ArticleStatistics in medicine2018
Joint mixed-effects models for causal inference with longitudinal data.
Article in Statistics in medicine, 2018. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 22 papers.
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Who cites it
22 citing papers in PubMed.
- A Doubly Robust Approach to Evaluating the Impact of Hospital Length of Stay on Post-Fracture Recovery in Older Adults With Cognitive Impairment.Statistics in medicine · 2026Article
- Bayesian Sensitivity Analysis for Causal Estimation With Time-Varying Unmeasured Confounding.Statistics in medicine · 2026Article
- Standardized survival probabilities and contrasts between hierarchical units in multilevel survival models.BMC medical research methodology · 2026Article
- Joint mixed-effects models for causal inference in clustered network-based observational studies.Statistical methods in medical research · 2026Article
- Long-term memory effects of an incremental blood pressure intervention in a mortal cohort.Biometrics · 2026Article
- Longitudinal impact of different treatment sequences of second-generation antipsychotics on metabolic outcomes: a study using targeted maximum likelihood estimation.Psychological medicine · 2025Article
- Simulating hierarchical data to assess the utility of ecological versus multilevel analyses in obtaining individual-level causal effects.BMC medical research methodology · 2025Article
- Causal Inference in Presence of Intra-Patient Correlation due to Repeated Measurements of Exposure and Outcome in Longitudinal Settings.Statistics in medicine · 2025Article
- Assessing the impact of insulin resistance trajectories on cardiovascular disease risk using longitudinal targeted maximum likelihood estimation.Cardiovascular diabetology · 2025Article
- How to develop causal directed acyclic graphs for observational health research: a scoping review.Health psychology review · 2025Article
- Joint modelling of longitudinal data: a scoping review of methodology and applications for non-time to event data.BMC medical research methodology · 2025Article
- Nonlinear mixed-effects modeling as a method for causal inference to predict exposures under desired within-subject dose titration schemes.CPT: pharmacometrics & systems pharmacology · 2025Article
- A Bayesian latent class approach to causal inference with longitudinal data.Statistical methods in medical research · 2025Article
- Article
- Analyzing risk factors for post-acute recovery in older adults with Alzheimer's disease and related dementia: A new semi-parametric model for large-scale medicare claims.Statistics in medicine · 2024Article
- A Bayesian semiparametric approach for inference on the population partly conditional mean from longitudinal data with dropout.Biostatistics (Oxford, England) · 2023Article
- Bayesian semi-parametric G-computation for causal inference in a cohort study with MNAR dropout and death.Journal of the Royal Statistical Society. Series C, Applied statistics · 2021Article
- Regional variations in medical trainee diet and nutrition counseling competencies: Machine learning-augmented propensity score analysis of a prospective multi-site cohort study.Medical science educator · 2020Article
- Linear mixed models with endogenous covariates: modeling sequential treatment effects with application to a mobile health study.Statistical science : a review journal of the Institute of Mathematical Statistics · 2020Article
- Joint modeling of concurrent binary outcomes in a longitudinal observational study using inverse probability of treatment weighting for treatment effect estimation.Annals of epidemiology · 2019Article
Corrections and comments
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Authors and funding
2 authors.
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Abstract
Causal inference with observational longitudinal data and time-varying exposures is complicated due to the potential for time-dependent confounding and unmeasured confounding. Most causal inference methods that handle time-dependent confounding rely on either the assumption of no unmeasured confounders or the availability of an unconfounded variable that is associated with the exposure (eg, an instrumental variable). Furthermore, when data are incomplete, validity of many methods often depends on the assumption of missing at random. We propose an approach that combines a parametric joint mixed-effects model for the study outcome and the exposure with g-computation to identify and estimate causal effects in the presence of time-dependent confounding and unmeasured confounding. G-computation can estimate participant-specific or population-average causal effects using parameters of the joint model. The joint model is a type of shared parameter model where the outcome and exposure-selection models share common random effect(s). We also extend the joint model to handle missing data and truncation by death when missingness is possibly not at random. We evaluate the performance of the proposed method using simulation studies and compare the method to both linear mixed- and fixed-effects models combined with g-computation as well as to targeted maximum likelihood estimation. We apply the method to an epidemiologic study of vitamin D and depressive symptoms in older adults and include code using SAS PROC NLMIXED software to enhance the accessibility of the method to applied researchers.
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Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.