Anders Perner

Different combined outcome‐data lags (follow‐up durations plus data‐collection lags) may affect the performance of adaptive clinical trial designs. We assessed the influence of different outcome‐data lags (0–105 days) on the performance of various multi‐stage, adaptive trial designs (2/4 arms, with/without a common control, fixed/response‐adaptive randomisation) with undesirable binary outcomes according to different inclusion rates (3.33/6.67/10 patients/day) under scenarios with no, small, and large differences. Simulations were conducted under a Bayesian framework, with constant stopping thresholds for superiority/inferiority calibrated to keep type‐1 error rates at approximately 5%. We assessed multiple performance metrics, including mean sample sizes, event counts/probabilities, probabilities of conclusiveness, root mean squared errors (RMSEs) of the estimated effect in the selected arms, and RMSEs between the analyses at the time of stopping and the final analyses including data from all randomised patients. Performance metrics generally deteriorated when the proportions of randomised patients with available data were smaller due to longer outcome‐data lags or faster inclusion, that is, mean sample sizes, event counts/probabilities, and RMSEs were larger, while the probabilities of conclusiveness were lower. Performance metric impairments with outcome‐data lags ≤45 days were relatively smaller compared to those occurring with ≥60 days of lag. For most metrics, the effects of different outcome‐data lags and lower proportions of randomised patients with available data were larger than those of different design choices, for example, the use of fixed versus response‐adaptive randomisation. Increased outcome‐data lag substantially affected the performance of adaptive trial designs. Trialists should consider the effects of outcome‐data lags when planning adaptive trials.