A Simulation Study on the Estimation of the Effect in the Hypothetical Scenario of No Use of Symptomatic Treatment in Trials for Disease-Modifying Agents for Alzheimer’s Disease

Abstract

In studying a potential disease-modifying treatment for Alzheimer’s Disease, the use of currently available symptomatic medicines—being proportional to the disease progression—can mask the disease-modifying effect. A solution in line with the Estimand framework is to adopt a hypothetical strategy for the intercurrent event “initiation of symptomatic treatment”. However, there is no consensus on reliable estimators for estimands adopting this strategy. To evaluate the performance of several candidate estimators, we have (i) designed clinically realistic data-generating mechanisms based on causal Directed Acyclic Graphs; (ii) selected potentially adequate estimators, amongst other, g-estimation methods usually employed to estimate “controlled direct effect” in mediation analysis; (iii) simulated 10,000 trials for six plausible scenarios and compared the performance of the estimators. The results of our simulations demonstrate that ignoring the intercurrent event introduces a bias compared to the true value of the target estimand. In contrast, g-estimation methods are unbiased, retain nominal power, and control the Type-I error rate at the intended level. Our results can be extrapolated, from a qualitative (absence or presence of bias) but not quantitative point of view (magnitude of the bias), to clinical scenarios with a similar underlying causal structure.