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Quantile regression for nonignorable missing data with its application of analyzing electronic medical records

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  • Aiai Yu
  • Yujie Zhong
  • Xingdong Feng
  • Ying Wei

Abstract

Over the past decade, there has been growing enthusiasm for using electronic medical records (EMRs) for biomedical research. Quantile regression estimates distributional associations, providing unique insights into the intricacies and heterogeneity of the EMR data. However, the widespread nonignorable missing observations in EMR often obscure the true associations and challenge its potential for robust biomedical discoveries. We propose a novel method to estimate the covariate effects in the presence of nonignorable missing responses under quantile regression. This method imposes no parametric specifications on response distributions, which subtly uses implicit distributions induced by the corresponding quantile regression models. We show that the proposed estimator is consistent and asymptotically normal. We also provide an efficient algorithm to obtain the proposed estimate and a randomly weighted bootstrap approach for statistical inferences. Numerical studies, including an empirical analysis of real‐world EMR data, are used to assess the proposed method's finite‐sample performance compared to existing literature.

Suggested Citation

  • Aiai Yu & Yujie Zhong & Xingdong Feng & Ying Wei, 2023. "Quantile regression for nonignorable missing data with its application of analyzing electronic medical records," Biometrics, The International Biometric Society, vol. 79(3), pages 2036-2049, September.
    • Handle: RePEc:bla:biomet:v:79:y:2023:i:3:p:2036-2049
    DOI: 10.1111/biom.13723
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    References listed on IDEAS

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