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. Biom. J.. ISSN 0323-3847

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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.

Item Type:
Journal Article
Journal or Publication Title:
Biom. J.
Uncontrolled Keywords:
/dk/atira/pure/subjectarea/asjc/2700
Subjects:
ID Code:
180114
Deposited By:
Deposited On:
05 Dec 2022 12:55
Refereed?:
Yes
Published?:
Published
Last Modified:
14 Dec 2022 03:30