Biopharma & Healthcare Consulting

Regulatory-ready evidence and measurement support that most often sit inside broader data science engagements.

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This capability most often supports Data Science work when clinical, outcomes, and evidence-generation questions require stronger modeling, validated measurement, and executive-ready interpretation.

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Scientific partnership, clinical credibility

Advisory Partner: Allison B. Reiss, MD

Board-certified in Internal Medicine and Associate Professor (Medicine; Foundations of Medicine), Dr. Reiss is a physician-scientist with 25+ years of research spanning inflammation, atherosclerosis, neurodegeneration (including Alzheimer’s disease), lipid metabolism, and cardiometabolic risk. She mentors clinician-scientists and directs translational research and education initiatives.

Focus areas: Alzheimer’s & neurodegeneration · Immunology & inflammation · Cardiometabolic disease · Lipids & metabolism · TBI · Nutrition & cognition.

What this means for your team

Work bridges rigorous statistics and real-world clinical judgment—so measures are valid, endpoints are defensible, and narratives land with medical, regulatory, and business stakeholders. Expect clarity on study design, measurement, and decisions.

Note: Academic affiliations are listed for background only and do not imply institutional endorsement of PrimeStata.

Where work creates value

Validated measurement

Psychometric rigor for clinical scales and PROs: construct mapping, EFA/CFA, IRT/DIF, reliability (α/ω), and interpretable score bands.

Study design & endpoints

Sample size & power, endpoint selection, Johnson–Neyman regions, guardrails, and pre-registration language your IRB will actually appreciate.

RWD/RWE & modeling

Clean, link, and model real-world data: survival/hazard, hierarchical/GLMM, uplift, subgroup consistency (Breslow–Day), and bias checks.

Regulatory-ready narratives

Executive briefs and technical appendices with assumptions, uncertainty, effect sizes, and traceable decisions for reviewers and KOLs.

Core modules

Clinical Measurement & PROs

Scale selection or development, validation (EFA/CFA/IRT), DIF/fairness, scoring, and interpretation guides.

Methods →

Evidence Generation

Observational design, comparative effectiveness, survival models, and sensitivity analyses aligned to regulatory expectations.

Case snapshots →

Medical & Scientific Strategy

Key claims and story architecture for medical affairs, publications planning, and advisory boards.

Tooling & delivery →

Health Outcomes & UX

Patient-centric endpoints, digital biomarkers, and clinician workflows that convert insight into practice.

Process →

Methods—plain language, clinical rigor

Psychometrics

EFA/CFA for structure, IRT for item behavior, DIF for fairness, reliability for stability, and score bands for clinical decisions.

Causal & predictive

Hierarchical/GLMM, survival/hazard, propensity and weighting, uplift models with transparent explanations (SHAP/ICE).

Subgroups & consistency

Breslow–Day, Johnson–Neyman, and interaction probes to know where effects hold before you scale.

Ethics & governance

Bias audits, privacy by design, model cards, and decision logs—documentation built for IRB and regulatory audiences.

Example outcomes

Neurodegeneration PRO

New patient-reported scale validated (CFA/IRT); scoring guide enabled cleaner endpoints and reduced screen failure variability.

Cardiometabolic RWE

Linked EHR + claims; survival modeling identified adherence drivers and risk cohorts—informing targeted education.

Medical narrative

Mechanism-to-outcome storyline connecting inflammatory pathways to cognition; supported payer and KOL engagement.

Tooling & delivery

Data & environments

R, Python, SQL, RStudio, jamovi; secure file exchange; reproducible notebooks; parameterized Quarto/RMarkdown reports.

Artifacts

Protocol snippets, analysis plans, validation reports, executive briefs, technical appendices, and publication-ready figures.

Access & security

Least-privilege access, de-identified datasets preferred; no production changes required to start discovery.

Collaboration

Clinical, HEOR, medical affairs, and data teams; KOLs and advisory boards; versioned decisions with review cadence.

Engagement options

Diagnostic Brief

Scope, metrics, and feasibility review. Available signals (EHR/claims/surveys) are assessed, endpoints outlined, and a one-page plan returned.

Typical timeline: 1–2 weeks · Fixed-fee engagement

Discuss Scope

Evidence Sprint

Answer 1–2 priority questions with validated analyses and a submission-friendly appendix reviewers can trace end-to-end.

Typical timeline: 4–8 weeks · Project-based

Discuss Scope

Full Program Enablement

End-to-end measurement, modeling, dashboards, and governance across studies—plus medical narrative and publication support.

Typical timeline: 6+ months · Retainer model

Discuss Scope

How we work

01. Intake

Clarify decisions, endpoints, timelines, and constraints. Define a minimal viable metric set and data map.

02. Validate

Run measurement checks (EFA/CFA/IRT/DIF), data quality audits, and pre-specify analyses and guardrails.

03. Analyze

Fit interpretable models with uncertainty and subgroup consistency; document assumptions and sensitivity.

04. Ship

Executive brief plus technical appendix, optional dashboards, and a narrative you can take to reviewers and KOLs.

Discuss Scope

Need a fast, defensible plan for your next milestone?

Share datasets and endpoints if useful, or move directly to a consultation to scope the evidence path around the decision in front of you.

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