyt, W. T., &
Symonds, D. (2011, June).
Methods in meta-analyses: Handling
within-study dependencies at the moderator level. Symposium at the
Society for Psychotherapy Research, Bern, Switzerland.
13 of 20

23. Fl¨uckiger, C., Del Re, A. C., Horvath, A. O., & Symonds, D. (2011,
June). Treatment and Design as Moderators of the Relationship of
the Alliance and Outcome: A Multilevel Longitudinal Meta Analy-
sis. Symposium at the Society for Psychotherapy Research, Berne,
Switzerland.
22. Horvath, A. O., Del Re, A. C., Fl¨uckiger, C., & Symonds, D. (2011,
June). The complex world of alliance assessments: Will the real al-
liance please stand up? Symposium at the Society for Psychotherapy
Research, Berne, Switzerland.
21. Del Re, A. C., Hoyt, W. T. (2011, June). A New Look at the Evi-
dence: Grief Counseling is Effective. 9th International Conference on
Grief and Bereavement in Contemporary Society, Miami, FL, USA.
20. Thames, A.D., Panos, S., Del Re, A. C., Schembari, B., Levine,
A.J., Castellon, S.A. & Hinkin, C.H. (2011, June).
Longitudinal
Changes in Medication Adherence among HIV-Infected Adults: A
Hierarchical Linear Modeling Approach. Presented at the 2011 an-
nual meeting of the American Academy of Clinical Neuropsychology,
Washington DC, USA.
19. Panos, S. E., Del Re, A. C., Thames A. D., Levine, A. J., Streiff,
V., Castellon, S., & Hinkin, C. H. (2011, June). Predictors of longi-
tudinal medication adherence: Evidence from an integrative model.
Poster presented at the 9th annual meeting of the American Academy
of Clinical Neuropsychology, Washington, DC. Abstract in The Clin-
ical Neuropsychologist, 25(4), p. 556.
18. Del Re, A. C. (2011, March). Therapist effects in the alliance/outcome
relationship: A restricted maximum likelihood meta-analysis. Grand
Rounds presentation at Long Beach VAHCS, Long Beach, CA, USA.
17. Del Re, A. C. (2011, March). Mindfulness practices for VA patients
and treatment providers.
In-service presentation for Long Beach
VAHCS medical and clinical staff, Long Beach, CA, USA.
16. Del Re, A. C., Fl¨uckiger, C., Horvath, A. O.,& Symonds, D.
(2010, June). Therapist effects in the alliance/outcome relationship:
A meta-analysis. Symposium at the Society for Psychotherapy Re-
search, Asilomar, CA, USA.
15. Fl¨uckiger, C., Del Re, A. C., Horvath, A. O.,& Symonds, D. (2010,
June). The Relation of the alliance and outcome: An international
perspective. Symposium at the Society for Psychotherapy Research,
Asilomar, CA, USA.
14 of 20

14. Horvath, A. O., Del Re, A. C., Fl¨uckiger, C., & Symonds, D.
(2010, June). The Therapeutic Alliance. Symposium at the Society
for Psychotherapy Research, Asilomar, CA, USA.
13. Baardseth, T., & Del Re, A. C. (2010, June). The relative efficacy
of psychotherapeutic treatments for anxiety disorders. Symposium
at the Society for Psychotherapy Research, Asilomar, CA, USA.
12. Horvath, A. O., Del Re, A. C., Fl¨uckiger, C., & Symonds, D.
(2010, June). Alliance Research 1976-2010. Invited Address at the
University do Minho (Braga, Portugal).
11. Fl¨uckiger, C., Znoj, H., Caspar, H, Del Re, A. C. (2010, June).
Developing the therapeutic relationship - A randomized controlled
process-study.
Paper presentation at the International Federation
for Psychotherapy World Congress, KKL Lucerne, Switzerland.
10. Del Re, A. C. (2010, March). Self-awareness and ethical decision-
making. Invited lecture to ethics course for doctoral counseling psy-
chology program at University of Wisconsin-Madison, Madison, WI,
USA.
9. Del Re, A. C. (2010, February). Self-awareness and ethical decision-
making. Invited lecture to ethics course for doctoral counseling psy-
chology program at University of Wisconsin-Madison, Madison, WI,
USA.
8. Del Re, A. C. (2010, January). Self-awareness and ethical decision-
making. Invited lecture to ethics course for doctoral counseling psy-
chology program at University of Wisconsin-Madison, Madison, WI,
USA.
7. Del Re, A. C. (2008, November). Mindfulness techniques for stress
reduction. Invited presentation for School of Veterinary Medicine at
University of Wisconsin-Madison, Madison, WI, USA.
6. Del Re, A. C. (2008, October).
The art of forgiveness through
mindfulness meditation. Presentation at the University of Wisconsin
Paul P. Carbone Comprehensive Cancer Center’s Seventh Annual
Symposium, Advances in Multidisciplinary Cancer Care, Madison,
WI, USA.
5. Del Re, A. C. (2008, April). Coping with stress as a student of
veterinary medicine. Invited presentation for School of Veterinary
Medicine at University of Wisconsin-Madison, Madison, Wisconsin.
4. Del Re, A. C. (2008, March). Integrating awareness and ethics in
psychology. Invited lecture to ethics course for doctoral counseling
15 of 20

psychology program at University of Wisconsin-Madison, Madison,
WI, USA.
3. Del Re, A. C. (2008, February). Empirical support for Mindfulness-
Based Stress Reduction. Invited lecture to ethics course for doctoral
counseling psychology program at University of Wisconsin-Madison,
Madison, Wisconsin.
2. Del Re, A. C. (2008, January). Why self-awareness is important for
ethical decision-making. Invited lecture to ethics course for doctoral
counseling psychology program at University of Wisconsin-Madison,
Madison, WI, USA.
1. Del Re, A. C., Clark, L. L., Dvorscek, M. J., Estrada, Y., Gloria,
A. M., Howard, C. M., Killinger, S. L., Lin, M. M., & Siewert, J.
J. (2006, April). Doctoral students’ perceptions of support in APA-
accredited counseling psychology programs: An exploratory study.
Paper presented at the meeting of the Great Lakes Regional Counsel-
ing Psychology Conference, West Lafayette, IN, USA.
Media
Interviews
1. Del Re, A. C. (2013, June). Win the War in Your Belly. Fight
back against microscopic marauders sabotaging your weight-loss ef-
forts. Mens Health Magazine. http://www.menshealth.com/weight-
loss/win-war-gut.
Professional
Activities
Peer Review (publons)
 Co-editor for Special Section on Meta-Analysis in Psy-
chotherapy Research (2017-2018)
 Advisory Editorial Board of Psychotherapy Research (2016
- 2019)
 Ad Hoc Reviewer (2010 - )
- Clinical Psychology Review
- Preventive Medicine
- BMC Psychiatry
- Journal of Counseling Psychology
- Addiction
- Behavior Research Methods
- Psychotherapy Research
- Group Dynamics
- Methods in Ecology and Evolution
- The Quantitative Methods for Psychology
16 of 20

University of Wisconsin-Madison, Counseling Psychology Depart-
Teaching
Experience
ment
Teaching Assistant/Lab Instructor
 Meta-Analysis in the Social Sciences
Summer 2009 & 2010
 Weekly guest lecturer and lab instructor for doctoral students
learning fundamental procedures for conducting fixed- and random-
effects meta-analyses.
 Co-created statistical programs, files, scripts, and syntax in R,
excel, and SPSS to assist students in conducting their meta-
analysis projects.
 Supervisor: William T. Hoyt, PhD
 Multiple Regression and Correlation
Fall 2009
 Weekly guest lecturer and lab instructor for regression methods
to doctoral students in the social sciences.
 Co-created statistical programs, files, scripts, and syntax in R,
excel, and SPSS used for learning MR/C formulas and analyzing
data sets .
 Supervisor: William T. Hoyt, PhD
 Theory and Practice of Interviewing
2006 & 2007
 Created syllabus and provided instruction to advanced under-
graduates in interviewing, communication, and counseling the-
ory and skills.
 Supervisors: Alberta Gloria, PhD; Teresa Bear, PhD
University of Wisconsin-Madison, School of Veterinary Medicine
Teaching Assistant/Lab Instructor
 Art of Clinical Communication
Fall 2008
 Co-taught, with a psychologist and veterinarian, a communica-
tion skills course designed to assist advanced veterinary students’
effectively manage interpersonal communication with clients.
 Supervisor: Corissa Lotta, PhD; Ruthanne Chun, DVM
VA Long Beach Healthcare System, Long Beach, California (40 hours/wk)
Clinical
Experience
Pre-doctoral internship
August 2010 to August 2011
 Conducted psychological assessments and individual and group psy-
chotherapy with military veterans in five specialized rotations: PTSD
& Mindfulness, Home-Based Primary Care, Spinal Cord Injury,
Smoking Cessation & Weight Loss, and Neuropsychology.
 Supervisors: John Huang, PhD; Angela Lau, PhD; Barry Rabin,
PhD; Linda Mona, PhD; Jeffery Webster, PhD
17 of 20

Waisman Laboratory for Brain Imaging and Behavior
William S. Middleton Memorial VA Medical Center
UW Health, Integrative Medicine, Madison, Wisconsin (5 hours/wk)
Mindfulness in Psychotherapy and Research Practicum
September
2007 to May 2010
 Co-facilitated Mindfulness-Based Cognitive Therapy and Mindfulness-
Based Stress Reduction groups with a medically-based outpatient
population.
 Created a practice quality measure, now added as a standard clinical
measure at UW Integrative Medicine.
 Supervisors: Donal MacCoon, PhD; Carmen Alonso, PhD; Kather-
ine Bonus, MA
UW Madison School of Veterinary Medicine, Madison, Wisconsin
(8 hours/wk)
Advanced Practicum
September 2008 to May 2009
 Provided individual therapy to veterinary students and support for
veterinary hospital clients. Clinical work involved short-term grief
counseling, decision-making assistance around euthanasia, and anx-
iety management.
 Co-led weekly clinical communication rounds for oncology staff in
the veterinary hospital and acted as a consultant for interpersonal
concerns that emerged with clients in day-to-day clinical work.
 Supervisor: Corissa Lotta, PhD
UW Madison Counseling Psychological Training Clinic, Madison,
Wisconsin (8 hours/wk)
Assistant Director
September 2008 to May 2009
 Assisted in the organizing and promoting of a new psychotherapy
training clinic for the department of counseling psychology.
 Created clinic brochure, intake forms, and assisted in selection of
outcome systems for tracking client progress in therapy.
 Supervisor: Teresa Bear, PhD
Supervisor-in-Training
Spring 2008
 Provided weekly individual and group supervision focused on the
clinical and professional development of first year Master’s students
in Counseling Psychology.
 Supervisor: Mary Lee Nelson, PhD
Group Practicum Counselor
Spring 2007
18 of 20

 Led a weekly interpersonal process group composed of undergradu-
ate students.
 Supervisor: William Hoyt, PhD
Wisconsin Psychiatric Institute and Clinics, Madison, Wisconsin (20
hours/wk)
Foundational Practicum
September 2006 to August 2007
 Conducted weekly intakes, individual therapy, and facilitated a Di-
alectical Behavioral Therapy Group with an outpatient psychiatric
population.
 Supervisor: Alan Gurman, PhD; Pauline Thome, PhD
Turquoise Lodge Alcohol and Drug Treatment Hospital, Madison,
Wisconsin (20 hours/wk)
Masters-level Internship
2004
 Administered ASI assessments and conducted weekly individual and
group therapy in an inpatient drug and alcohol treatment hospital
with a diverse adult population.
 Supervisor: Gene Coffield, PhD
Body Evolution, Santa Fe, New Mexico (30 hours/wk)
Related
Professional
Experience
Personal Fitness Trainer (Self-Employed)
May 1998 to May 2002
 Created and ran a personal fitness training and nutrition consulta-
tion business.
 Conducted individual and group health training sessions with an
integrated mind and body focus, tailored to clients’ personal goals.
 Competed in and won several bodybuilding competitions, including
Mid-USA & New Mexico titles.
Basic Rights New Mexico, Santa Fe, New Mexico (5 hours/wk)
Human Rights Advocate
May 2003 to May 2004
 Involved in political campaign planning and advocacy work to up-
hold the New Mexico anti-discrimination laws for the lesbian, gay,
bisexual, and transgendered community.
Additional
Education
 Make Money with Machine Learning, machinelearning.io, 2020, 10-
Week Web Training.
 Introduction to Machine Learning, Stats Camp, 2019, 5-Day Intensive
Training, Abq, NM.
 Python: Intermediate Training, pluralsight.com, 2017, 12 hour Web
Training.
19 of 20

 Python: Beginner Training, pluralsight.com, 2017, 8 hour Web Train-
ing.
 Querying Microsoft SQL Server 2014, 360Training.com, 2016, 40 hour
Live Web Training.
 SAS SQL 1: Essentials, SAS Institute, 2013, Live Web Training.
 SAS Statistical Programming 1: Essentials, SAS Institute, 2012, San
Francisco, CA.
 SAS Statistical Programming 2, SAS Institute, 2012, San Francisco,
CA.
 Workshop: Multi-level modeling using the ‘lme4’ package for the R sta-
tistical software program, 2010 UserR International Conference, Gaithers-
burg, Maryland.
 Workshop: A crash course in efficient R programming, 2010 UserR
International Conference, Gaithersburg, Maryland.
 Workshop: Introduction to the R Statistical Computing Environment
(2-day intensive), Madison, Wisconsin.
 Professional training in Acceptance and Commitment Therapy (2-day
intensive), Nashville, Tennessee.
 Professional training in Mindfulness-Based Cognitive Therapy (5-day
intensive), Rhinebeck, New York.
 Professional training in Mindfulness-Based Stress Reduction (7-day in-
tensive), Watsonville, California.
 Professional training in Dialectical Behavioral Therapy, Madison, Wis-
consin.
Awards
 APA Division 17 Outstanding Graduate Student Award nom-
inee
 Rothney Award for doctoral research
 Advanced Opportunity Fellowship for doctoral studies
Professional
Affilliations
 American Psychological Association - Division 5 (Evaluation, Measure-
ment, & Statistics)
 Society for Psychotherapy Research
20 of 20

1. Predictive Suite: Model Builder (Phase 1)

The Methodology & System Design

RWE Analytics Portal (Logistic Regression) - Phase 1.

Ingests historical clinic data to train localized logistic regression models. Crucially, it allows models to have different, independent predictors at each individual session, extracting the unique coefficients that define success for a specific clinic’s population. It provides empirical evidence that baseline predictions fail by session 4.

Strategic Dissemination Pathway: The Algorithm Teardown

Implementation Strategy: Record a detailed methodological breakdown demonstrating the statistical decay of static baseline predictors by Session 5. Overlay the Phase 1 dynamic predictor model to illustrate the recovery in variance explained ($R^2$). Disseminate this analysis directly to Medical Directors with an offer to run a localized “Predictor Leakage” analysis on their historical data.

Target Organizations & Target Leadership

Targeting platforms that collect massive amounts of outcome data but lack dynamic, session-by-session intelligence.

Tier 3: Niche Therapy & Data Innovators

Octave: Sarah Adler, PsyD (Chief Medical Officer)
Equip Health: Erin Parks, PhD (Chief Medical Officer)
“As high-acuity digital clinics utilizing proprietary companion apps for daily behavioral and symptom tracking, you have the exact continuous data required to power our localized predictive suite.”
NOCD: Patrick McGrath, PhD (Chief Medical Officer)
“As high-acuity digital clinics utilizing proprietary companion apps for daily behavioral and symptom tracking, you have the exact continuous data required to power our localized predictive suite.”
Woebot Health: Athena Robinson, PhD (Chief Medical Officer)
Valera Health: Thomas Tsang, MD (CEO)
Brightside Health: Brad Kittredge (CEO), Mimi Winsberg, MD (CMO)
K Health: Edo Paz, MD (SVP Medical Affairs)
Kintsugi: Grace Chang (CEO)
Mirah: Mark Potter (CEO), Chief Medical Officer
Mentra: Head of Medical Outcomes

#Tier 2: Measurement-Based Care & Specialized Platforms

Greenspace Health: Simon Weisz (President), Jesse Hayko (VP Product)
“Because your entire platform architecture is built around continuous Measurement-Based Care (MBC) tracking, you already have the pristine data infrastructure required to run our session-by-session predictive coefficients.”
Owl (formerly Owl Insights): Eric Meier (CEO), Jason Williams (CTO)
“Because your entire platform architecture is built around continuous Measurement-Based Care (MBC) tracking, you already have the pristine data infrastructure required to run our session-by-session predictive coefficients.”
Two Chairs: Colleen Marshall (VP Medical Affairs), Edward Jones (VP Data Science)
“Because your care model requires mandatory digital pre-session check-ins tracking both symptom severity and the therapeutic alliance, you provide the exact time-series data our predictive engine models.”
Blueprint Health: Kevin Dedner (CEO)
“Because your entire platform architecture is built around continuous Measurement-Based Care (MBC) tracking, you already have the pristine data infrastructure required to run our session-by-session predictive coefficients.”
Tridiuum (New Directions): Ed Jones (Chief Medical Officer)
Meru Health: Albert Carlsson (Co-Founder/Chief Science Officer)
“Your 12-week program combining a digital therapeutic app with a wearable HRV biofeedback device provides the exact biometric and psychological feedback loop needed to train our localized models.”
SilverCloud (Amwell): Derek Richards, PhD (Chief Science Officer)
Big Health: Colin Espie, PhD (Chief Scientist)
“Because your users actively log sleep diaries, module completion data, and anxiety scores longitudinally in your app, our engine can calculate individualized success probabilities per treatment milestone.”
Eleos Health: Alon Joffe (CEO/Co-Founder)
“By utilizing ambient Voice AI to passively extract and track clinical themes across consecutive therapy sessions, you generate the rich longitudinal data necessary to train our session-by-session dropout predictors.”
NeuroFlow: Chris Molaro (CEO), Thomas Zaubler (CMO)
“Since your app effectively gamifies continuous behavioral health tracking between appointments, our engine can ingest that telemetry to predict exact clinical ROI for your enterprise buyers.”

Tier 1: Mega-Platforms & Unicorns

Spring Health: Adam Chekroud, PhD (Co-Founder & President), Millard Brown (SVP Medical Affairs)
“Because your platform mandates recurring digital clinical assessments (PHQ-9, GAD-7) before therapy sessions to drive your Precision Mental Healthcare routing, you possess the exact continuous data required to train our dynamic models.”
Lyra Health: Smita Das, MD, PhD (VP Medical Affairs), Joe Grasso, PhD (Sr. Director)
“Since you utilize a continuous clinical tracking system via your patient app to measure mental health trajectories and report ROI, our localized predictor can mathematically isolate dropout risks session-by-session.”
Modern Health: Myra Altman, PhD (VP Medical Strategy), Neha Chaudhary, MD (CMO)
“With your users continuously logging WHO-5 surveys and mood check-ins within your companion app, our live predictor can visualize their exact trajectory of clinical improvement.”
Talkspace: Nikole Benders-Hadi, MD (CMO)
“Since you automatically trigger in-app clinical progress assessments every 3 weeks for active therapy users, we can calculate individualized session-by-session success probabilities using your historical data.”
BetterHelp: Nicole Amesbury (Head of Clinical Development)
Alma: Nina Vasan, MD (CMO)
“Because your networks require therapists to input session-by-session MBC data to maintain standing and track outcomes, our engine can run locally to map exact patient deterioration risk.”
Headspace Health: Dana Udall, PhD (Chief Medical Officer)
“Since your B2B platforms already track daily streaks, login duration, and routine mood check-ins, our PQM API can upgrade your metrics from ‘minutes meditated’ to clinical-grade longitudinal outcome prediction.”
Sondermind: Douglas Newton, MD (CMO), VP of Data Analytics
“Because your networks require therapists to input session-by-session MBC data to maintain standing and track outcomes, our engine can run locally to map exact patient deterioration risk.”
Cerebral: David Mou, MD (CMO)
Path Mental Health (Rula): Josh Bruno (CEO), Chief Medical Officer

Pricing Strategy

Phase 1 Implementation (Model Training): $15,000 - $25,000 flat consulting fee to sanitize historical data and train/extract localized coefficients.

The Outreach Script

Subject: Methodological limitations of baseline predictors in [Company Name]’s clinical data

Dear Dr. [Last Name],

In reviewing the standard approaches to outcome prediction in behavioral health, it is clear that many organizations rely heavily on static, baseline variables. As you know from your own clinical work, the variables that predict treatment response at Session 2 differ significantly from those at Session 8.

I am a Clinical Statistician (PhD, Counseling Psychology) specializing in psychotherapy outcomes. I have developed a localized methodology that ingests historical clinical data to extract dynamic, session-by-session regression coefficients. This isolates the exact shifting variables that predict patient success within your specific population.

Because this pipeline trains locally, it requires zero cloud PHI transmission. I am looking to partner with a select group of measurement-based care platforms to implement this pipeline. I would welcome the opportunity to review this methodology with you or your data science team.

Sincerely,
Dr. Aaron Del Re

2. Predictive Suite: Live Predictor (Phase 2 & 3)

The Methodology & System Design

RWE Analytics Portal (Logistic Regression) - Phase 2 & 3.

The live clinical execution layer. Phase 2 ingests raw outcome data alongside the trained coefficients from Phase 1 to compute live success probabilities session-by-session. Phase 3 visualizes this trajectory. Operates strictly within local firewalls (Zero PHI cloud transmission).

Strategic Dissemination Pathway: Operational Cost Analysis

Implementation Strategy: Develop a localized web-calculator for Intensive Outpatient Programs (IOPs). The tool allows Medical Directors to input their monthly active census and session 4 dropout rates to quantify operational losses. The tool demonstrates how the Phase 2 live predictor flags 68% of patient deterioration earlier in the treatment cycle, directly reducing attrition.

Target Organizations & Target Leadership

Targeting programs where early dropout prediction significantly impacts patient outcomes.

Tier 3: Brick-and-Mortar Groups & CRO Retention Teams

Mindpath Health: Zishan Samiuddin, MD (CMO)
Thriveworks: VP Medical Operations
Ellie Mental Health: Erin Pash (Founder/Chief Medical Officer)
Array Behavioral Care: James R. Varrell, MD (CMO)
Transformations Care Network: Chief Medical Officer
Geode Health: Chief Medical Officer
Centria Healthcare: VP of Medical Outcomes
Medpace (CRO): Director of Patient Recruitment/Retention
“Since your clinical trial retention teams deploy ePRO (electronic Patient-Reported Outcomes) software to strictly monitor longitudinal trial endpoints over years, our engine can dynamically predict and prevent patient attrition.”
IQVIA (Patient Concierge Div): VP of Medical Operations
“Since your clinical trial retention teams deploy ePRO (electronic Patient-Reported Outcomes) software to strictly monitor longitudinal trial endpoints over years, our engine can dynamically predict and prevent patient attrition.”
Syneos Health: VP of Real World Evidence
“Since your clinical trial retention teams deploy ePRO (electronic Patient-Reported Outcomes) software to strictly monitor longitudinal trial endpoints over years, our engine can dynamically predict and prevent patient attrition.”

#Tier 2: Addiction & SUD Treatment

Hazelden Betty Ford: Alta DeRoo, MD (CMO)
Caron Treatment Centers: Joseph Garbely, DO (CMO)
Workit Health: Justin Alves, RN (CMO)
“Because your digital SUD platforms continuously track recurring at-home drug screens, buprenorphine adherence, and recovery milestones over months, you feed a perfect longitudinal dataset into our algorithms.”
Bicycle Health: Brian Clear, MD (CMO)
“Because your digital SUD platforms continuously track recurring at-home drug screens, buprenorphine adherence, and recovery milestones over months, you feed a perfect longitudinal dataset into our algorithms.”
Boulder Care: Stephen Martin, MD (CMO)
Groups Recover Together: Jacob L. Freedman, MD (CMO)
Eleanor Health: Nzinga Harrison, MD (CMO)
Pelago (formerly Quit Genius): Yusuf Sherwani, MD (CEO/CMO)
“By coupling a mobile app with a connected Bluetooth breathalyzer to longitudinally track substance use and CBT module completion, your platform is mathematically primed for our predictive models.”
Monument: Mike Russell (CEO)
CleanSlate Centers: Chief Medical Officer

Tier 1: High-Acuity IOP/PHP & Eating Disorders

Compass Health Center: Claudia Finkelstein, MD (Chief Medical Officer), VP of Ops
“Because your intensive outpatient programs (IOPs) mandate continuous EHR-based clinical tracking to manage step-down trajectories, our engine can run locally to flag patient deterioration in real time.”
LifeStance Health: Ujjwal Ramtekkar, MD (CMO), VP of Quality
“Because your intensive outpatient programs (IOPs) mandate continuous EHR-based clinical tracking to manage step-down trajectories, our engine can run locally to flag patient deterioration in real time.”
Eating Recovery Center (Pathlight): Anne Marie O’Melia, MD (CMO)
“Since you continuously track highly sensitive biometric (weight restoration) and psychological recovery markers across long-term residential stays, our dashboard can plot those exact outcomes dynamically to predict relapse.”
Rogers Behavioral Health: Jerry Halverson, MD (CMO)
“Because your intensive outpatient programs (IOPs) mandate continuous EHR-based clinical tracking to manage step-down trajectories, our engine can run locally to flag patient deterioration in real time.”
Newport Healthcare: Barbara Nosal, PhD (Chief Medical Officer)
Charlie Health: Caroline Fenkel, LCSW (Chief Medical Officer)
“As high-acuity digital clinics utilizing proprietary companion apps for daily behavioral and symptom tracking, you have the exact continuous data required to power our localized predictive suite.”
Discovery Behavioral Health: Matthew Polacheck, PsyD (Chief Medical Officer)
Acadia Healthcare: Michael Genovese, MD (CMO)
PrairieCare: Thomas Tarzian, MD (CMO)
Monte Nido: VP of Medical Operations

Pricing Strategy

Phase 2 & 3 Licensing: $2,500 - $5,000/month recurring enterprise fee to run the live predictor and visualization dashboards.

The Outreach Script

Subject: Session-by-session patient deterioration tracking without PHI transmission

Dear Dr. [Last Name],

Monitoring patient trajectories in high-acuity IOP settings is critical, but transmitting real-time session data to external cloud predictors introduces significant HIPAA compliance challenges.

I have engineered an independent clinical dashboard that utilizes deterministic logistic regression entirely within the local firewall. It ingests your raw outcome data alongside predefined statistical coefficients to generate real-time probabilistic risk scores for patient deterioration, calculated session-by-session.

I would be glad to arrange a brief call to demonstrate how this localized scoring integrates into existing clinical workflows to identify at-risk patients before dropout occurs.

Sincerely,
Dr. Aaron Del Re

3. Serverless Mixed-Effects Engine

The Methodology & System Design

Serverless Mixed-Effects RWE Engine (Predictive Outcomes Suite).

Distinct from logistic regression, this engine uses longitudinal linear mixed-effects modeling (lmer). Designed to model how a specific intervention (a wearable, a supplement, a digital therapeutic) changes continuous outcomes over time, isolating the treatment effect from random variance. Executes the matrix algebra entirely in the user’s browser for zero PHI risk.

Strategic Dissemination Pathway: Comparative Longitudinal Visualization

Implementation Strategy: Extract public data from a recently published, peer-reviewed trial evaluating a specific bio-tracking device or supplement. Re-analyze and visualize the data using the Serverless Mixed-Effects dashboard to demonstrate superior longitudinal modeling. Share this interactive visualization with the study’s Chief Science Officer to illustrate how their trial data could be presented dynamically to stakeholders.

Target Organizations & Target Leadership

Targeting Chief Science Officers who require robust longitudinal modeling for their interventions.

Tier 4: Apollo Extended Outbound Database

Halcyon Technology Holdings: Todd Griffin (Chief Science Officer)
Sensuscorp Incorporated: Francha K. (Chief Medical Officer)
International Institute of…: Garry Gordon (Medical Director)
The Longevity Suite: Ron Krotoshesky (Medical Director)
Human Longevity, Inc.: Koon Poon (Medical Director), Pamila B. (Medical Director)
“Because you aggregate whole-genome sequencing, MRIs, and continuous biomarker data into a deeply longitudinal patient health registry, our mixed-effects pipeline can visualize these predictive trajectories.”
Alexander Shulgin Research: Paul D. (Chief Science Officer)
ORCORA Inc.: Kevin Kaplan (Chief Medical Officer)
Venocare, Inc.: Cheryl C. (VP of Medical Affairs)
Regene Network: Michael Duncan (Chief Science Officer)
Ora Biomedical, Inc.: Yuri T. (Chief Science Officer)
Thorance: Angel Angelov (Chief Medical Officer)
L-Nutra, Inc: William Hsu (Chief Medical Officer)
Parcept Corporation: Genese M. (VP of Medical Affairs)
hubSpacely Medica: Velma C. (VP Medical Affairs)
Lujo Andol Biosciences: Will Ramsey (Chief Medical Officer)
ReAge Labs: Paul R. (Chief Medical Officer)
Fountain Life: Dawn Mussallem (Chief Medical Officer), Carmen Keith (Medical Director)
PA Options for Wellness: Tom Lee (VP of Research)
SHIFT (Life): Brian H. (Chief Medical Officer)
LIMINA Fitness: Sunshine H. (Chief Science Officer)
Vesta Nutra: Toy G. (Chief Science Officer)
Sequel Med Tech: Joanna Mitri (Chief Medical Officer)
Incarnus Healthcare Inc: Lou Barbato (Chief Medical Officer)
LifeQ, Inc.: Franco Prezzi (Chief Science Officer)
Macis Health: Rachna Sankari (Chief Medical Officer)
Caridial Therapeutics Inc: Andrew Harner (Chief Medical Officer)
Adventist HealthCare: Zachary Levine (Clinical Professor)
Epizaimity AI: William Rosenberg (Chief Medical Officer)
ReCareLink: Chelsea Litchman (Chief Medical Officer)
Amalgam Rx: Suzanne Clough (Chief Medical Officer)
“Since your digital therapeutics actively track continuous chronic disease markers (connected glucometers, BP cuffs) via mobile apps, our serverless engine can provide your sales reps with interactive clinical efficacy dashboards.”
Rejuvenate Bio: Deborah A. (Chief Medical Officer)
Reset Health: Ben T. (Chief Medical Officer)
Cellarate: Linda A. (Chief Medical Officer)
Dario: Omar Mangipudi (Chief Medical Officer)
“Since your digital therapeutics actively track continuous chronic disease markers (connected glucometers, BP cuffs) via mobile apps, our serverless engine can provide your sales reps with interactive clinical efficacy dashboards.”
American Herbal Products: Holly J. (Chief Science Officer)
Epiral: Mark F. (Chief Medical Officer)
Canary Health: Neal K. (Founder and Chief Medical Officer)
Element Science, Inc.: Zubin E. (Chief Medical Officer)
Level 42 AI: Ogan Gurel (Chief Medical Officer)
Well Founded: Jayo Noel (Chief Medical Officer)
Garmto: Gus Haddad (Chief Medical Officer)
TharaTec: Donahue (Chief Medical Officer)
Medpace: Loren P. (Medical Director), Jaclyn J. (Medical Director), Jennifer Lobert (Medical Director), Francis L. (Medical Director), Philip Roehrs (Medical Director), Ruchi B. (Medical Director), Adem L. (Medical Director), Maricor D. (Medical Director), Tiana Aurora (Strategic Partnerships), Jaleh Fallah (Medical Director), Mary Brune (Medical Director), Andrew H. (Medical Director)
“Since your clinical trial retention teams deploy ePRO (electronic Patient-Reported Outcomes) software to strictly monitor longitudinal trial endpoints over years, our engine can dynamically predict and prevent patient attrition.”
Biography Health: William S. (Co-Founder and Chief Medical Officer)
BioAge Labs: Rusty Montgomery (Senior Vice President Research)
Collabree Ltd: Anjali B. (Co-Founder)
Green Earth Medicinals: Christian Lo (Chief Executive Officer & CMO)
Noom: Jeffrey E. (Chief Medical Officer)
Curonix: Andrea Trescot (Chief Medical Officer)
Optispan: George H. (Medical Director/Co-founder)
Prima-Temp: Wade Webster (Founder & Chief Medical Officer)
Eledon Pharmaceuticals: Eliezer Katz (Chief Medical Officer)
Acie® Inc.: David Botequim (Chief Medical Officer)
SUPMOGO: Peter Michael (Chief Medical Officer/Co Founder)
Biopharma: Mina P. (Chief Medical Officer)
PureTech Health: Gregory Zugates (Vice President of Research)
JangoBio: Michael Foley (Chief Medical Officer)

#Tier 3: HRT, Longevity & Telehealth Interventions

Hone Health: Jack Jeng, MD (CMO)
“Since your telehealth protocols (HRT, menopause) require continuous longitudinal lab tracking and symptom monitoring for prescription management, our dashboard can plot those exact outcomes dynamically.”
Marek Health: Medical Director
Midi Health: Kathleen Jordan, MD (CMO)
“Since your telehealth protocols (HRT, menopause) require continuous longitudinal lab tracking and symptom monitoring for prescription management, our dashboard can plot those exact outcomes dynamically.”
Lifeforce: Vinita Tandon, MD (CMO)
“Since your telehealth protocols (HRT, menopause) require continuous longitudinal lab tracking and symptom monitoring for prescription management, our dashboard can plot those exact outcomes dynamically.”
Tally Health: Adiv Johnson, PhD (Head of Science)
“Because your epigenetic testing requires recurring cheek-swab testing over time, our mixed-effects architecture can perfectly model the rate of biological age reversal for your clients.”
Wild Health: Matt Dawson, MD (CEO)
Parsley Health: Robin Berzin, MD (CEO/Founder)
Viome: Momo Vuyisich, PhD (Chief Science Officer)
Calibrate: Kim Boyd, MD (CMO)
TruDiagnostic: Ryan Smith (VP Business Dev)
“Because your epigenetic testing requires recurring cheek-swab testing over time, our mixed-effects architecture can perfectly model the rate of biological age reversal for your clients.”

Tier 2: Premium Nootropics & Supplements

Thesis: Dan Freed (CEO/Founder)
Thorne HealthTech: Nathan Price, PhD (Chief Scientific Officer)
Seed Health: Raja Dhir (Co-CEO/R&D)
InsideTracker: Gil Blander, PhD (Chief Scientific Officer)
“Because your business model relies on users taking recurring 100+ biomarker blood panels, microbiome, or DNA tests over several months, our engine can seamlessly visualize their physiological improvement trajectories.”

Tier 1: Wearables & Continuous Bio-tracking

Oura Ring: Shyamal Patel (Head of Science), Marco Altini
“Because your hardware streams 24/7 continuous longitudinal biometric data (HRV, sleep architecture, physiological strain), our serverless mixed-effects engine can securely model and visualize longitudinal behavioral modifications for your B2B enterprise clients.”
Whoop: Kristen Holmes (VP of Performance Science)
“Because your hardware streams 24/7 continuous longitudinal biometric data (HRV, sleep architecture, physiological strain), our serverless mixed-effects engine can securely model and visualize longitudinal behavioral modifications for your B2B enterprise clients.”
Levels: Casey Means, MD (Co-Founder/CMO), Taylor Sittler (Head of Research)
“Since your users wear Continuous Glucose Monitors (CGMs) that generate thousands of longitudinal data points in your app, our engine can mathematically isolate your platform’s direct treatment effect on metabolic health over time.”
NutriSense: Kara Collier (VP of Health)
“Since your users wear Continuous Glucose Monitors (CGMs) that generate thousands of longitudinal data points in your app, our engine can mathematically isolate your platform’s direct treatment effect on metabolic health over time.”
Signos: William Dixon (Co-Founder), VP of Clinical Affairs
“Since your users wear Continuous Glucose Monitors (CGMs) that generate thousands of longitudinal data points in your app, our engine can mathematically isolate your platform’s direct treatment effect on metabolic health over time.”
Eight Sleep: Craig Heller, PhD (Medical Advisory), David Marrero (VP Research)
“Because your hardware streams 24/7 continuous longitudinal biometric data (HRV, sleep architecture, physiological strain), our serverless mixed-effects engine can securely model and visualize longitudinal behavioral modifications for your B2B enterprise clients.”
Function Health: Mark Hyman, MD (Co-Founder/CMO)
“Because your business model relies on users taking recurring 100+ biomarker blood panels, microbiome, or DNA tests over several months, our engine can seamlessly visualize their physiological improvement trajectories.”
ZOE (Nutrition): Tim Spector, MD (Scientific Founder)
“Because your business model relies on users taking recurring 100+ biomarker blood panels, microbiome, or DNA tests over several months, our engine can seamlessly visualize their physiological improvement trajectories.”
Garmin (Health B2B): Scott Burgett (Director of Health Engineering)
“Because your B2B API division aggregates continuous wearable telemetry data for corporate wellness research, our serverless architecture can visualize clinical efficacy with zero PHI liability.”
Supersapiens: Head of Sports Science

Pricing Strategy

Flat Build (Data Wrangle + Dashboard Build): $25,000 - $45,000 project fee per trial/intervention dataset.

The Outreach Script

Subject: Interactive longitudinal modeling of [Company Name]’s trial data

Dear Dr. [Last Name],

Demonstrating the longitudinal efficacy of health interventions to stakeholders often requires complex statistical modeling that is difficult to visualize interactively without risking PHI exposure via server backends.

My background involves 15 years of building mixed-effects models (lmer) for the VA and DoD. I have developed a serverless system that compiles complex longitudinal models into localized JSON objects. This allows the variance-covariance matrix algebra to execute entirely within the user’s browser.

The result is a localized “RWE Efficacy Dashboard” that allows your team to dynamically adjust baseline demographics and observe predicted longitudinal outcomes with zero HIPAA liability. I would welcome a brief discussion to share a live demonstration of this pipeline.

Sincerely,
Dr. Aaron Del Re

4. PQM Tracker

The Methodology & System Design

Practice Quality Mindfulness (PQM) Tracker.

Built directly on your published clinical scales (Del Re et al., 2013). Abandons “minutes meditated” to track Receptivity and Perseverance session-by-session, cross-referencing against daily mood to capture the actual active mechanisms of change.

Strategic Dissemination Pathway: Grant Methodology Integration

Implementation Strategy: Draft a rigorous, NIH-compliant methodology template detailing the integration of the Practice Quality-Mindfulness (PQM) scale and mixed-effects modeling. Provide this freely to academic PIs via ResearchGate, offering it as a plug-and-play measurement strategy for R01/R34 grant applications, with the stipulation of inclusion as a Co-Investigator for statistical analysis.

Target Organizations & Target Leadership

Targeting R01-funded researchers, MBSR clinics, and Psychedelic Integration clinics tracking state-changes.

Tier 3: Corporate MBSR & Digital Apps

Ten Percent Happier: Jay Michaelson, PhD (Chief Content Officer)
“Since your B2B platforms already track daily streaks, login duration, and routine mood check-ins, our PQM API can upgrade your metrics from ‘minutes meditated’ to clinical-grade longitudinal outcome prediction.”
Calm Health (Clinical Div): Chief Medical Officer
“Since your B2B platforms already track daily streaks, login duration, and routine mood check-ins, our PQM API can upgrade your metrics from ‘minutes meditated’ to clinical-grade longitudinal outcome prediction.”
Headspace (Science): VP of Medical Strategy
“Since your B2B platforms already track daily streaks, login duration, and routine mood check-ins, our PQM API can upgrade your metrics from ‘minutes meditated’ to clinical-grade longitudinal outcome prediction.”
Insight Timer: Christopher Plowman (CEO)
Healthy Minds Innovations: Cortland Dahl, PhD (Chief Science Officer)
“Since your B2B platforms already track daily streaks, login duration, and routine mood check-ins, our PQM API can upgrade your metrics from ‘minutes meditated’ to clinical-grade longitudinal outcome prediction.”
Mindful Schools: Director of Research
Waking Up (Sam Harris): Head of Content / Research
InnerTrek (Psilocybin Training): Director of Clinical Training
Ketamine Clinics Los Angeles: Steven Mandel, MD
Palouse Mindfulness: Dave Potter (Founder)

#Tier 2: Psychedelic Integration & Pharma

Compass Pathways: Guy Goodwin, MD, DPhil (CMO)
“Since your FDA clinical trials require highly structured longitudinal tracking of patient state-changes (CAPS-5, MADRS) between dosing sessions, our PQM API can precisely quantify integration adherence.”
MAPS (Lykos Therapeutics): Corine de Boer, MD (CMO), Berra Yazar-Klosinski, PhD (CSO)
“Since your FDA clinical trials require highly structured longitudinal tracking of patient state-changes (CAPS-5, MADRS) between dosing sessions, our PQM API can precisely quantify integration adherence.”
Awakn Life Sciences: Prof. David Nutt (Chief Research Officer)
“Since your FDA clinical trials require highly structured longitudinal tracking of patient state-changes (CAPS-5, MADRS) between dosing sessions, our PQM API can precisely quantify integration adherence.”
Cybin: Alex Belser, PhD (CMO)
“Since your FDA clinical trials require highly structured longitudinal tracking of patient state-changes (CAPS-5, MADRS) between dosing sessions, our PQM API can precisely quantify integration adherence.”
Atai Life Sciences: Srinivas Rao, MD, PhD (CSO)
Innerwell: Mike Eby, MD (CMO)
“Because your telehealth platforms utilize companion apps to track patient mood continuously between at-home ketamine treatments, our psychometric tracker can empirically prove the ROI of your integration protocols.”
Journey Clinical: Jonathan Sabbagh (CEO)
“Because your telehealth platforms utilize companion apps to track patient mood continuously between at-home ketamine treatments, our psychometric tracker can empirically prove the ROI of your integration protocols.”
Stella Center: Eugene Lipov, MD (CMO)
Mindbloom: Leonardo Vando, MD (Medical Director)
“Because your telehealth platforms utilize companion apps to track patient mood continuously between at-home ketamine treatments, our psychometric tracker can empirically prove the ROI of your integration protocols.”
Ketamine Wellness Centers: Kevin Nicholson (CMO)

Tier 1: Top-Tier Academic Mindfulness Labs

UW-Madison Center for Healthy Minds: Simon Goldberg, PhD / Richard Davidson, PhD
“Because your R01-funded RCTs utilize Ecological Momentary Assessment (EMA) to track patient psychological state-changes over time, our PQM engine can be seamlessly written into your protocols to capture the daily variance of practice quality.”
Brown University Mindfulness Center: Judson Brewer, MD, PhD (Director of Research)
“Because your R01-funded RCTs utilize Ecological Momentary Assessment (EMA) to track patient psychological state-changes over time, our PQM engine can be seamlessly written into your protocols to capture the daily variance of practice quality.”
Oxford Mindfulness Foundation (UK): Willem Kuyken, PhD
“Because your R01-funded RCTs utilize Ecological Momentary Assessment (EMA) to track patient psychological state-changes over time, our PQM engine can be seamlessly written into your protocols to capture the daily variance of practice quality.”
UCSD Center for Mindfulness: Fadel Zeidan, PhD / Zindel Segal, PhD
“Because your R01-funded RCTs utilize Ecological Momentary Assessment (EMA) to track patient psychological state-changes over time, our PQM engine can be seamlessly written into your protocols to capture the daily variance of practice quality.”
UMass Memorial Center for Mindfulness: Carl Fulwiler, MD
Harvard MAPS (Mindfulness Research): Benjamin Shapero, PhD
“Since your FDA clinical trials require highly structured longitudinal tracking of patient state-changes (CAPS-5, MADRS) between dosing sessions, our PQM API can precisely quantify integration adherence.”
MGH Center for Mindfulness: Sara Lazar, PhD
Stanford University (Mind & Body Lab): David Spiegel, MD
“Because your R01-funded RCTs utilize Ecological Momentary Assessment (EMA) to track patient psychological state-changes over time, our PQM engine can be seamlessly written into your protocols to capture the daily variance of practice quality.”
Johns Hopkins (Psychedelic Research): Matthew Johnson, PhD
UCLA Mindful Awareness Research Center: Diana Winston

Pricing Strategy

Academic Trials (NIH Grants): Write yourself into their R01/R34 grants as the Lead Statistical Consultant or Co-Investigator (10-20% FTE = $15,000 - $30,000/year per trial).
Commercial Clinic Licensing: $500 - $1,000/month for private MBSR/MBCT clinics.

The Outreach Script (To Researchers)

Subject: Integrating the PQM scale into upcoming MBSR/MBCT trials

Dear Dr. [Last Name],

I am a counseling psychologist and clinical statistician. I have been communicating with Simon Goldberg at UW-Madison regarding the limitations of relying solely on “minutes practiced” as a metric in mindfulness research.

To address this, I have digitized my published Practice Quality-Mindfulness (PQM) framework into an interactive tracking dashboard. It measures state-level Perseverance and Receptivity post-practice, cross-referencing these variables against daily mood impacts to better isolate the active mechanisms of change in mindfulness interventions.

Given your laboratory’s current focus on [Insert Topic], I wanted to contact you directly. I am providing this digital infrastructure to select research teams for upcoming trials to capture the variance that standard time-tracking instruments miss.

If this aligns with your current research goals, I would welcome a brief discussion regarding its potential inclusion in your upcoming grant protocols.

Sincerely,
Dr. Aaron Del Re

5. Veloz Suite

The Methodology & System Design

Veloz Language Learning Suite. A three-pillar cultural immersion ecosystem:

The Core App: Built on Language Islands and Active Recall. Features a 3-column UI: Regular Spanish $ ightarrow$ Alternate Spanish $ ightarrow$ Authentic Mexican Slang.
Slang Explorer Dashboard: A deeply interactive visual dictionary detailing the exact etymology, geographic context, and nuance of regional street language.
The Master Cookbook: A dynamic UX where a slider translates recipe cards from 0% Spanish to 100% Spanish (hybrid reading) for organic vocabulary acquisition through context.

Strategic Dissemination Pathway: Linguistic Etymology Case Studies

Implementation Strategy: Create highly academic yet accessible video breakdowns of authentic Mexican colloquialisms, detailing their etymological origins and geographic variations. Utilize these videos to demonstrate the limitations of gamified translation apps, directing viewers to the interactive Slang Explorer dashboard for deeper linguistic study.

Target Organizations & Target Leadership

Bypass traditional marketing and partner with massive YouTube polyglots who sell their own courses, or B2B immersion schools.

Tier 3: Expat & Corporate Relocation

Mexico Relocation Guide: Mariana Lange (Founder)
Cartus (Global Relocation): VP of Intercultural Solutions
SIRVA (Relocation): Director of Global Mobility Services
Crown World Mobility: Head of Intercultural Training
Babbel for Business: VP of Corporate Sales
“Because your enterprise platforms actively track employee longitudinal data—including login times, module completion rates, and proficiency assessment scores—our dynamic hybrid-reading algorithm can be integrated directly into your corporate reporting dashboards.”
Rosetta Stone (Enterprise): Director of Corporate Sales
“Because your enterprise platforms actively track employee longitudinal data—including login times, module completion rates, and proficiency assessment scores—our dynamic hybrid-reading algorithm can be integrated directly into your corporate reporting dashboards.”
Berlitz Corporation: VP of Digital Learning
“Because your enterprise platforms actively track employee longitudinal data—including login times, module completion rates, and proficiency assessment scores—our dynamic hybrid-reading algorithm can be integrated directly into your corporate reporting dashboards.”
Expat.com: Head of Partnerships

#Tier 2: High-End Immersion Schools & Digital Platforms

BaseLang: Connor Grooms (Founder)
“Because you capture continuous longitudinal telemetry on student-tutor booking frequency, hours logged, and recurring lesson retention, our interactive tools can integrate into your ecosystem to minimize platform churn.”
Fluenz Spanish Immersion: Sonia Gil (Founder)
“Since your software continuously monitors user learning progression, interactive video quizzes, and CEFR level progression, our interactive Slang Dashboard and ‘Shadow Loop’ methodology can seamlessly ingest this data to accelerate time-to-fluency.”
FluentU: Alan Park (Founder)
“Since your software continuously monitors user learning progression, interactive video quizzes, and CEFR level progression, our interactive Slang Dashboard and ‘Shadow Loop’ methodology can seamlessly ingest this data to accelerate time-to-fluency.”
Lingoda: Head of B2B Partnerships
Italki: VP of Product (B2B Integration)
“Because you capture continuous longitudinal telemetry on student-tutor booking frequency, hours logged, and recurring lesson retention, our interactive tools can integrate into your ecosystem to minimize platform churn.”
SpanishVIP: Founder / CEO
Preply: Head of B2B
“Because you capture continuous longitudinal telemetry on student-tutor booking frequency, hours logged, and recurring lesson retention, our interactive tools can integrate into your ecosystem to minimize platform churn.”
Ecela Spanish Schools: Director of Academics

Tier 1: Massive YouTube Polyglots (For 50/50 Rev-Share)

No Hay Tos (Podcast/YT): Hector & Beto (Perfect match for Mexican slang)
Dreaming Spanish (YT): Pablo Román
Spanish and Go (YT): Jim & May
Butterfly Spanish (YT): Ana
Language Lords (YT): Channel Creator
Xiaomanyc (YT): Arieh Smith
Superholly (YT): Holly Radio
Mextalki (YT): Diego & Efraín
How to Spanish (YT): Andrea & Nate
Bilingue Blogs (YT): Kike
Fluent in 3 Months: Benny Lewis
Español con Juan: Juan
Easy Spanish: Juan & Paulina
Qroo Paul: Paul

Pricing Strategy

Influencer Affiliate Rev-Share: 50/50 split. You host the tech; they sell a $149 Lifetime Access pass or a $15/month subscription to their Patreon listeners.
B2B Language School License: $5,000 - $10,000/year to white-label the suite for their students.

The Outreach Script (To Influencers)

Subject: Software collaboration for advanced Spanish language acquisition

Dear [Name],

As a data scientist and language acquisition researcher, I highly regard your approach to teaching authentic, colloquial Mexican Spanish. Traditional gamified applications often fail to convey the etymological depth and cultural context that your platform provides.

I have engineered Veloz, an advanced software suite designed to address this gap. It includes an Active Recall module utilizing a 3-column paradigm (Standard, Alternate, Colloquial), a detailed Slang Explorer mapping regional etymology, and a dynamic reading interface that shifts text natively from English to Spanish to facilitate contextual vocabulary acquisition.

I am seeking a partner with an established audience to integrate this pipeline. I oversee the technical infrastructure and updates, while you offer the platform as a premium resource to your audience on a revenue-share basis.

I have generated a master access account for your review here: [Link]. I would be glad to discuss a potential collaboration.

Sincerely,
Dr. Aaron Del Re

6. The “Honeypot”

The Methodology & System Design

compute.es, MAd, and MAc are your legacy. We do not sell these. They are the ultimate top-of-funnel Lead Generation Engine (Honeypot).

When a VP of RWE Data or a University PI sees that you authored the tools cited in thousands of papers, the conversation immediately shifts from “Are you qualified?” to “When can you start?”

Strategic Dissemination Pathway: Literature-Driven Collaborative Outreach

Implementation Strategy: Establish automated tracking for newly published academic papers citing the compute.es, MAd, or MAc packages. Contact the lead authors to congratulate them on their publication and naturally transition the conversation toward customized statistical consulting or serverless dashboard development for their future grants.

The Execution Strategy (Web/Portfolio UI)

The SEO Play: Ensure that the URL hosting these web apps is meticulously SEO optimized for terms like “Effect Size Calculator,” “Meta-Analysis Generator,” and “Calculate Cohen’s d.” Google will naturally rank it highly because of your CRAN authorship. Every psychology undergrad, PhD candidate, and pharma statistician globally will use your site.
The Call to Action (CTA): Directly beneath the effect size calculators, place a highly visible banner: > “Is your clinical team struggling to build complex Phase 1/Phase 2 predictive pipelines or Mixed-Effects pipelines? Dr. Del Re accepts a limited number of B2B consulting contracts.” [Link to Contact/Calendar]
The Workflow: A pharma data scientist Googles how to aggregate dependent effect sizes, uses your web app, sees your consulting CTA, and realizes they can just hire you to build their predictive dashboard infrastructure.

Overview

Master Blueprint: RWE Decision Support & Cognitive Acquisition

1. Predictive Suite: Model Builder (Phase 1)

The Methodology & System Design

Target Organizations & Target Leadership

Pricing Strategy

The Outreach Script

2. Predictive Suite: Live Predictor (Phase 2 & 3)

The Methodology & System Design

Target Organizations & Target Leadership

Pricing Strategy

The Outreach Script

3. Serverless Mixed-Effects Engine

The Methodology & System Design

Target Organizations & Target Leadership

Pricing Strategy

The Outreach Script

4. PQM Tracker

The Methodology & System Design

Target Organizations & Target Leadership

Pricing Strategy

The Outreach Script (To Researchers)

5. Veloz Suite

The Methodology & System Design

Target Organizations & Target Leadership

Pricing Strategy

The Outreach Script (To Influencers)

6. The “Honeypot”

The Methodology & System Design

The Execution Strategy (Web/Portfolio UI)

Contact Request