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

Shiro Tanaka1,2, M Alan Brookhart2, Jason Fine3

  • 1Department of Clinical Biostatistics, Graduate School of Medicine, Kyoto University, Kyoto, Japan.

Statistics in Medicine
|July 4, 2023
PubMed
Summary
This summary is machine-generated.

Fenofibrate use in type 2 diabetes patients significantly reduced diabetic retinopathy progression in the initial 4 years. However, long-term efficacy beyond this period was not supported by this causal analysis.

Keywords:
g-estimationintercurrent eventinterval-censoringpseudo-valuetime-varying treatment effecttreatment-switching

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Area of Science:

  • Diabetology
  • Ophthalmology
  • Biostatistics

Background:

  • Randomized trials (FIELD, ACCORD) showed fenofibrate reduced diabetic retinopathy progression.
  • Previous analyses were complicated by treatment switching and interval-censored data.
  • Accurate causal effect estimation for long-term fibrate use in type 2 diabetes is challenging.

Purpose of the Study:

  • To address challenges in estimating causal effects of long-term fibrate use.
  • To propose novel statistical methods for time-varying treatment effects with interval-censored data.
  • To re-evaluate fenofibrate's efficacy in diabetic retinopathy progression using a cohort study.

Main Methods:

  • Utilized structural nested mean models (SNMMs) for time-varying treatment effects.
  • Developed pseudo-observation estimators for interval-censored data.
  • Employed nonparametric maximum likelihood estimator (MLE) and parametric piecewise exponential distribution for estimation.

Main Results:

  • Pseudo-observation estimators, particularly the nonparametric Wellner-Zhan estimator, performed well even with dependent interval-censoring.
  • Application to a diabetes cohort (8-year follow-up) indicated fenofibrate reduced diabetic retinopathy risk within the first 4 years.
  • Efficacy of fenofibrate in reducing diabetic retinopathy progression was not observed beyond 4 years.

Conclusions:

  • Novel statistical methods effectively estimate causal effects in the presence of interval-censoring and time-varying treatments.
  • Fenofibrate demonstrates short-term (4-year) benefit in reducing diabetic retinopathy progression in type 2 diabetes.
  • Long-term use of fenofibrate beyond 4 years did not show sustained efficacy for diabetic retinopathy in this cohort.