Speaker
Description
Background:
Multiple endpoints are a major topic of discussion in rare disease research, particularly regarding to patient-centered outcome measures, as they allow for a more comprehensive assessment of treatment effects. However, a critical challenge in these trials is allocation bias, as they are often unblinded or single-blinded. Allocation bias arises when future treatment allocations can be predicted from prior ones, potentially leading to the preferential assignment of patients with specific characteristics to either the treatment or control group. Despite its potential impact, the effects of allocation bias in clinical trials with multiple endpoints remains insufficiently studied.
Methods:
To quantify allocation bias in two-arm parallel group trials with continuous multiple endpoints, we derived a biasing policy based on the convergence strategy of Blackwell and Hodges. We assessed the impact of allocation bias by evaluating type I error rates of various multiple testing approaches, including the Bonferroni correction, all-or-none, and Wei-Lachin methods, in the presence of bias. In a simulation study we computed these type I error rates across various randomization procedures and evaluated whether allocation bias leads to inflated error rates.
Results:
Simulations show that allocation bias inflates type I error rates, leading to incorrect statistical conclusions. Even small bias effects can cause the nominal 5% significance level to be exceeded. The extent of inflation depends on the chosen randomization procedure and the multiple testing approach used. Less restrictive randomization procedures, such as Complete Randomization and the Big Stick Design, exhibited the lowest type I error inflation, while Permuted Block Randomization results in the highest type I error inflation.
Conclusion:
Allocation bias threatens the validity of clinical trials and should be minimized through careful study design. In particular, selecting a randomization procedure that reduces susceptibility to allocation bias is crucial. Regardless of the analytical approach, adopting less restrictive randomization procedures, such as the Big Stick Design, can reduce allocation bias and improve the reliability of trial results. Informing the scientific community that the Big Stick Design outperforms Permuted Block Designs in preventing allocation bias effects on test decisions is particularly important, especially given that the randomization section of the ICH E9 guideline still refers to block randomization. The developed methodology provides guidance on selecting bias-mitigating randomization procedures, contributing to more robust trial designs. Moreover, this approach can be extended to enable bias-adjusted testing, offering a way to correct for allocation bias and ensure more valid results in rare disease trials.
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