Abstract
The method of principal stratification is a unifying framework for modelling cause and effect which is applicable to adjusting for treatment noncompliance in multiple arms of a trial. Baseline covariates which predict compliance with treatment are useful in addressing parameter identification problem associated with principal stratification. Roy, Hogan and Marcus (RHM) (2008) proposed a principal stratification framework in which they used baseline covariates to adjust for imperfect compliance in both arms of a two-active treatments trial. Key to the application of this method is a defining but untestable distributional assumption whose robustness is unknown. The present work uses statistically designed simulation studies in the framework of a clinical trial comparing two active treatments as applied to survival data under both homogeneous and heterogeneous treatment effect assumptions to evaluate the performance of the RHM method in terms of bias and $95\%$ credible intervals. We first apply the standard proportional hazard model to obtain the ITT estimate and evaluate resulting bias if viewed as estimating a causal hazard ratio. We then compare the method's performance in terms of stratum-specific causal relative risk for different specifications of a user-defined spectrum parameter. The results showed no effect of the spectrum parameter on the ITT estimates. The RHM method performed poorly by producing significantly biased efficacy estimates in all strata with wider corresponding $95\%$ credible intervals under heterogeneous treatment effect assumption. The resulting efficacy estimates varied a lot depending on the value of the unknown (user-defined) spectrum parameter.
Highlights
Estimating causal effects is a primary objective in most medical studies which compare two or more interventions
The mean compliance proportion reduced as φ values increased among those patients likely to comply with one treatment only
While the principal effects of treatment for those patients likely to comply with their respective treatment allocation were smaller than the ITT estimates under the assumption of homogeneous treatment effects, the effects were larger than ITT for the heterogenous case
Summary
Estimating causal effects is a primary objective in most medical studies which compare two or more interventions. This task may be achieved in randomized clinical trials under perfect compliance with treatment assignment. When there is imperfect compliance with allocation to intervention, the ITT produces biased efficacy estimate when the effects of treatment non-compliers mixes with the effects of compliers (White & Pocock, 1996). In-treatment and as-protocol analyses are mostly used to augment ITT estimates while evaluating true treatment effects These post-hoc analyses are devoid of the tenets of randomization and are likely to produce biased estimates due to selective choices arising from the underlying nature/pattern of arm-specific compliance (White, 2005; Little et al, 2009)
Sign-in/Register to view highlights & summary Talk to us
Join us for a 30 min session where you can share your feedback and ask us any queries you have
Schedule a callMore From: International Journal of Statistics and Probability
Disclaimer: All third-party content on this website/platform is and will remain the property of their respective owners and is provided on "as is" basis without any warranties, express or implied. Use of third-party content does not indicate any affiliation, sponsorship with or endorsement by them. Any references to third-party content is to identify the corresponding services and shall be considered fair use under The CopyrightLaw.
