History-restricted marginal structural model and latent class growth analysis of treatment trajectories for a time-dependent outcome

Diop Awa (), Sirois Caroline (), Guertin Jason R. (), Schnitzer Mireille E. (), Brophy James M. (), Blais Claudia () and Talbot Denis ()
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Diop Awa: Département de médecine sociale et préventive, Université Laval, Centre de recherche du CHU de Québec – Université Laval, Axe santé des populations et pratiques optimales en santé, Québec, QC, Canada
Sirois Caroline: Faculté de pharmacie, Université Laval, Centre de recherche du CHU de Québec – Université Laval, Axe santé des populations et pratiques optimales en santé, Québec, QC, Canada
Guertin Jason R.: Tissue Engineering Laboratory (LOEX), Département de médecine sociale et préventive, Université Laval, Centre de recherche du CHU de Québec – Université Laval, Axe santé des populations et pratiques optimales en santé, Québec, QC, Canada
Schnitzer Mireille E.: Faculté de pharmacie et Département de médecine sociale et préventive, ESPUM, Department of Epidemiology, Biostatistics, and Occupational Health, Université de Montréal, McGill University, Montréal, QC, Canada
Brophy James M.: Hospital Center for Health Outcomes Research, McGill University, Montréal, QC, Canada
Blais Claudia: Institut national de santé publique du Québec (INSPQ), Québec, QC, Canada
Talbot Denis: Département de médecine sociale et préventive, Université Laval, Centre de recherche du CHU de Québec – Université Laval, Axe santé des populations et pratiques optimales en santé, Québec, QC, Canada

The International Journal of Biostatistics, 2024, vol. 20, issue 2, 467-490

Abstract: In previous work, we introduced a framework that combines latent class growth analysis (LCGA) with marginal structural models (LCGA-MSM). LCGA-MSM first summarizes the numerous time-varying treatment patterns into a few trajectory groups and then allows for a population-level causal interpretation of the group differences. However, the LCGA-MSM framework is not suitable when the outcome is time-dependent. In this study, we propose combining a nonparametric history-restricted marginal structural model (HRMSM) with LCGA. HRMSMs can be seen as an application of standard MSMs on multiple time intervals. To the best of our knowledge, we also present the first application of HRMSMs with a time-to-event outcome. It was previously noted that HRMSMs could pose interpretation problems in survival analysis when either targeting a hazard ratio or a survival curve. We propose a causal parameter that bypasses these interpretation challenges. We consider three different estimators of the parameters: inverse probability of treatment weighting (IPTW), g-computation, and a pooled longitudinal targeted maximum likelihood estimator (pooled LTMLE). We conduct simulation studies to measure the performance of the proposed LCGA-HRMSM. For all scenarios, we obtain unbiased estimates when using either g-computation or pooled LTMLE. IPTW produced estimates with slightly larger bias in some scenarios. Overall, all approaches have good coverage of the 95 % confidence interval. We applied our approach to a population of older Quebecers composed of 57,211 statin initiators and found that a greater adherence to statins was associated with a lower combined risk of cardiovascular disease or all-cause mortality.

Keywords: pooled LTMLE; g-computation; IPTW; history-restricted MSMs; survival analysis; cardiovascular disease (search for similar items in EconPapers)
Date: 2024
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DOI: 10.1515/ijb-2023-0116

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