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Biostatistics Advance Access originally published online on July 27, 2009
Biostatistics 2009 10(4):756-772; doi:10.1093/biostatistics/kxp029
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© The Author 2009. Published by Oxford University Press. All rights reserved. For permissions, please e-mail: journals.permissions@oxfordjournals.org.

Second-order estimating equations for the analysis of clustered current status data

Richard J. Cook*
David Tolusso

Department of Statistics and Actuarial Science, University of Waterloo, Waterloo, ON, Canada N2L 3G1 rjcook{at}uwaterloo.ca

* To whom correspondence should be addressed.

With clustered event time data, interest most often lies in marginal features such as quantiles or probabilities from the marginal event time distribution or covariate effects on marginal hazard functions. Copula models offer a convenient framework for modeling. We present methods of estimating the baseline marginal distributions, covariate effects, and association parameters for clustered current status data based on second-order generalized estimating equations. We examine the efficiency gains realized from using second-order estimating equations compared with first-order equations, issues of copula misspecification, and apply the methods to motivating studies including one on the incidence of joint damage in patients with psoriatic arthritis.

Keywords: Current status data; Generalized estimating equations; Piecewise constant hazards; Relative efficiency

Received August 5, 2008; revised May 12, 2009; revised June 6, 2009; accepted for publication June 27, 2009.


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