Statistical analysis in clinical research must meet a higher evidential standard than most other research domains. The findings directly inform patient care, clinical guidelines, and regulatory decisions. Methodological errors are not just academic failures — they can have real consequences for how treatments are evaluated and adopted.
Clinical study designs each require specific analytical approaches. Randomised controlled trials use intention-to-treat analysis as the primary analysis, with per-protocol analysis as a sensitivity check. Cohort studies require Cox regression or Kaplan-Meier estimation for time-to-event outcomes. Case-control studies use conditional logistic regression. Cross-sectional surveys use weighted prevalence estimates and chi-square or logistic regression for associations.
Confounding is a particular concern in clinical research. Unlike a laboratory experiment, clinical studies cannot always randomise. Propensity score matching, stratified analysis, and multivariate regression are tools for managing confounding in observational studies. Each approach has assumptions that must be documented and justified.
Interim analysis and sample size re-estimation are required for adaptive clinical trial designs. These procedures require pre-specified stopping rules and alpha-spending functions to maintain the overall Type I error rate. If your study uses an adaptive design, get statistical input early — retrofitting adaptive elements to a running trial is methodologically problematic.
Our clinical research statisticians support researchers from protocol design through to publication. We are familiar with ICH-E9 guidelines for statistical aspects of clinical trials, CONSORT reporting standards, and the requirements of major clinical journals including The Lancet, JAMA, BMJ, and PLOS Medicine.
