Sensitivity Analysis in Non-Inferiority Trials with Residual Inconstancy After Covariate Adjustment

Abstract

A major issue in non-inferiority trials is the controversial assumption of constancy, namely that the active control has the same effect relative to placebo as in previous studies comparing the active control with placebo. The constancy assumption is often in doubt, which has motivated various methods that 'discount' the control effect estimate from historical data as well as methods that adjust for imbalances in observed covariates. We develop a new approach to deal with residual inconstancy, i.e. possible violations of the constancy assumption due to imbalances in unmeasured covariates after adjusting for the measured covariates. We characterize the extent of residual inconstancy under a generalized linear model framework and use the results to obtain fully adjusted estimates of the control effect in the current study based on plausible assumptions about an unmeasured covariate. Because such assumptions may be difficult to justify, we propose a sensitivity analysis approach that covers a range of situations. This approach is developed for indirect comparison with placebo and effect retention, and implemented through additive and multiplicative adjustments.The approach proposed is applied to two examples concerning benign prostate hyperplasia and human immunodeficiency virus infection, and evaluated in simulation studies.

Joint Estimation of Treatment and Placebo Effects in Clinical Trials With Longitudinal Blinding Assessments

“…Because these assumptions cannot be verified with observed data, it is important to assess the impact of possible violations of these assumptions. In Web Appendix B, we examine the impact of unmeasured confounders using the approaches of Brumback et al (2004) and Zhang et al (2014). In Web Appendix C, we consider possible departures from the sequential ignorability assumption under a location shift model.…”