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Social Psychiatry and Psychiatric Epidemiology
Published in: Social Psychiatry and Psychiatric Epidemiology 11/2016

01-11-2016 | Invited Reviews

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

Authors: Tyler J. VanderWeele, John W. Jackson, Shanshan Li

Published in: Social Psychiatry and Psychiatric Epidemiology | Issue 11/2016

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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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Literature
1.
Robins JM (1986) A new approach to causal inference in mortality studies with sustained exposure period—application to control of the healthy worker survivor effect. Math Model 7:1393–1512CrossRef
2.
Pearl J (2009) Causality: models, reasoning, and inference, 2nd edn. Cambridge University Press, CambridgeCrossRef
3.
Robins JM (1999) Association, causation, and marginal structural models. Synthese 121:151–179CrossRef
4.
Robins JM, Hernán MA, Brumback B (2000) Marginal structural models and causal inference in epidemiology. Epidemiology 11:550–560CrossRefPubMed
5.
Robins JM, Hernán MA (2009) In: Fitzmaurice G, Davidian M, Verbeke G, Molenberghs G (eds) Estimation of the causal effects of time-varying exposures. Chapman and Hall, New York
6.
Koenig HG, King DE, Carson VB (2012) Handbook of religion and health, 2nd edn. Oxford University Press, Oxford
7.
Maselko J, Hayward RD, Hanlon A, Buka S, Meador K (2012) Religious service attendance and major depression: a case of reverse causality? Am J Epidemiol 175(6):576–583CrossRefPubMedPubMedCentral
8.
VanderWeele TJ (2013) Re: “Religious service attendance and major depression: a case of reverse causality?”. Am J Epidemiol 177(3):275–276CrossRefPubMed
9.
Li S, Okereke OI, Chang S-C, Kawachi I, VanderWeele TJ (2016) Religious service attendance and lower depression among women - a prospective cohort study. Ann Behav Med. doi:10.​1007/​s12160-016-9813-9
10.
Koegh R, Daniels R, VanderWeele TJ, Vansteelandt S Analysis of longitudinal studies: adjusting for time-dependent confounding using conventional methods. Am J Epidemiol
11.
Hernán MA, Robins JM (2016) Causal inference. Chapman Hall, New York
12.
Shrier I, Platt RW (2008) Reducing bias through directed acyclic graphs. BMC Med Res Methodol 8(1):1CrossRef
13.
Glymour MM, Weuve J, Berkman LF, Kawachi I, Robins JM (2005) When is baseline adjustment useful in analyses of change? An example with education and cognitive change. Am J Epidemiol 162(3):267–278CrossRefPubMed
14.
Danaei G, Tavakkoli M, Hernán MA (2012) Bias in observational studies of prevalent users: lessons for comparative effectiveness research from a meta-analysis of statins. Am J Epidemiol 175(4):250–262CrossRefPubMedPubMedCentral
15.
16.
17.
Rothman KJ, Greenland S, Lash TL (2008) Modern epidemiology, 3rd edn. Lippincott Williams and Wilkins, Philadelphia
18.
19.
Morgan SL, Winship C (2014) Counterfactuals and causal inference, 2nd edn. Cambridge University Press, CambridgeCrossRef
20.
Imbens G, Rubin DB (2015) Causal inference in statistics, social, and biomedical sciences: an introduction. Cambridge University Press, New York (in press) CrossRef
21.
VanderWeele TJ (2016) Explanation in causal inference: methods for mediation and interaction. Oxford University Press, New York
22.
Horvitz DG, Thompson DJ (1952) A generalization of sampling without replacement from a finite universe. J Am Stast Assoc 47:663–685CrossRef
23.
Rosenbaum PR, Rubin DB (1983) The central role of the propensity score in observational studies for causal effects. Biometrika 70:41–55CrossRef
24.
25.
26.
Hernán MA, Brumback B, Robins JM (2002) Estimating the causal effect of zidovudine on CD4 count with a marginal structural model for repeated measures. Stat Med 21:1689–1709CrossRefPubMed
27.
Hernán MA, Brumback B, Robins JM (2000) Marginal structural models to estimate the causal effect of zidovudine on the survival of HIV-positive men. Epidemiology 11:561–570CrossRefPubMed
28.
Robins JM (1999) Marginal structural models versus structural nested models as tools for causal inference. In: Halloran ME, Berry D (eds) Statistical models in epidemiology: the environment and clinical trials. Springer, NY, pp 95–134
29.
30.
VanderWeele TJ, Hawkley LC, Cacioppo JT (2012) On the reciprocal relationship between loneliness and subjective well-being. Am J Epidemiol 176:777–784CrossRefPubMedPubMedCentral
31.
Barber JS, Murphy SA, Verbitsky N (2004) Adjusting for time-varying confounding in survival analysis. Sociol Methodol 34:163–192CrossRef
32.
Bray BC, Almirall D, Zimmerman RS, Lynam D, Murphy SA (2006) Assessing the total effect of time-varying predictors in prevention research. Prev Sci 7:1–17CrossRefPubMedPubMedCentral
33.
Vansteelandt S, Sjolander A (2016) Revisiting g-estimation of the effect of a time-varying exposure subject to time-varying confounding. Epidemiol Methods. doi:10.​1515/​em-2015-0005
34.
Vansteelandt S (2009) Estimating direct effects in cohort and case-control studies. Epidemiology 20:851–860CrossRefPubMed
35.
Strawbridge WJ, Shema SJ, Cohen RD, Kaplan GA (2001) Religious attendance increases survival by improving and maintaining good health behaviors, mental health, and social relationships. Ann Behav Med 23(1):68–74CrossRefPubMed
36.
Van Voorhees BW, Paunesku D, Kuwabara SA et al (2008) Protective and vulnerability factors predicting new-onset depressive episode in a representative of US adolescents. J Adolesc Health 42(6):605–616CrossRefPubMed
37.
Norton MC, Singh A, Skoog I et al (2008) Church attendance and new episodes of major depression in a community study of older adults: the Cache County Study. J Gerontol B Psychol Sci Soc Sci 63(3):P129–P137CrossRefPubMedPubMedCentral
38.
Balbuena L, Baetz M, Bowen R (2013) Religious attendance, spirituality, and major depression in Canada: a 14-year follow-up study. Can J Psychiatry 58:225–232PubMed
39.
Li S, Stamfer M, Williams DR, VanderWeele TJ (2016) Association between religious service attendance and mortality among women. JAMA Intern Med 176(6):777–785CrossRefPubMed
40.
Schnall E, Wassertheil-Smoller S, Swencionis C et al (2010) The relationship between religion and cardiovascular outcomes and all-cause mortality in the Women’s Health Initiative Observational Study. Psychol Health 25(2):249–263CrossRefPubMed
41.
Lim C, Putnam RD (2010) Religion, social networks, and life satisfaction. Am Sociol Rev 75:914–933CrossRef
42.
Moerkerke B, Loeys T, Vansteelandt S (2015) Structural equation modeling versus marginal structural modeling for assessing mediation in the presence of posttreatment confounding. Psychol Methods 20(2):204CrossRefPubMed
43.
Marshall B, Galea S (2015) Formalizing the role of agent-based modelling in causal inference and epidemiology. Am J Epidemiol 181(2):92–99CrossRefPubMed
Metadata
Title
Causal inference and longitudinal data: a case study of religion and mental health
Authors
Tyler J. VanderWeele
John W. Jackson
Shanshan Li
Publication date
01-11-2016
Publisher
Springer Berlin Heidelberg
Published in
Social Psychiatry and Psychiatric Epidemiology / Issue 11/2016
Print ISSN: 0933-7954
Electronic ISSN: 1433-9285
DOI
https://doi.org/10.1007/s00127-016-1281-9

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