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G-estimation of structural nested mean models for interval-censored data using pseudo-observations.
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- Author(s): Tanaka S;Tanaka S;Tanaka S; Brookhart MA; Brookhart MA; Fine J; Fine J
- Source:
Statistics in medicine [Stat Med] 2023 Sep 20; Vol. 42 (21), pp. 3877-3891. Date of Electronic Publication: 2023 Jul 04.- Publication Type:
Randomized Controlled Trial; Journal Article; Research Support, Non-U.S. Gov't- Language:
English - Source:
- Additional Information
- Source: Publisher: Wiley Country of Publication: England NLM ID: 8215016 Publication Model: Print-Electronic Cited Medium: Internet ISSN: 1097-0258 (Electronic) Linking ISSN: 02776715 NLM ISO Abbreviation: Stat Med Subsets: MEDLINE
- Publication Information: Original Publication: Chichester ; New York : Wiley, c1982-
- Subject Terms:
- 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.
(© 2023 The Authors. Statistics in Medicine published by John Wiley & Sons Ltd.) - References: Keech AC, Mitchell P, Summanen PA, et al. Effect of fenofibrate on the need for laser treatment for diabetic retinopathy (FIELD study): a randomised controlled trial. Lancet. 2007;370:1687-1697.
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Vansteelandt S, Sjölander A. Revisiting g-estimation of the effect of a time-varying exposure subject to time-varying confounding. Epidemiol Methods. 2016;5:37-56. - Contributed Indexing: Keywords: g-estimation; intercurrent event; interval-censoring; pseudo-value; time-varying treatment effect; treatment-switching
- Accession Number: U202363UOS (Fenofibrate)
- Publication Date: Date Created: 20230704 Date Completed: 20230821 Latest Revision: 20230913
- Publication Date: 20231215
- Accession Number: 10.1002/sim.9838
- Accession Number: 37402505
- Source:
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