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Marginalized Transition Models for Longitudinal Binary Data With Ignorable and Nonignorable Dropout

Author

Listed:
  • Brenda Kurland

    (University of Washington)

  • Patrick Heagerty

    (University of Washington)

Abstract

We extend the marginalized transition model of Heagerty (2002) to accommodate nonignorable monotone dropout. Using a selection model, weakly identified dropout parameters are held constant and their effects evaluated through sensitivity analysis. For data missing at random (MAR), efficiency of inverse probability of censoring weighted generalized estimating equations (IPCW-GEE) is as low as 40% compared to a likelihood-based marginalized transition model (MTM) with comparable modeling burden. MTM and IPCW-GEE regression parameters both display misspecification bias for MAR and nonignorable missing data, and both reduce bias noticeably by improving model fit

Suggested Citation

  • Brenda Kurland & Patrick Heagerty, 2004. "Marginalized Transition Models for Longitudinal Binary Data With Ignorable and Nonignorable Dropout," UW Biostatistics Working Paper Series 1054, Berkeley Electronic Press.
    • Handle: RePEc:bep:uwabio:1054
    Note: oai:bepress.com:uwbiostat-1054
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    File URL: http://www.bepress.com/cgi/viewcontent.cgi?article=1054&context=uwbiostat
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    Cited by:

    1. Jonathan S. Schildcrout & Patrick J. Heagerty, 2007. "Marginalized Models for Moderate to Long Series of Longitudinal Binary Response Data," Biometrics, The International Biometric Society, vol. 63(2), pages 322-331, June.

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