Inverse probability weighting for clustered nonresponse

C. J. Skinner and D'arrigo

Biometrika, 2011, vol. 98, issue 4, 953-966

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.

Date: 2011
References: Add references at CitEc
Citations: View citations in EconPapers (9)

Downloads: (external link)
http://hdl.handle.net/10.1093/biomet/asr058 (application/pdf)
Access to full text is restricted to subscribers.

Related works:
This item may be available elsewhere in EconPapers: Search for items with the same title.

Export reference: BibTeX RIS (EndNote, ProCite, RefMan) HTML/Text

Persistent link: https://EconPapers.repec.org/RePEc:oup:biomet:v:98:y:2011:i:4:p:953-966

Ordering information: This journal article can be ordered from
https://academic.oup.com/journals

Access Statistics for this article

Biometrika is currently edited by Paul Fearnhead

More articles in Biometrika from Biometrika Trust Oxford University Press, Great Clarendon Street, Oxford OX2 6DP, UK.
Bibliographic data for series maintained by Oxford University Press ().