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A latent class model to multiply impute missing treatment indicators in observational studies when inferences of the treatment effect are made using propensity score matching

Mitra, Robin; (2022) A latent class model to multiply impute missing treatment indicators in observational studies when inferences of the treatment effect are made using propensity score matching. Biometrical Journal 10.1002/bimj.202100284. (In press). Green open access

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Abstract

Analysts often estimate treatment effects in observational studies using propensity score matching techniques. When there are missing covariate values, analysts can multiply impute the missing data to create m completed data sets. Analysts can then estimate propensity scores on each of the completed data sets, and use these to estimate treatment effects. However, there has been relatively little attention on developing imputation models to deal with the additional problem of missing treatment indicators, perhaps due to the consequences of generating implausible imputations. However, simply ignoring the missing treatment values, akin to a complete case analysis, could also lead to problems when estimating treatment effects. We propose a latent class model to multiply impute missing treatment indicators. We illustrate its performance through simulations and with data taken from a study on determinants of children's cognitive development. This approach is seen to obtain treatment effect estimates closer to the true treatment effect than when employing conventional imputation procedures as well as compared to a complete case analysis.

Type: Article
Title: A latent class model to multiply impute missing treatment indicators in observational studies when inferences of the treatment effect are made using propensity score matching
Location: Germany
Open access status: An open access version is available from UCL Discovery
DOI: 10.1002/bimj.202100284
Publisher version: https://doi.org/10.1002/bimj.202100284
Language: English
Additional information: © 2022 The Authors. Biometrical Journal published by Wiley-VCH GmbH. This is an open access article under the terms of the Creative Commons Attribution License (http://creativecommons.org/licenses/by/4.0/).
Keywords: latent class, missing data, multiple imputation, observational studies, propensity score
UCL classification: UCL
UCL > Provost and Vice Provost Offices > UCL BEAMS
UCL > Provost and Vice Provost Offices > UCL BEAMS > Faculty of Maths and Physical Sciences
UCL > Provost and Vice Provost Offices > UCL BEAMS > Faculty of Maths and Physical Sciences > Dept of Statistical Science
URI: https://discovery.ucl.ac.uk/id/eprint/10161171
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