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Please use this identifier to cite or link to this item: http://hdl.handle.net/1942/397

Title: Testing for bias in weighted estimating equations
Authors: Lipsitz, Stuart
Parzen, Michael
Molenberghs, Geert
Ibrahim, Joseph G.
Keywords: Categorical data
Longitudinal data
Missing data
Issue Date: 2001
Publisher: OXFORD UNIV PRESS
Citation: Biostatistics, 2(3). p. 295-307
Abstract: It is very common in regression analysis to encounter incompletely observed covariate information. A recent approach to analyse such data is weighted estimating equations (Robins, J. M., Rotnitzky, A. and Zhao, L. P. (1994), JASA, 89, 846-866, and Zhao, L. P., Lipsitz, S. R. and Lew, D. (1996), Biometrics, 52, 1165-1182). With weighted estimating equations, the contribution to the estimating equation from a complete observation is weighted by the inverse of the probability of being observed. We propose a test statistic to assess if the weighted estimating equations produce biased estimates. Our test statistic is similar to the test statistic proposed by DuMouchel and Duncan (1983) for weighted least squares estimates for sample survey data. The method is illustrated using data from a randomized clinical trial on chemotherapy for multiple myeloma
URI: http://hdl.handle.net/1942/397
DOI: 10.1093/biostatistics/2.3.295
ISSN: 1465-4644
Category: A1
Type: Journal Contribution
Appears in Collections: Research publications

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