Causal effect estimation and dose adjustment in exposure-response relationship analysis

Abstract

Determining causal exposure effects is often a challenging task even with randomized clinical trials. Confounding factors may cause bias in: (1) the pharmacokinetic exposure-response relationship and (2) dose-response relationship when dose-adjustment depends on potential responses. Dose adjustment often happens in clinical trials either designed for therapeutic dose monitoring, or spontaneously due to, for example, adverse events. It makes causal effect inference difficult since it often relates to potential response. On the other hand, dose adjustment in some trials such as the randomized concentration controlled (RCC) trials are designed to reduce confounding bias in exposure-response relationship. We review different types of dose-adjustment mechanisms and their impact on causal effect estimation with a number of dose-exposure and exposure response models. Following the concept of sequential randomization and approaches for missing data analysis, we examine a number of approaches for causal effect estimation including the classical joint modeling based on joint likelihood functions and instrumental variable and control function methods. We explore simplified approaches for joint modeling with sequential randomization conditional on potentially confounded subject effects and alternatives to the joint modeling approaches. Performance of these approaches in typical practical scenarios was assessed with a simulation study.