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A multiple robust propensity score method for longitudinal analysis with intermittent missing data

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  • Chixiang Chen
  • Biyi Shen
  • Aiyi Liu
  • Rongling Wu
  • Ming Wang

Abstract

Longitudinal data are very popular in practice, but they are often missing in either outcomes or time‐dependent risk factors, making them highly unbalanced and complex. Missing data may contain various missing patterns or mechanisms, and how to properly handle it for unbiased and valid inference still presents a significant challenge. Here, we propose a novel semiparametric framework for analyzing longitudinal data with both missing responses and covariates that are missing at random and intermittent, a general and widely encountered situation in observational studies. Within this framework, we consider multiple robust estimation procedures based on innovative calibrated propensity scores, which offers additional relaxation of the misspecification of missing data mechanisms and shows more satisfactory numerical performance. Also, the corresponding robust information criterion on consistent variable selection for our proposed model is developed based on empirical likelihood‐based methods. These advocated methods are evaluated in both theory and extensive simulation studies in a variety of situations, showing competing properties and advantages compared to the existing approaches. We illustrate the utility of our approach by analyzing the data from the HIV Epidemiology Research Study.

Suggested Citation

  • Chixiang Chen & Biyi Shen & Aiyi Liu & Rongling Wu & Ming Wang, 2021. "A multiple robust propensity score method for longitudinal analysis with intermittent missing data," Biometrics, The International Biometric Society, vol. 77(2), pages 519-532, June.
    • Handle: RePEc:bla:biomet:v:77:y:2021:i:2:p:519-532
    DOI: 10.1111/biom.13330
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    References listed on IDEAS

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    1. Baojiang Chen & Xiao-Hua Zhou, 2011. "Doubly Robust Estimates for Binary Longitudinal Data Analysis with Missing Response and Missing Covariates," Biometrics, The International Biometric Society, vol. 67(3), pages 830-842, September.
    2. Chen, Baojiang & Yi, Grace Y. & Cook, Richard J., 2010. "Weighted Generalized Estimating Functions for Longitudinal Response and Covariate Data That Are Missing at Random," Journal of the American Statistical Association, American Statistical Association, vol. 105(489), pages 336-353.
    3. Chung-Wei Shen & Yi-Hau Chen, 2012. "Model Selection for Generalized Estimating Equations Accommodating Dropout Missingness," Biometrics, The International Biometric Society, vol. 68(4), pages 1046-1054, December.
    4. Chixiang Chen & Biyi Shen & Lijun Zhang & Yuan Xue & Ming Wang, 2019. "Empirical‐likelihood‐based criteria for model selection on marginal analysis of longitudinal data with dropout missingness," Biometrics, The International Biometric Society, vol. 75(3), pages 950-965, September.
    5. Peisong Han & Lu Wang, 2013. "Estimation with missing data: beyond double robustness," Biometrika, Biometrika Trust, vol. 100(2), pages 417-430.
    6. Peisong Han, 2016. "Intrinsic efficiency and multiple robustness in longitudinal studies with drop-out," Biometrika, Biometrika Trust, vol. 103(3), pages 683-700.
    7. Huiming Lin & Bo Fu & Guoyou Qin & Zhongyi Zhu, 2017. "Doubly robust estimation of generalized partial linear models for longitudinal data with dropouts," Biometrics, The International Biometric Society, vol. 73(4), pages 1132-1139, December.
    8. Peisong Han, 2014. "Multiply Robust Estimation in Regression Analysis With Missing Data," Journal of the American Statistical Association, Taylor & Francis Journals, vol. 109(507), pages 1159-1173, September.
    9. Heejung Bang & James M. Robins, 2005. "Doubly Robust Estimation in Missing Data and Causal Inference Models," Biometrics, The International Biometric Society, vol. 61(4), pages 962-973, December.
    10. Sixia Chen & David Haziza, 2017. "Multiply robust imputation procedures for the treatment of item nonresponse in surveys," Biometrika, Biometrika Trust, vol. 104(2), pages 439-453.
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    Cited by:

    1. Chixiang Chen & Ming Wang & Shuo Chen, 2023. "An efficient data integration scheme for synthesizing information from multiple secondary datasets for the parameter inference of the main analysis," Biometrics, The International Biometric Society, vol. 79(4), pages 2947-2960, December.
    2. Jiachen Cai & Ning Zhang & Xin Zhou & Donna Spiegelman & Molin Wang, 2023. "Correcting for bias due to mismeasured exposure history in longitudinal studies with continuous outcomes," Biometrics, The International Biometric Society, vol. 79(4), pages 3739-3751, December.

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