ABSTRACT The use of historical data to increase power in clinical trials has been a topic of interest for many years. A recent approach adjusts linearly for a prognostic score. This is supported by asymptotic optimality results using influence functions for asymptotically linear estimators as well as finite sample optimality results. We review plug‐in and linear estimators of average treatment effect in randomized clinical trials, sample size determination, and linear adjustment for a prognostic score. Guidelines and recommendations for the implementation of linear adjustment for a prognostic score are given including curation of historical data and construction of a prognostic score based on the historical data. A simulation study is conducted to investigate the performance in finite samples, comparing it to standard procedures such as propensity score matching for RCTs (PSM‐RCT) and ANCOVA using simple baseline adjustment. Unlike PSM‐RCT, linear adjustment for a prognostic score avoids biased treatment effect estimates and maintains control of type I error probability. The simulation study shows that the method is robust against deviations from method assumptions and poor performance of the prognostic model. A case study demonstrates an increase in prospective power using linear adjustment with a prognostic score in a phase IIIb clinical trial for type 2 diabetes. A final discussion considers limitations of the method for example in regard to subgroup analysis and the existence of already known prognostic baseline covariates.
Højbjerre‐Frandsen et al. (Thu,) studied this question.