A tutorial comparing different covariate balancing methods with an application evaluating the causal effects of substance use treatment programs for adolescents

Markoulidakis, Andreas, Taiyari, Khadijeh, Holmans, Peter ORCID: https://orcid.org/0000-0003-0870-9412, Pallmann, Philip ORCID: https://orcid.org/0000-0001-8274-9696, Busse, Monica ORCID: https://orcid.org/0000-0002-5331-5909, Godley, Mark D. and Griffin, Beth Ann 2023. A tutorial comparing different covariate balancing methods with an application evaluating the causal effects of substance use treatment programs for adolescents. Health Services and Outcomes Research Methodology 23 , pp. 115-148. 10.1007/s10742-022-00280-0

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Abstract

Randomized controlled trials are the gold standard for measuring causal effects. However, they are often not always feasible, and causal treatment effects must be estimated from observational data. Observational studies do not allow robust conclusions about causal relationships unless statistical techniques account for the imbalance of pretreatment confounders across groups and key assumptions hold. Propensity score and balance weighting (PSBW) are useful techniques that aim to reduce the observed imbalances between treatment groups by weighting the groups to look alike on the observed confounders. Notably, there are many methods available to estimate PSBW. However, it is unclear a priori which will achieve the best trade-off between covariate balance and effective sample size for a given application. Moreover, it is critical to assess the validity of key assumptions required for robust estimation of the needed treatment effects, including the overlap and no unmeasured confounding assumptions. We present a step-by-step guide to the use of PSBW for estimation of causal treatment effects that includes steps on how to evaluate overlap before the analysis, obtain estimates of PSBW using multiple methods and select the optimal one, check for covariate balance on multiple metrics, and assess sensitivity of findings (both the estimated treatment effect and statistical significance) to unobserved confounding. We illustrate the key steps using a case study examining the relative effectiveness of substance use treatment programs and provide a user-friendly Shiny application that can implement the proposed steps for any application with binary treatments.

Item Type:	Article
Date Type:	Publication
Status:	Published
Schools:	Medicine MRC Centre for Neuropsychiatric Genetics and Genomics (CNGG) Centre for Trials Research (CNTRR)
Additional Information:	This article is licensed under a Creative Commons Attribution 4.0 International License, which permits use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons licence, and indicate if changes were made.
Publisher:	Springer
ISSN:	1387-3741
Date of First Compliant Deposit:	8 June 2022
Date of Acceptance:	14 May 2022
Last Modified:	30 May 2023 17:50
URI:	https://orca.cardiff.ac.uk/id/eprint/150304

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