ArticleAnnals of epidemiology2019
Joint modeling of concurrent binary outcomes in a longitudinal observational study using inverse probability of treatment weighting for treatment effect estimation.
Article in Annals of epidemiology, 2019. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 7 papers.
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Who cites it
7 citing papers in PubMed.
- Community-level disaster exposure and attrition in a longitudinal cohort study: Implications for aging research.Annals of epidemiology · 2026Article
- Causal Inference in Presence of Intra-Patient Correlation due to Repeated Measurements of Exposure and Outcome in Longitudinal Settings.Statistics in medicine · 2025Article
- Joint modelling of longitudinal data: a scoping review of methodology and applications for non-time to event data.BMC medical research methodology · 2025Article
- Derivation of a multivariate longitudinal causal effects model.Journal of applied statistics · 2025Article
- A Joint Model for Disability, Self-Rated Health, and Mortality Among Medicare Beneficiaries-Differences by Chronic Disease and Race/Ethnicity.Journal of aging and health · 2024Article
- CRcoder: An Interactive Web Application and SAS Macro to Support Personalized Clinical Decisions.The Permanente journal · 2020Article
- Personalized and typical concurrent risk of limitations in social activity and mobility in older persons with multiple chronic conditions and polypharmacy.Annals of epidemiology · 2019Article
Corrections and comments
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Authors and funding
4 authors.
Funding
Abstract
purposeCorrelated healthcare utilization outcomes may be encoded as binary outcomes in epidemiologic studies. We demonstrate how to account for correlation between concurrent binary outcomes and confounding by person characteristics when estimating a treatment effect in observational studies.
methodsWe present a joint shared-parameter model, weighted by inverse probability of treatment weights (IPTW) to account for confounding. The model is evaluated in a simulation study that emulates the Medical Expenditure Panel Survey data and compared with a covariate-adjusted joint model and with separate outcome models (IPTW weighted and covariate adjusted).
resultsFor the IPTW-weighted joint model, relative bias in the estimated treatment effect on outcome 1 ranged from -0.057 to -0.033 and outcome 2 from -0.077 to -0.043. For the covariate-adjusted joint model, relative bias ranged from -0.010 to -0.083 for outcome 1 and from -0.087 to -0.110 for outcome 2. The covariate-adjusted joint model estimated the effect more closely than the covariate-adjusted separate model. The IPTW-weighted joint model estimated the effect more closely for outcome 1.
conclusionsThe IPTW-weighted joint model handles correlation between binary outcomes, adjusts for confounding, and estimates the treatment effect accurately in observational studies. We illustrate the contribution of person-specific effects in estimating personalized risk.
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