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Logistic regression with missing covariates—Parameter estimation, model selection and prediction within a joint-modeling framework

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  • Jiang, Wei
  • Josse, Julie
  • Lavielle, Marc

Abstract

Logistic regression is a common classification method in supervised learning. Surprisingly, there are very few solutions for performing logistic regression with missing values in the covariates. A complete approach based on a stochastic approximation version of the EM algorithm is proposed in order to perform statistical inference with missing values, including the estimation of the parameters and their variance, derivation of confidence intervals, and also a model selection procedure. The problem of prediction for new observations on a test set with missing covariate data is also tackled. Supported by a simulation study in which the method is compared to previous ones, it has proved to be computationally efficient, and has good coverage and variable selection properties. The approach is then illustrated on a dataset of severely traumatized patients from Paris hospitals by predicting the occurrence of hemorrhagic shock, a leading cause of early preventable death in severe trauma cases. The aim is to improve the current red flag procedure, a binary alert identifying patients with a high risk of severe hemorrhage. The method is implemented in the R package misaem.

Suggested Citation

  • Jiang, Wei & Josse, Julie & Lavielle, Marc, 2020. "Logistic regression with missing covariates—Parameter estimation, model selection and prediction within a joint-modeling framework," Computational Statistics & Data Analysis, Elsevier, vol. 145(C).
    • Handle: RePEc:eee:csdana:v:145:y:2020:i:c:s0167947319302622
    DOI: 10.1016/j.csda.2019.106907
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    References listed on IDEAS

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    1. Joseph G. Ibrahim & Ming-Hui Chen & Stuart R. Lipsitz & Amy H. Herring, 2005. "Missing-Data Methods for Generalized Linear Models: A Comparative Review," Journal of the American Statistical Association, American Statistical Association, vol. 100, pages 332-346, March.
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    6. Josse, Julie & Husson, François, 2016. "missMDA: A Package for Handling Missing Values in Multivariate Data Analysis," Journal of Statistical Software, Foundation for Open Access Statistics, vol. 70(i01).
    7. W. R. Gilks & P. Wild, 1992. "Adaptive Rejection Sampling for Gibbs Sampling," Journal of the Royal Statistical Society Series C, Royal Statistical Society, vol. 41(2), pages 337-348, June.
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    Cited by:

    1. Shen-Ming Lee & Truong-Nhat Le & Phuoc-Loc Tran & Chin-Shang Li, 2023. "Estimation of logistic regression with covariates missing separately or simultaneously via multiple imputation methods," Computational Statistics, Springer, vol. 38(2), pages 899-934, June.
    2. Daniele Bottigliengo & Giulia Lorenzoni & Honoria Ocagli & Matteo Martinato & Paola Berchialla & Dario Gregori, 2021. "Propensity Score Analysis with Partially Observed Baseline Covariates: A Practical Comparison of Methods for Handling Missing Data," IJERPH, MDPI, vol. 18(13), pages 1-17, June.

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