Statistical methods for handling missing data to align with treatment policy strategy.

The International Council for Harmonization (ICH) E9(R1) addendum recommends choosing an appropriate estimand based on the study objectives in advance of trial design. One defining attribute of an estimand is the intercurrent event, specifically what is considered an intercurrent event and how it should be handled. 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. In this article, we explain how missing data can be handled using the treatment policy strategy from the authors' viewpoint in connection with antihyperglycemic product development programs. The article discusses five statistical methods to impute missing data occurring after intercurrent events. All five methods are applied within the framework of the treatment policy strategy. The article compares the five methods via Markov Chain Monte Carlo simulations and showcases 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.

Utilizing shared internal control arms and historical information in small-sized platform clinical trials.

Recruitment of patients in concurrent control arms can be very challenging for clinical trials for pediatric and rare diseases. Innovative approaches, such as platform trial designs, including shared internal control arm(s), can potentially reduce the needed sample size, improving the efficiency and speed of the drug development program. Furthermore, historical borrowing, which involves leveraging information from control arms in previous relevant clinical trials, may further enhance a clinical trial's efficiency. In this paper, we discuss platform trials highlighting their advantages and limitations. We then compare various strategies that borrow historical data or information, such as pooling data from different studies, analyzing data from studies separately, test-then-pool, dynamic pooling, and Bayesian hierarchical modeling, which focuses on the meta-analytic-predictive (MAP) prior. We further propose a procedure to illustrate the feasibility of utilizing historical controls under a platform setting and describe the statistical performance of our method via simulations.