Bias-correction in adaptive trials using Stata

Adaptive trials permit design features - sample size, randomization ratios, or enrolment criteria - to be changed based on accumulating data and are of particular interest in drug trials. A novel potential application is in public health and social measures (PHSM) trials. While PHSM were widely used during COVID-19, evidence on their benefits and harms remains limited. PHSM trials are challenging because the sampling frame is confined to the winter respiratory infection season, and measures can be burdensome, limiting enrolment. Adaptive PHSM trials can reduce sample sizes, spread recruitment across multiple winters, and allow early stopping for efficacy or futility, facilitating reallocation of research resources. While adaptive designs control type I and type II error, data-dependent adaptations tend to bias conventional estimators. This is well-documented, and a substantial corrective literature exists, yet systematic reviews find that bias correction is rarely used. This may be because analytic corrections are design- and outcome-specific, and there is little software support. I will present a prototype Stata command for bias correction applicable to arbitrarily complex adaptive designs. I will also show results for simulation-based experiments validating the command in a range of adaptive designs, including a real PHSM trial: an ongoing group sequential trial of portable air purifiers in schools to reduce student absence due to illness.