In clinical development, it is useful to characterize the causal relationship between individual drug concentrations and clinical outcomes in large phase III trials of new therapeutic agents because it can provide insights on whether increasing the currently administered drug dose may lead to better outcomes. However, estimating causal effects of drug concentration is complicated by the fact that drug concentration is a continuous measure and it is usually influenced by patient-level prognostic characteristics such as body weight and sex. In this article, we compare two approaches to estimate causal effects of continuous point exposures on time-to-event outcomes: (a) outcome regression (OR) and (b) weighting. In particular, we make the first direct comparison of the balancing weights, inverse probability weighting and OR methods for estimating the effects of continuous exposures on time-to-event outcomes in simulations and demonstrate that these methods can exhibit markedly different behaviours that subsequently lead to a change in the conclusions. To improve weighted exposure effect estimators, we also propose a new simple-to-apply diagnostic to detect when such estimators might be subject to severe bias, and demonstrate its effectiveness in simulations. Finally, we apply these methods to an example of multiple sclerosis drug development by providing causal effect estimates of average ocrelizumab concentrations on time-to-event disability progression outcomes.
Keywords: Confounding bias; exposure–response model; hazard ratio; outcome regression; selection bias; weighting methods.