## A bias-adjusted analysis of stratified clinical trials with multi-component endpoints using the Wei-Lachin test
#Background:
##Background:
The disease heterogeneity and geographic dispersions of patients in rare diseases often necessitates a multi-center design and the use of multi-component endpoints, which combine multiple outcome measures into a single score. A common issue in these trials is allocation bias, as trials in rare diseases are frequently unblinded or single-blinded. The ICH E9 guideline recommends assessing the potential contributions of bias to inference. Therefore, we aim to develop a bias-adjusted analysis strategy for stratified clinical trials with multi-component endpoints.
#Methods:
##Methods:
To model biased patient responses, we derived an allocation biasing policy based on the 'convergence strategy' of Blackwell and Hodges tailored to stratified clinical trials with multi-component endpoints. Using this policy, we developed a bias-adjusted analysis strategy for a stratified version of the Wei-Lachin test, integrating Fleiss's stratified test with the Wei-Lachin test.
#Results:
##Results:
When allocation bias is present, ignoring it during trial evaluation with the stratified Wei-Lachin test increases the type I error rate, potentially exceeding the 5% significance level and inflating power beyond the nominal power of 80%, leading to an overestimation of the treatment effect. In contrast, the bias-adjusted stratified Wei-Lachin test preserves the 5% significance level while maintaining approximately 75–80% power, depending on the randomization procedure and the extent of allocation bias.
#Conclusion:
##Conclusion:
In stratified clinical trials with multi-component endpoints that are susceptible to allocation bias — such as those that are unblinded or single-blinded, lack allocation concealment, involve numerous strata, or use restrictive randomization procedures such as stratified PBR — incorporating a bias-adjusted test as a sensitivity analysis enhances the validity of trial results. Accounting for allocation bias during the analysis phase strengthens the robustness of clinical trials, leading to more reliable and accurate conclusions.
