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

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

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.

Integrity signals

Registry

Crossref · 10.1007/s00127-016-1281-9

Cites

48 of 48 references verified0 citation errors · 0 not checkable

Statistics

Recomputing p-values needs the paper’s full text.

Claims the paper makes

We couldn’t retrieve this paper’s full text, so its claims and self-check weren’t run. The registry and citation checks still apply.

Claims other papers make about this one

Each is checked against this paper’s own text: does it say what the citing paper says it does?

Cites

Flagged first, then by citations

Cited by

Sorted by citations

Authors
VanderWeele, T. J., Jackson, J. W., & Li, S.
Affiliations
Harvard University (US)
Corresponding author
VanderWeele, Tyler J.
Venue
Social Psychiatry and Psychiatric Epidemiology
Published
2016-09-08
Volume
51
Issue
11
Pages
1457-1466
ISSN
  • 0933-7954 (ISSN-L)
  • 1433-9285
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
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

About these checks

Registry, retraction, Cites and statistics results are computed, not judged, with one exception: a printed reference that no index lists is read once by a model before it counts as a citation error. Claims are extracted by a model and kept only when their quote appears verbatim in the text; each cited source is then checked, with a model’s help, for whether it says what the claim relies on. Those verdicts can be wrong — read the claim’s quote and the cited source before relying on one.

Statistics and arithmetic are recomputed, not judged.

Confidence bands are shown only for fields calibrated on our annotated test set. None is calibrated yet, so no band is shown.

What counts as a claim ›How we measure confidence ›How we check this ›

Are you an author, or do you see a problem with this page? Request a review