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Itemwise conditionally independent nonresponse modelling for incomplete multivariate data

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  • Mauricio Sadinle
  • Jerome P. Reiter

Abstract

SUMMARY We introduce a nonresponse mechanism for multivariate missing data in which each study variable and its nonresponse indicator are conditionally independent given the remaining variables and their nonresponse indicators. This is a nonignorable missingness mechanism, in that nonresponse for any item can depend on values of other items that are themselves missing. We show that under this itemwise conditionally independent nonresponse assumption, one can define and identify nonparametric saturated classes of joint multivariate models for the study variables and their missingness indicators. We also show how to perform sensitivity analysis with respect to violations of the conditional independence assumptions encoded by this missingness mechanism. We illustrate the proposed modelling approach with data analyses.

Suggested Citation

  • Mauricio Sadinle & Jerome P. Reiter, 2017. "Itemwise conditionally independent nonresponse modelling for incomplete multivariate data," Biometrika, Biometrika Trust, vol. 104(1), pages 207-220.
    • Handle: RePEc:oup:biomet:v:104:y:2017:i:1:p:207-220.
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    File URL: http://hdl.handle.net/10.1093/biomet/asw063
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    1. Li, C. & Sun, M. & Wang, Y. & Luo, L. & Yu, M. & Zhang, Y. & Wang, H. & Shi, P. & Chen, Z. & Wang, J. & Lu, Y. & Li, Q. & Wang, X. & Bi, Z. & Fan, M. & Fu, L. & Yu, J. & Hao, M., 2016. "The centers for disease control and prevention system in China: Trends from 2002-2012," American Journal of Public Health, American Public Health Association, vol. 106(12), pages 2093-2102.
    2. Geert Molenberghs & Michael G. Kenward & Els Goetghebeur, 2001. "Sensitivity analysis for incomplete contingency tables: the Slovenian plebiscite case," Journal of the Royal Statistical Society Series C, Royal Statistical Society, vol. 50(1), pages 15-29.
    3. Stasny, Elizabeth A, 1988. "Modeling Nonignorable Nonresponse in Categorical Panel Data with an Example in Estimating Gross Labor-Force Flows," Journal of Business & Economic Statistics, American Statistical Association, vol. 6(2), pages 207-219, April.
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    Cited by:

    1. Heng Chen & Daniel F. Heitjan, 2022. "Analysis of local sensitivity to nonignorability with missing outcomes and predictors," Biometrics, The International Biometric Society, vol. 78(4), pages 1342-1352, December.
    2. Olanrewaju Akande & Gabriel Madson & D. Sunshine Hillygus & Jerome P. Reiter, 2021. "Leveraging auxiliary information on marginal distributions in nonignorable models for item and unit nonresponse," Journal of the Royal Statistical Society Series A, Royal Statistical Society, vol. 184(2), pages 643-662, April.
    3. Daniel O. Scharfstein & Jon Steingrimsson & Aidan McDermott & Chenguang Wang & Souvik Ray & Aimee Campbell & Edward Nunes & Abigail Matthews, 2022. "Global sensitivity analysis of randomized trials with nonmonotone missing binary outcomes: Application to studies of substance use disorders," Biometrics, The International Biometric Society, vol. 78(2), pages 649-659, June.
    4. Yilin Li & Wang Miao & Ilya Shpitser & Eric J. Tchetgen Tchetgen, 2023. "A self‐censoring model for multivariate nonignorable nonmonotone missing data," Biometrics, The International Biometric Society, vol. 79(4), pages 3203-3214, December.

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