CITIES: Clinical trials with intercurrent events simulator.

Although clinical trials are often designed with randomization and well-controlled protocols, complications will inevitably arise in the presence of intercurrent events (ICEs) such as treatment discontinuation. These can lead to missing outcome data and possibly confounding causal inference when the missingness is a function of a latent stratification of patients defined by intermediate outcomes. The pharmaceutical industry has been focused on developing new methods that can yield pertinent causal inferences in trials with ICEs. However, it is difficult to compare the properties of different methods developed in this endeavor as real-life clinical trial data cannot be easily shared to provide benchmark data sets. Furthermore, different methods consider distinct assumptions for the underlying data-generating mechanisms, and simulation studies often are customized to specific situations or methods. We develop a novel, general simulation model and corresponding Shiny application in R for clinical trials with ICEs, aptly named the Clinical Trials with Intercurrent Events Simulator (CITIES). It is formulated under the Rubin Causal Model where the considered treatment effects account for ICEs in clinical trials with repeated measures. CITIES facilitates the effective generation of data that resemble real-life clinical trials with respect to their reported summary statistics, without requiring the use of the original trial data. We illustrate the utility of CITIES via two case studies involving real-life clinical trials that demonstrate how CITIES provides a comprehensive tool for practitioners in the pharmaceutical industry to compare methods for the analysis of clinical trials with ICEs on identical, benchmark settings that resemble real-life trials.

A novel estimand to adjust for rescue treatment in randomized clinical trials.

The interpretation of randomized clinical trial results is often complicated by intercurrent events. For instance, rescue medication is sometimes given to patients in response to worsening of their disease, either in addition to the randomized treatment or in its place. The use of such medication complicates the interpretation of the intention-to-treat analysis. In view of this, we propose a novel estimand defined as the intention-to-treat effect that would have been observed, had patients on the active arm been switched to rescue medication if and only if they would have been switched when randomized to control. This enables us to disentangle the treatment effect from the effect of rescue medication on a patient's outcome, while tempering the strong extrapolations that are typically needed when inferring what the intention-to-treat effect would have been in the absence of rescue medication. We propose a novel inverse probability weighting method for estimating this effect in settings where the decision to initiate rescue medication is made at one prespecified time point. This estimator relies on specific untestable assumptions, in view of which we propose a sensitivity analysis. We use the method for the analysis of a clinical trial conducted by Janssen Pharmaceuticals, in which patients with type 2 diabetes mellitus can switch to rescue medication for ethical reasons. Monte Carlo simulations confirm that the proposed estimator is unbiased in moderate sample sizes.