Cited by 322Advanced Causal Inference TechniquesStatistical Methods and Bayesian InferenceReligion, Spirituality, and Psychology

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Causal inference and longitudinal data: a case study of religion and mental health

VanderWeele, Tyler J., Jackson, John W., Li, Shanshan·2016·Social Psychiatry and Psychiatric Epidemiology

DOI 10.1007/s00127-016-1281-9

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Abstract

Purpose We provide an introduction to causal inference with longitudinal data and discuss the complexities of analysis and interpretation when exposures can vary over time. Methods We consider what types of causal questions can be addressed with the standard regression-based analyses and what types of covariate control and control for the prior values of outcome and exposure must be made to reason about causal effects. We also consider newer classes of causal models, including marginal structural models, that can assess questions of the joint effects of time-varying exposures and can take into account feedback between the exposure and outcome over time. Such feedback renders cross-sectional data ineffective for drawing inferences about causation. Results The challenges are illustrated by analyses concerning potential effects of religious service attendance on depression, in which there may in fact be effects in both directions with service attendance preventing the subsequent depression, but depression itself leading to lower levels of the subsequent religious service attendance. Conclusions Longitudinal designs, with careful control for prior exposures, outcomes, and confounders, and suitable methodology, will strengthen research on mental health, religion and health, and in the biomedical and social sciences generally.

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Authors: VanderWeele, T. J., Jackson, J. W., & Li, S.
Affiliations: Harvard University (US)
Corresponding author: VanderWeele, Tyler J.
ORCID: VanderWeele, Tyler J.
Jackson, John W.
Li, Shanshan
Venue: Social Psychiatry and Psychiatric Epidemiology
Publisher: Springer Science and Business Media LLC
Published: 2016-09-08
Volume: 51
Issue: 11
Pages: 1457-1466
ISSN: 0933-7954 (ISSN-L)
1433-9285
DOI: 10.1007/s00127-016-1281-9
OpenAlex: W2510473071
Cited by: 322
References: 48
Citations by year: 2026: 16 · 2025: 28 · 2024: 38 · 2023: 80 · 2022: 40 · 2021: 34 · 2020: 37 · 2019: 18 · 2018: 18 · 2017: 10 · 2016: 3
FWCI: 2.31
Type: journal article
Language: en
Open access: closed
Licence: www.springer.com/tdm (tdm)
www.springer.com/tdm (vor)
Locations: Social Psychiatry and Psychiatric Epidemiology (publishedVersion)
PubMed (publishedVersion)
Funders: John Templeton Foundation (52125)
National Institute of Environmental Health Sciences (ES017876)
Topics: Physical Sciences › Mathematics › Statistics and Probability › Advanced Causal Inference Techniques
Physical Sciences › Mathematics › Statistics and Probability › Statistical Methods and Bayesian Inference
Social Sciences › Health › Religion, Spirituality, and Psychology
Keywords: causal inference, longitudinal data, time-varying exposure, marginal structural models, religious service attendance, depression, religion and mental health, confounding adjustment, causal effects
MeSH terms: Cross-Sectional Studies, Depressive Disorder, Humans, Mental Health, Religion, Research Design

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Causal inference and longitudinal data: a case study of religion and mental health

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