Services
Biostatistics consulting for medical researchers, small companies, and nonprofits.
What I do
I work with medical researchers, small companies, and nonprofits on the methodological pieces of clinical and health-services research, including the parts that have to hold up under journal-club critique, IRB review, peer review, and regulatory scrutiny.
The practice runs across four surfaces. Each can be a one-off engagement (a methods review on an existing study, a sample-size memo, a methodology section for a manuscript, a sensitivity-analysis design) or part of a longer-term advisory relationship through study design, analysis, and write-up.
Study design
What I help with. Research-question framing, target trial emulation, study population definition, inclusion / exclusion criteria, sample size and power calculation, treatment-comparator-outcome (PICO / PECO) specification, primary versus secondary endpoint selection, protocol writing, pre-registration drafting.
Typical engagements. A resident designing their first observational study and needing a defensible analysis plan before data collection. A small company scoping a clinical or observational trial and trying to translate a clinical hypothesis into a statistical one. An NGO running a program evaluation that needs to be designed for credible causal claims rather than just descriptive reporting.
Portfolio example. The Medicare Part D insulin DiD case study walks through how a clean difference-in-differences design is constructed: simultaneous treatment, honest control group, parallel-trends defense, placebo tests, leave-one-out robustness.
Analysis methods
What I help with. Causal inference (difference-in-differences, regression discontinuity, instrumental variables, synthetic control, target trial emulation), propensity score methods (matching, weighting, doubly-robust estimators), survival analysis, longitudinal data methods (mixed effects, GEE), Bayesian methods for clinical and health-services applications, Monte Carlo simulation for sample size or bias quantification.
Typical engagements. An attending whose RCT was disrupted by an external event and now needs causal-inference repair work to salvage the analysis. A company validating a real-world evidence claim against regulatory expectations. A researcher whose existing analysis needs strengthening before submission to a methods-rigorous journal.
Portfolio example. The Medicaid outliers case study walks through robust statistics on heavy-tailed data, BH-FDR multiplicity correction, isolation-forest triangulation, and the methodological subtleties of applying these tools at scale on claims data.
Sensitivity and robustness
What I help with. Pre-specified sensitivity analyses, placebo and falsification tests, robustness across model specifications, leave-one-out diagnostics, bias quantification (E-values, Rosenbaum bounds), unmeasured-confounding sensitivity, missing-data sensitivity (multiple imputation, pattern-mixture models), structural-overlap diagnostics for comparator analyses.
Typical engagements. A manuscript revision where reviewers requested additional sensitivity analyses and the authors need design help on which to run and how to report them. A regulatory submission where the headline result needs defensible robustness work. A methods peer-review job for a journal or funder.
Portfolio example. The NHANES cardiometabolic case study walks through calibration over discrimination as the load-bearing metric, case-definition sensitivity bands, and the structural-overlap trap in comparator analyses.
Methods writing
What I help with. Methods sections for manuscripts, grant applications, IRB protocols, and regulatory submissions; reviewer responses on methodology; methods primers and statistical analysis plan (SAP) drafting; methods appendices and supplementary methods writing; methodology frameworks for emerging areas (such as clinical AI and real-world evidence).
Typical engagements. A resident writing up their first manuscript and needing a methods section that survives peer review. An attending facing reviewer methods queries that require a careful written response. A company assembling a regulatory submission whose methods documentation needs to meet FDA or EMA expectations. A methodology-focused position paper or framework that needs to be drafted, structured, and reviewed.
Portfolio example. The Risk-of-Bias appraisal for AI training corpora framework is an example of structured methods writing: six bias domains with inline signaling questions, a stylized worked example end-to-end, explicit limitations and open questions. The voice and structure of that piece is the kind of methods writing I produce for client engagements.
How an engagement starts
Every engagement starts with a free 20-minute discovery call to scope the question, the timeline, and the deliverable. The call is non-committal; you leave it knowing whether the work is a fit and what a proposal would look like.
After the call, you receive a written proposal with scope, methodology, deliverable, timeline, and fee. Each proposal is written to the specific study; there is no flat-rate price list, and the fee depends on the scope you actually need rather than a tier you have to fit into. Most one-off engagements (methods review, sample-size memo, methodology section, sensitivity-analysis design) complete in two to six weeks. Longer advisory relationships (study design through analysis through write-up) are scoped as multi-month proposals, typically over three to six months.
If you want the methodology reference in addition to (or instead of) a scoped engagement, the Handbook is free to read: structured chapters on study design, populations and sample sizes, causal inference, sensitivity analysis, and Monte Carlo simulation. The Confounder delivers it to your inbox over time, plus dispatches on new research and methods.
Background
Physician (MD), MPH in epidemiology and biostatistics from Johns Hopkins, practicing clinical data scientist working with EHR, claims, and SDoH data at scale. Past methods work includes a systematic review that shaped Wilms tumor chemotherapy guidelines for the Philippines, and current methodology frameworks for clinical-AI evidence appraisal.