A/B testing is critical for modern technological companies to evaluate the effectiveness of newly developed products against standard baselines. This paper studies optimal designs that aim to maximize the amount of information obtained from online experiments to estimate treatment effects accurately.

Popular algorithms suchasUCB[4,5]andThompson sampling [3,34]typically explorethearms sufficiently and as more evidence is gathered, converge to the optimal arm.

Modeling dependencies in multivariate discrete data is a challenging problem, especiallyinhighdimensions.

In the following equation, we use the results inAppendix D.1 tocalculate the probability that there exists some arm whose mean value isaboveitsconfidence intervalofwidth

Treatment individualization and adaptation over time are crucial for managing chronic diseases.

A targeted learning (TL) framework is developed to estimate the difference in the restricted mean survival time (RMST) for a clinical trial with time-to-event outcomes. The approach starts by defining the target estimand as the RMST difference between investigational and control treatments. Next, an efficient estimation method is introduced: a targeted minimum loss estimator (TMLE) utilizing pseudo-observations. Moreover, a version of the copy reference (CR) approach is developed to perform a sensitivity analysis for right-censoring. The proposed TL framework is demonstrated using a real data application.