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Generalized estimating equations for censored data

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If you have a question about this talk, please contact Michael Sweeting.

Models for longitudinal measurements truncated by possibly informative dropout tend to be mathematically complex or computationally demanding. Diggle et al. (2007, JRSSC ) recently proposed an alternative using simple ideas from event-history analysis (where censoring is commonplace) to yield moment-based estimators for balanced, continuous longitudinal data. Here we show that their estimate may be derived as a limit of generalized estimating equations. We use this fact to extend their ideas to more general longitudinal contexts (unbalanced, categorical data, for example) while maintaining simplicity of understanding and implementation.

This talk is part of the MRC Biostatistics Unit Seminars series.

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