Evidence map›Paper›PMID 27631394›Full record

ReviewSocial psychiatry and psychiatric epidemiology2016

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

Tyler J VanderWeele, John W Jackson, Shanshan Li

Abstract readReview
PubMed Publisher
In one paragraph

Review in Social psychiatry and psychiatric epidemiology, 2016. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 121 papers, 4 of them syntheses that pooled it.

0numbers the graph read from it
0cells of the map it votes in
121citing papers in PubMed, 4 pooled it
–field-weighted citation impact
1 · What the graph read from it

What it found

Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.

The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.

2 · The registry

The trial behind it

Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.

Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.

3 · Its place in the literature

Who cites it

121 citing papers in PubMed, 4 syntheses or guidelines pooled it.

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  3. Marginal structural models for multilevel clustered data.Biostatistics (Oxford, England) · 2022
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61 more citing papers are in PubMed but not listed here.

4 · The record

Corrections and comments

5 · Who and what money

Authors and funding

3 authors.

Tyler J VanderWeeleDepartment of Epidemiology, Harvard Chan School of Public Health, 677 Huntington Avenue, Boston, MA, 02115, USA. tvanderw@hsph.harvard.edu.ORCID 0000-0002-6112-0239
John W JacksonDepartment of Epidemiology, Harvard Chan School of Public Health, 677 Huntington Avenue, Boston, MA, 02115, USA.
Shanshan LiDepartment of Epidemiology, Harvard Chan School of Public Health, 677 Huntington Avenue, Boston, MA, 02115, USA.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

purposeWe provide an introduction to causal inference with longitudinal data and discuss the complexities of analysis and interpretation when exposures can vary over time.

methodsWe 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.

resultsThe 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.

conclusionsLongitudinal 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.

Indexed as

Mental HealthReligionCross-Sectional StudiesDepressive DisorderHumansResearch DesignCausal inferenceConfoundingLongitudinal dataMarginal structural modelsReligion

Identifiers

PMID27631394

What OpenQuestion holds

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Registered trials

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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.