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

School Of Mathematical Sciences

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https://doi.org/10.1002/bimj.202100284
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Keywords

RESEARCH ARTICLE, RESEARCH ARTICLES, latent class, missing data, multiple imputation, observational studies, propensity score

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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.
In: Biometrical Journal, Vol. 65, No. 3, 2100284, 31.03.2023.

Research output: Contribution to Journal/Magazine › Journal article › peer-review

Bibtex

@article{a9951b5ae6a341c9b97194a61cfc0c49,

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",

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.",

keywords = "RESEARCH ARTICLE, RESEARCH ARTICLES, latent class, missing data, multiple imputation, observational studies, propensity score",

author = "Robin Mitra",

year = "2023",

month = mar,

day = "31",

doi = "10.1002/bimj.202100284",

language = "English",

volume = "65",

journal = "Biometrical Journal",

issn = "0323-3847",

publisher = "Wiley-VCH Verlag",

number = "3",

}

RIS

TY - JOUR

T1 - 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

AU - Mitra, Robin

PY - 2023/3/31

Y1 - 2023/3/31

N2 - 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.

AB - 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.

KW - RESEARCH ARTICLE

KW - RESEARCH ARTICLES

KW - latent class

KW - missing data

KW - multiple imputation

KW - observational studies

KW - propensity score

U2 - 10.1002/bimj.202100284

DO - 10.1002/bimj.202100284

M3 - Journal article

VL - 65

JO - Biometrical Journal

JF - Biometrical Journal

SN - 0323-3847

IS - 3

M1 - 2100284

ER -

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