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Inverse probability weighting for clustered nonresponse

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  • C. J. Skinner
  • D'arrigo

Abstract

Correlated nonresponse within clusters arises in certain survey settings. It is often represented by a random effects model and assumed to be cluster-specific nonignorable, in the sense that survey and nonresponse outcomes are conditionally independent given cluster-level random effects. Two basic forms of inverse probability weights are considered: response propensity weights based on a marginal model, and weights based on predicted random effects. It is shown that both approaches can lead to biased estimation under cluster-specific nonignorable nonresponse, when the cluster sample sizes are small. We propose a new form of weighted estimator based upon conditional logistic regression, which can avoid this bias. An associated estimator of variance and an extension to observational studies with clustered treatment assignment are also described. Properties of the alternative estimators are illustrated in a small simulation study. Copyright 2011, Oxford University Press.

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  • C. J. Skinner & D'arrigo, 2011. "Inverse probability weighting for clustered nonresponse," Biometrika, Biometrika Trust, vol. 98(4), pages 953-966.
    • Handle: RePEc:oup:biomet:v:98:y:2011:i:4:p:953-966
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    File URL: http://hdl.handle.net/10.1093/biomet/asr058
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    Cited by:

    1. Youjin Lee & Trang Q. Nguyen & Elizabeth A. Stuart, 2021. "Partially pooled propensity score models for average treatment effect estimation with multilevel data," Journal of the Royal Statistical Society Series A, Royal Statistical Society, vol. 184(4), pages 1578-1598, October.
    2. Jouni Kuha & Myrsini Katsikatsou & Irini Moustaki, 2018. "Latent variable modelling with non‐ignorable item non‐response: multigroup response propensity models for cross‐national analysis," Journal of the Royal Statistical Society Series A, Royal Statistical Society, vol. 181(4), pages 1169-1192, October.
    3. Helene Boistard & Guillaume Chauvet & David Haziza, 2016. "Doubly Robust Inference for the Distribution Function in the Presence of Missing Survey Data," Scandinavian Journal of Statistics, Danish Society for Theoretical Statistics;Finnish Statistical Society;Norwegian Statistical Association;Swedish Statistical Association, vol. 43(3), pages 683-699, September.
    4. Brick J. Michael, 2013. "Unit Nonresponse and Weighting Adjustments: A Critical Review," Journal of Official Statistics, Sciendo, vol. 29(3), pages 329-353, June.
    5. Koltsova Anna A. & Starobinskaya Nadegda M. & Chekmarev Oleg P., 2018. "Art Market: Distinctive Features And Value Of Art Objects," Annals of marketing-mba, Department of Marketing, Marketing MBA (RSconsult), vol. 4, December.
    6. Li He & Yu-Bo Wang & William C. Bridges & Zhulin He & S. Megan Che, 2023. "Bayesian Framework for Causal Inference with Principal Stratification and Clusters," Statistics in Biosciences, Springer;International Chinese Statistical Association, vol. 15(1), pages 114-140, April.
    7. Hans Walter Steinhauer & Christian Aßmann & Sabine Zinn & Solange Goßmann & Susanne Rässler, 2015. "Sampling and Weighting Cohort Samples in Institutional Contexts," AStA Wirtschafts- und Sozialstatistisches Archiv, Springer;Deutsche Statistische Gesellschaft - German Statistical Society, vol. 9(2), pages 131-157, November.
    8. Plewis Ian & Shlomo Natalie, 2017. "Using Response Propensity Models to Improve the Quality of Response Data in Longitudinal Studies," Journal of Official Statistics, Sciendo, vol. 33(3), pages 753-779, September.

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