YouTube Marketing Strategy: Del Re Data & Statistical Consulting

Goal: Drive high-ticket B2B leads ($20k - $100k contracts) from Pharma VPs, Biotech Directors, HealthTech CEOs, and NIH PIs. Format: No fluffy intros or outros. A 5-second aggressive hook, 90 seconds of highly-visual “PhD Shark” authority content (using Google Flow, screen-shares of your Quarto pipelines, or direct-to-camera), and a strict Call to Action (CTA) pointing to your consulting page.

Category 1: The “Clinical Trial Savior” (Targeting Pharma, Biotech & NIH PIs)

Focus: Visualizing how bad math destroys multi-million dollar trials, and how your Longitudinal Mixed Models fix them.

  1. Why Your Clinical Trial’s ‘Intention-to-Treat’ Analysis is Probably Flawed
    • The Hook: “If you are using standard Intention-to-Treat models for a longitudinal drug trial, you are probably leaving signal on the table.”
    • The Content: Based on your 2013 BMJ Open paper on alcohol use disorders. Use Google Flow to visually animate patient attrition over time, showing how standard models fail to capture the missing data, while your specific mixed-models isolate the true drug effect.
    • The CTA: “If your trial data is drowning in noise and attrition, my team builds the exact predictive engines to salvage it. Link in description.”
  2. Standard Linear Models are Lying to You About Your Drug’s Efficacy
    • The Hook: “Averages lie. If your biostatistician is using standard ANOVA on multi-site clinical data, fire them.”
    • The Content: A brutal visual teardown of standard ANOVA vs. Hierarchical Linear Modeling (HLM). Explain how ignoring site-level clustering (like you handled at Progenity) mathematically guarantees false positives.
    • The CTA: “We build Hierarchical Mixed Models that survive FDA audits. Book a call below.”
  3. The Placebo Group Illusion in Pharmacotherapy
    • The Hook: “Placebo groups don’t stay flat. If you don’t account for placebo improvement over time, your drug looks like a failure.”
    • The Content: Based on your 2013 Journal of Clinical Psychopharmacology paper. Use Flow to visualize the trajectory of placebo improvement. Show how to statistically control for this shifting baseline.
  4. N-of-1 Trials: The Secret to Personalized Medicine Data
    • The Hook: “You don’t need 1,000 patients to prove your intervention works. You need 1 patient, tracked longitudinally.”
    • The Content: A deep dive into N-of-1 trial design. Explain how you structure intra-individual data to prove causality without massive control groups. Highly attractive to specialized biotech and rare-disease startups.
  5. Stop Using Excel for NIH Grants: The Power of R-Meditation
    • The Hook: “If your research team is manually copying p-values into Excel, your workflow is dangerously prone to error.”
    • The Content: Show a real-time screen share of your automated R/Quarto pipelines. Hit ‘Render’ and watch it generate an audit-ready, APA-formatted PDF instantly. Sell “R-Meditation” as the ultimate risk mitigation tool.

Category 2: The “HealthTech Architect” (Targeting App CEOs & Founders)

Focus: Positioning yourself as the Chief Data Architect who builds the proprietary “Brain” of health apps.

  1. How We Built a Real-World Evidence (RWE) Engine in 30 Days
    • The Hook: “Most health apps are just glorified diaries. Here is how we turned one into a Predictive RWE Engine.”
    • The Content: Screen-share your Empirical Predictive Engine app. Walk through the LLMM backend and explain how it translates messy user inputs into hard efficacy data for B2B sales.
  2. Engineering the Algorithm Behind a Top Psychotherapy App
    • The Hook: “How do you predict if a therapy patient is going to drop out before they actually do?”
    • The Content: A case study on your work with MyOutcomes. Discuss the logic of building a proprietary predictive treatment algorithm based on historical clinical trajectories.
  3. Measuring the Unmeasurable: Tracking Quality, Not Just Quantity
    • The Hook: “Counting the minutes a user meditates is useless if you aren’t measuring the quality of the session.”
    • The Content: Leverage your PhD dissertation and the PQM Tracker app. Pitch your ability to design validated psychometric scoring systems (like PQ-M) that give apps a proprietary competitive advantage.
  4. Automating Data Intelligence for 10,000+ Users
    • The Hook: “When you scale past 10,000 users, your dashboard will crash if your database isn’t communicating with your statistical engine.”
    • The Content: A case study on the MoreBetter RWE pipeline. Show HealthTech CEOs how you handle massive scale by separating the UI from the heavy statistical processing.
  5. Why Your Health App Needs a ‘Motor-Reflex Engine’
    • The Hook: “Your learning app isn’t teaching; it’s just testing memory. You need a motor-reflex engine.”
    • The Content: Use Veloz as a case study. Explain the architecture behind cognitive/reflex loops and how you programmatically decrease latency to build fluency.

Category 3: The “Meta-Analysis Authority” (Targeting Research Teams)

Focus: Flexing your academic credibility (12,500+ citations) and CRAN packages.

  1. I Wrote the R Package for Effect Sizes: Here is Where Most Researchers Fail
    • The Hook: “I wrote the global R package for effect size computation. Here is the #1 mistake I see researchers make every day.”
    • The Content: The ultimate authority flex. Talk about building compute.es and MAd, and walk through the Compute ES Desktop app.
  2. Therapist Effects vs. Treatment Effects: A Multilevel Breakdown
    • The Hook: “In clinical trials, who delivers the treatment often matters more than what the treatment is.”
    • The Content: Visualize your highly-cited 2021 JCCP paper. Show how a multilevel meta-analysis separates the therapist’s variance from the drug/therapy’s variance.
  3. Network Meta-Analysis Explained Visually
    • The Hook: “How do you compare Drug A to Drug C when they’ve never been tested against each other?”
    • The Content: Use Google Flow to visually explain Network Meta-Analysis. Show the nodes connecting, establishing your dominance in high-level research synthesis.
  4. How to Calculate Your True ‘Scientific C-Score’
    • The Hook: “Your h-index is lying about your actual scientific impact. Here is how to calculate your true C-Score.”
    • The Content: Screen-share your C-Score Shiny app. Discuss how it ranks researchers against global norms. This is highly shareable and viral within academic circles.
  5. Inside the US Navy’s Epidemiological Data
    • The Hook: “I spent two years analyzing epidemiological data for the US Navy. Here is what actual ‘Big Data’ looks like.”
    • The Content: Leverage your NHRC background to talk about extracting psychological readiness signals from massive, noisy databases to present to military leadership.

Category 4: “Out of the Box” / Shark Tactics

Focus: Aggressive, contrarian hooks that disrupt the industry standard and demand attention.

  1. The $50,000 Mistake Your Biostatistician is Making Right Now
    • The Hook: “If your data science team is ignoring nested data structures, your entire clinical trial is at risk.”
    • The Content: Call out the industry standard of using basic regressions when Hierarchical Linear Modeling (HLM) is required. Prove mathematically why it’s a liability.
  2. Don’t Hire a React Developer for Your Analytics Dashboard
    • The Hook: “A front-end developer can make a beautiful chart, but they cannot build a predictive statistical engine.”
    • The Content: Explain the gap between software engineering and statistical modeling, and why hiring a Data Architect (who speaks both R and JS) is cheaper and faster.
  3. Live Teardown: Rebuilding the Data Architecture of [Famous App]
    • The Hook: “Noom has a great UI, but their backend predictive modeling is leaving millions on the table. Here is how I would rebuild it.”
    • The Content: Take a massive public app and aggressively explain exactly how you would improve their proprietary scoring algorithm using your methods.
  4. Why ‘Average Patient’ Demographics are Ruining Healthcare Apps
    • The Hook: “Stop designing your health app for the ‘average’ user. The average user does not exist.”
    • The Content: Explain the statistical concept of “ecological fallacy” and why personalized modeling (Mixed Models / N-of-1) is the only way to build effective health interventions.
  5. The R-Meditation Manifesto: Achieving 100% Reproducibility
    • The Hook: “If you are clicking buttons in SPSS or Tableau to generate your reports, your data is not reproducible, and it will fail an audit.”
    • The Content: A manifesto video on why code-driven Quarto pipelines are the only acceptable standard for FDA/audit-ready reporting.

Generated based on CV, Upwork history, published CRAN packages, Veloz, Empirical Predictive Engines, PQM Tracker, and C-Score apps.

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