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Developing a postestimation command for joint models in merlin

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  • Nuzhat B. Ashra

    (Biostatistics Research Group, Department of Health Sciences, University of Leicester)

  • Michael Crowther

    (Biostatistics Research Group, Department of Health Sciences, University of Leicester)

Abstract

Joint longitudinal-survival models are now increasingly utilized to quantify the association between a repeatedly measured biomarker and time-to-event outcome. Where singular methods ignore the dependency between the biomarker and time-to-event outcome, joint models describe the association while accounting for possible measurement error and the intermittent nature of observations. Furthermore, extensions to these models can allow estimation of survival probabilities that are conditional on measurements to date and individual characteristic information. These probabilities give an up-to-date risk estimate for event occurrence tailored to the individual. Currently, there are two commands available in Stata, that are designed to fit these models. The command stjm was first on the scene and was specifically written to fit joint models. However, as the new kid on the block, merlin has greater flexibility than its predecessor. As a fairly recently established command, however, the postestimation command options are still a work in progress. The aim is to establish a command using both ado and Mata programming that will be able to produce a graphical illustration of individualized conditional survival probabilities. In this presentation, I will be talking about my coding journey to this end.

Suggested Citation

  • Nuzhat B. Ashra & Michael Crowther, 2019. "Developing a postestimation command for joint models in merlin," London Stata Conference 2019 02, Stata Users Group.
    • Handle: RePEc:boc:usug19:02
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    References listed on IDEAS

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    1. Dimitris Rizopoulos, 2011. "Dynamic Predictions and Prospective Accuracy in Joint Models for Longitudinal and Time-to-Event Data," Biometrics, The International Biometric Society, vol. 67(3), pages 819-829, September.
    2. Eleni†Rosalina Andrinopoulou & Paul H. C. Eilers & Johanna J. M. Takkenberg & Dimitris Rizopoulos, 2018. "Improved dynamic predictions from joint models of longitudinal and survival data with time†varying effects using P†splines," Biometrics, The International Biometric Society, vol. 74(2), pages 685-693, June.
    3. Michael J. Crowther & Keith R. Abrams & Paul C. Lambert, 2013. "Joint modeling of longitudinal and survival data," Stata Journal, StataCorp LP, vol. 13(1), pages 165-184, March.
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