Intervention treatment distributions that depend on the observed treatment process and model double robustness in causal survival analysis. (March 2023)

• Record Type: Journal Article
• Authors: Wen, Lan; Marcus, Julia L.; Young, Jessica G.
• Is Part Of: Statistical methods in medical research. Volume 32:Number 3(2023)
• Page Start: 509
• Page End: 523
• Publication Date: 2023-03
• Subjects: Causal inference -- double robustness -- estimating equations -- observational study -- stochastic treatment strategies; Medicine -- Research -- Statistical methods -- Periodicals; Research -- Periodicals; Review Literature -- Periodicals; Statistics -- methods -- Periodicals; Médecine -- Recherche -- Méthodes statistiques -- Périodiques; 610.727
• DOI: 10.1177/09622802221146311
• Languages: English
• ISSNs: 0962-2802

Abstract

The generalized g-formula can be used to estimate the probability of survival under a sustained treatment strategy. When treatment strategies are deterministic, estimators derived from the so-called efficient influence function (EIF) for the g-formula will be doubly robust to model misspecification. In recent years, several practical applications have motivated estimation of the g-formula under non-deterministic treatment strategies where treatment assignment at each time point depends on the observed treatment process. In this case, EIF-based estimators may or may not be doubly robust. In this paper, we provide sufficient conditions to ensure the existence of doubly robust estimators for intervention treatment distributions that depend on the observed treatment process for point treatment interventions and give a class of intervention treatment distributions dependent on the observed treatment process that guarantee model doubly and multiply robust estimators in longitudinal settings. Motivated by an application to pre-exposure prophylaxis (PrEP) initiation studies, we propose a new treatment intervention dependent on the observed treatment process. We show there exist (1) estimators that are doubly and multiply robust to model misspecification and (2) estimators that when used with machine learning algorithms can attain fast convergence rates for our proposed intervention. Finally, we explore the finite sample performance of our estimators via simulation studies.