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- Title
Causal inference and longitudinal data: a case study of religion and mental health.
- Authors
VanderWeele, Tyler; Jackson, John; Li, Shanshan; VanderWeele, Tyler J; Jackson, John W
- Abstract
<bold>Purpose: </bold>We provide an introduction to causal inference with longitudinal data and discuss the complexities of analysis and interpretation when exposures can vary over time.<bold>Methods: </bold>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.<bold>Results: </bold>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.<bold>Conclusions: </bold>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.
- Subjects
MENTAL health; MENTAL health &; religion; MEDICAL sciences; SOCIAL sciences; CAUSAL models; LONGITUDINAL method; CROSS-sectional method; MENTAL depression; EXPERIMENTAL design; PRAYER
- Publication
Social Psychiatry & Psychiatric Epidemiology, 2016, Vol 51, Issue 11, p1457
- ISSN
0933-7954
- Publication type
journal article
- DOI
10.1007/s00127-016-1281-9