Statistical Methods for Handling Missing Data to Align with Treatment Policy Strategy

The primary objective of a clinical study is usually to assess a product's effectiveness and safety based on the planned treatment regimen instead of the actual treatment received. The estimand using the treatment policy strategy, which collects and analyzes data regardless of the occurrence of intercurrent events, is usually utilized to align with this study objective. In this presentation, the speaker will explain how missing data can be handled using the treatment policy strategy in connection with antihyperglycemic product development programs. Five statistical methods (retrieved dropouts, return-to baseline, placebo wash out, jump to reference, copy reference) to impute missing data occurring after intercurrent events will be discussed and compared via Markov Chain Monte Carlo simulations. Case studies will be presented to show how three of these five methods have been applied to estimate the treatment effects published in the labels for three antihyperglycemic agents currently on the market.

treatment policy estimand

retrieved dropout

placebo washout

return to baseline