Integrating non-concurrent controls in the analyses of late-entry experimental arms in multi-arm trials with a shared control group in the presence of parameter drift

Background: Under a master protocol, open platform trials allow new experimental treatments to enter an existing clinical trial. Whether late-entry experimental treatments should be compared to all available or concurrently randomized controls is not well established. Using all available data can increase power and precision; however, drift in population parameters can yield biased estimates and impact type I error rate. Methods: We explored the application of methods developed to incorporate historical controls in two-arm trials to the analysis of a late-entry arm in a simulated open platform trial under varying scenarios of parameter drift. Methods explored include test-then-pool, fixed power prior, dynamic power prior, and multi-source exchangeability model approaches. Results/Conclusions: Simulated trial results confirm that in the presence of no drift, naively pooling all controls increases power and produces more precise, unbiased estimates when compared to using concurrent controls only. However, under drift, pooling can result in type I error rate inflation or deflation and biased estimates. In the presence of parameter drift, methods that partially borrow non-concurrent data, either through a static weighting mechanism or through methods that allow the heterogeneity between non-concurrent and concurrent data to determine the degree of borrowing, are superior to naively pooling the data. However, compared to using concurrent controls only, these approaches cannot guarantee type I error control or unbiased estimates. Thus, concurrent controls should be used as comparators in confirmatory studies.