A novel statistical test for treatment differences in clinical trials using a response-adaptive forward-looking Gittins Index Rule.

The most common objective for response-adaptive clinical trials is to seek to ensure that patients within a trial have a high chance of receiving the best treatment available by altering the chance of allocation on the basis of accumulating data. Approaches that yield good patient benefit properties suffer from low power from a frequentist perspective when testing for a treatment difference at the end of the study due to the high imbalance in treatment allocations. In this work we develop an alternative pairwise test for treatment difference on the basis of allocation probabilities of the covariate-adjusted response-adaptive randomization with forward-looking Gittins Index (CARA-FLGI) Rule for binary responses.

Comparing sampling methods for pharmacokinetic studies using model averaged derived parameters.

Pharmacokinetic studies aim to study how a compound is absorbed, distributed, metabolised, and excreted. The concentration of the compound in the blood or plasma is measured at different time points after administration and pharmacokinetic parameters such as the area under the curve (AUC) or maximum concentration (C ) are derived from the resulting concentration time profile. In this paper, we want to compare different methods for collecting concentration measurements (traditional sampling versus microsampling) on the basis of these derived parameters.