Confounding in longitudinal studies in addiction treatment research

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<p><b>Background:</b> The effectiveness of treatment for people with substance use disorders is usually examined using longitudinal cohorts. In these studies, treatment is often considered as a time-varying exposure. The aim of this commentary is to examine confounding in this context, when the confounding variable is time-invariant and when it is time-varying.</p> <p><b>Method:</b> Types of confounding are described with examples and illustrated using path diagrams. Simulations are used to demonstrate the direction of confounding bias and the extent that it is accounted for using standard regression adjustment techniques.</p> <p><b>Results:</b> When the confounding variable is time invariant or time varying and not influenced by prior treatment, then standard adjustment techniques are adequate to control for confounding bias, provided that in the latter scenario the time-varying form of the variable is used. When the confounder is time varying and affected by prior treatment status (i.e. it is a mediator of treatment), then standard methods of adjustment result in inconsistency.</p> <p><b>Conclusions:</b> In longitudinal cohorts where treatment exposure is time varying, confounding is an issue which should be considered, even if treatment exposure is initially randomized. In these studies, standard methods of adjustment may result be inadequate, even when all confounders have been identified. This occurs when the confounder is also a mediator of treatment. This is a likely scenario in many studies in addiction.</p>
Date made available22 Dec 2016
Publisherfigshare

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