Analysis of longitudinal studies with repeated outcome measures: adjusting for time-dependent confounding using conventional methods

Keogh, Ruth H., Daniel, Rhian M. ORCID: https://orcid.org/0000-0001-5649-9320, Vanderweele, Tyler J. and Vansteelandt, Stijn 2018. Analysis of longitudinal studies with repeated outcome measures: adjusting for time-dependent confounding using conventional methods. American Journal of Epidemiology 187 (5) , pp. 1085-1092. 10.1093/aje/kwx311

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Abstract

Estimation of causal effects of time-varying exposures using longitudinal data is a common problem in epidemiology. When there are time-varying confounders, which may include past outcomes, affected by prior exposure, standard regression methods can lead to bias. Methods such as inverse probability weighted estimation of marginal structural models have been developed to address this problem. However, in this paper we show how standard regression methods can be used, even in the presence of time-dependent confounding, to estimate the total effect of an exposure on a subsequent outcome by controlling appropriately for prior exposures, outcomes and time-varying covariates. We refer to the resulting estimation approach as sequential conditional mean models (SCMM), which can be fitted using generalised estimating equations. We outline this approach and describe how including propensity score adjustment is advantageous. We compare the causal effects being estimated using SCMMs and marginal structural models, and compare the two approaches using simulations. SCMMs enable more precise inferences, with greater robustness against model misspecification via propensity score adjustment, and easily accommodate continuous exposures and interactions. A new test for direct effects of past exposures on a subsequent outcome is described.

Item Type:	Article
Date Type:	Publication
Status:	Published
Schools:	Medicine
Subjects:	R Medicine > R Medicine (General)
Additional Information:	This is an open access article distributed under the terms of the Creative Commons CC BY license, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.
Publisher:	Oxford University Press
ISSN:	0002-9262
Date of First Compliant Deposit:	2 November 2017
Date of Acceptance:	25 August 2017
Last Modified:	07 Nov 2023 03:06
URI:	https://orca.cardiff.ac.uk/id/eprint/106071

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