G‐estimation of structural nested mean models for interval‐censored data using pseudo‐observations

Abstract

Two large‐scale randomized clinical trials compared fenofibrate and placebo in diabetic patients with pre‐existing retinopathy (FIELD study) or risk factors (ACCORD trial) on an intention‐to‐treat basis and reported a significant reduction in the progression of diabetic retinopathy in the fenofibrate arms. However, their analyses involved complications due to intercurrent events, that is, treatment‐switching and interval‐censoring. This article addresses these problems involved in estimation of causal effects of long‐term use of fibrates in a cohort study that followed patients with type 2 diabetes for 8 years. We propose structural nested mean models (SNMMs) of time‐varying treatment effects and pseudo‐observation estimators for interval‐censored data. The first estimator for SNMMs uses a nonparametric maximum likelihood estimator (MLE) as a pseudo‐observation, while the second estimator is based on MLE under a parametric piecewise exponential distribution. Through numerical studies with real and simulated datasets, the pseudo‐observations estimators of causal effects using the nonparametric Wellner–Zhan estimator perform well even under dependent interval‐censoring. Its application to the diabetes study revealed that the use of fibrates in the first 4 years reduced the risk of diabetic retinopathy but did not support its efficacy beyond 4 years.