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Author: Mitra, Robin
Resulting in 2 citations.
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Hu, Jingchen Mitra, Robin Reiter, Jerome P. |
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Are Independent Parameter Draws Necessary for Multiple Imputation? The American Statistician 67,3 (2013): 143-149. Also: http://www.tandfonline.com/doi/full/10.1080/00031305.2013.821953#.UjISYneHp4N Cohort(s): Children of the NLSY79, NLSY79 Publisher: American Statistical Association Keyword(s): Breastfeeding; Missing Data/Imputation; Peabody Individual Achievement Test (PIAT- Math); Statistical Analysis In typical implementations of multiple imputation for missing data, analysts create m completed datasets based on approximately independent draws of imputation model parameters. We use theoretical arguments and simulations to show that, provided m is large, the use of independent draws is not necessary. In fact, appropriate use of dependent draws can improve precision relative to the use of independent draws. It also eliminates the sometimes difficult task of obtaining independent draws; for example, in fully Bayesian imputation models based on MCMC, analysts can avoid the search for a subsampling interval that ensures approximately independent draws for all parameters. We illustrate the use of dependent draws in multiple imputation with a study of the effect of breast feeding on children’s later cognitive abilities. |
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Bibliography Citation
Hu, Jingchen, Robin Mitra and Jerome P. Reiter. "Are Independent Parameter Draws Necessary for Multiple Imputation?" The American Statistician 67,3 (2013): 143-149.
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| 2. |
Mitra, Robin |
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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 Biometrical Journal published online (23 November 2022): DOI: 10.1002/bimj.202100284. Also: https://onlinelibrary.wiley.com/doi/10.1002/bimj.202100284 Cohort(s): Children of the NLSY79, NLSY79 Publisher: Wiley Online Keyword(s): Cognitive Development; Missing Data/Imputation; Modeling, Latent Class Analysis/Latent Transition Analysis; Peabody Individual Achievement Test (PIAT- Math); Propensity Scores Permission to reprint the abstract has not been received from the publisher. 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. |
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Bibliography Citation
Mitra, Robin. "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 published online (23 November 2022): DOI: 10.1002/bimj.202100284.
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