Speaker
Description
In rare diseases, the need for innovative clinical trial designs is increasing. Platform trials are becoming particularly popular, as they allow for flexible adding and dropping of arms and reduce sample size requirements by using a shared control. In a platform trial setting with two experimental arms and one control, we use clinical trial simulations to quantify the impact on operating characteristics such as type I error rate, power, and bias caused by allocation and chronological bias.
Especially if a trial is not blinded, allocation bias may damage the integrity of the trial. If the researcher could predict the next allocation, it might be tempting to include a “better” patient if the next treatment is more likely to be an experimental treatment, using the information from a prognostic marker. To quantify the allocation bias, we evaluate and compare different allocation biasing policies like the Blackwell-Hodges convergence strategy. We investigate the impact on the type I error rate for different randomization methods such as permuted block randomization or complete randomization. Furthermore, we evaluate both one-step and two-step randomization for these methods and we explore how the error rate changes depending on the entry of the treatment arm of interest.
A chronological bias can occur if there are time-related changes in the outcome, e.g., caused by changes in the patient population when treatment arms leave or enter the trial. We explore different time trends such as step functions, linear, or seasonal trends.
We show how the results are impacted when using either only concurrent controls, or enriching the analysis by using non-concurrent control data from patients who have been included into the study before the treatment of interest joined the platform trial.
Results show that allocation bias substantially inflates Type I error when using permuted block randomization, particularly with small block sizes. It is less pronounced when using complete randomization. Chronological bias causes the highest type I error inflation in the presence of monotonically increasing trends, especially when simply pooling all available controls, i.e., both concurrent and non-concurrent. However, using an ANOVA model adjusted for time periods, rather than a standard t-test, corrects for the type I error inflation caused by time trends.
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