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Adaptive enrichment allows for pre-defined patient subgroups of interest tobe investigated throughout the course of a clinical trial. Many trials whichmeasure a long-term time-to-event endpoint often also routinely collectrepeated measures on biomarkers which may be predictive of the primaryendpoint. Although these data may not be leveraged directly to support subgroupselection decisions and early stopping decisions, we aim to make greater use ofthese data to increase efficiency and improve interim decision making. In thiswork, we present a joint model for longitudinal and time-to-event data and twomethods for creating standardised statistics based on this joint model. We canuse the estimates to define enrichment rules and efficacy and futility earlystopping rules for a flexible efficient clinical trial with possibleenrichment. Under this framework, we show asymptotically that the familywiseerror rate is protected in the strong sense. To assess the results, we considera trial for the treatment of metastatic breast cancer where repeated ctDNAmeasurements are available and the subgroup criteria is defined by patients' ERand HER2 status. Using simulation, we show that incorporating biomarkerinformation leads to accurate subgroup identification and increases in power.

Adaptive enrichment trial designs using joint modeling of longitudinal and time-to-event data