The benefits of covariate adjustment for adaptive multi-arm designs

TY - JOUR

T1 - The benefits of covariate adjustment for adaptive multi-arm designs

AU - Lee, Kim May

AU - Robertson, David S.

AU - Jaki, Thomas

AU - Emsley, Richard

N1 - Funding Information: This research was supported by the NIHR Maudsley Biomedical Research Centre at South London and Maudsley NHS Foundation Trust and King’s College London, and the NIHR Cambridge Biomedical Research Centre (BRC-1215-20014). RE and KML are supported by the National Institute for Health Research (NIHR Research Professorship, Professor Richard Emsley, NIHR300051). TJ and DSR received funding from the Medical Research Council (MC_UU_00002/14) and DSR also was funded by the Biometrika Trust. For the purpose of open access, the author has applied a Creative Commons Attribution (CC BY) licence to any Author Accepted Manuscript version arising. Funding Information: The views expressed are those of the authors and not necessarily those of the NIHR or the Department of Health and Social Care (DHSC). This research was supported by the NIHR Maudsley Biomedical Research Centre at South London and Maudsley NHS Foundation Trust and King’s College London, and the NIHR Cambridge Biomedical Research Centre (BRC-1215-20014). RE and KML are supported by the National Institute for Health Research (NIHR Research Professorship, Professor Richard Emsley, NIHR300051). TJ and DSR received funding from the Medical Research Council (MC_UU_00002/14) and DSR also was funded by the Biometrika Trust. For the purpose of open access, the author has applied a Creative Commons Attribution (CC BY) licence to any Author Accepted Manuscript version arising. Publisher Copyright: © The Author(s) 2022.

PY - 2022/11

Y1 - 2022/11

N2 - Covariate adjustment via a regression approach is known to increase the precision of statistical inference when fixed trial designs are employed in randomized controlled studies. When an adaptive multi-arm design is employed with the ability to select treatments, it is unclear how covariate adjustment affects various aspects of the study. Consider the design framework that relies on pre-specified treatment selection rule(s) and a combination test approach for hypothesis testing. It is our primary goal to evaluate the impact of covariate adjustment on adaptive multi-arm designs with treatment selection. Our secondary goal is to show how the Uniformly Minimum Variance Conditionally Unbiased Estimator can be extended to account for covariate adjustment analytically. We find that adjustment with different sets of covariates can lead to different treatment selection outcomes and hence probabilities of rejecting hypotheses. Nevertheless, we do not see any negative impact on the control of the familywise error rate when covariates are included in the analysis model. When adjusting for covariates that are moderately or highly correlated with the outcome, we see various benefits to the analysis of the design. Conversely, there is negligible impact when including covariates that are uncorrelated with the outcome. Overall, pre-specification of covariate adjustment is recommended for the analysis of adaptive multi-arm design with treatment selection. Having the statistical analysis plan in place prior to the interim and final analyses is crucial, especially when a non-collapsible measure of treatment effect is considered in the trial.

AB - Covariate adjustment via a regression approach is known to increase the precision of statistical inference when fixed trial designs are employed in randomized controlled studies. When an adaptive multi-arm design is employed with the ability to select treatments, it is unclear how covariate adjustment affects various aspects of the study. Consider the design framework that relies on pre-specified treatment selection rule(s) and a combination test approach for hypothesis testing. It is our primary goal to evaluate the impact of covariate adjustment on adaptive multi-arm designs with treatment selection. Our secondary goal is to show how the Uniformly Minimum Variance Conditionally Unbiased Estimator can be extended to account for covariate adjustment analytically. We find that adjustment with different sets of covariates can lead to different treatment selection outcomes and hence probabilities of rejecting hypotheses. Nevertheless, we do not see any negative impact on the control of the familywise error rate when covariates are included in the analysis model. When adjusting for covariates that are moderately or highly correlated with the outcome, we see various benefits to the analysis of the design. Conversely, there is negligible impact when including covariates that are uncorrelated with the outcome. Overall, pre-specification of covariate adjustment is recommended for the analysis of adaptive multi-arm design with treatment selection. Having the statistical analysis plan in place prior to the interim and final analyses is crucial, especially when a non-collapsible measure of treatment effect is considered in the trial.

KW - Adaptive design

KW - covariate adjustment

KW - multi-arm

KW - treatment selection

KW - UMVCUE

UR - http://www.scopus.com/inward/record.url?scp=85134773938&partnerID=8YFLogxK

U2 - 10.1177/09622802221114544

DO - 10.1177/09622802221114544

M3 - Article

AN - SCOPUS:85134773938

SN - 0962-2802

VL - 31

SP - 2104

EP - 2121

JO - Statistical Methods in Medical Research

JF - Statistical Methods in Medical Research

IS - 11

ER -