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Recent Advances in the Statistical Analysis of Retrospective Time-to-Event Data

In: Advances in Growth Curve and Structural Equation Modeling

Author

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  • Sedigheh Mirzaei Salehabadi

    (E. K. Shriver National Institute of Child Health and Human Development)

  • Debasis Sengupta

    (Indian Statistical Institute, Applied Statistical Unit)

Abstract

In a cross-sectional observational study on time-to-event, the probability distribution of that time is often estimated from data on current status. Recall data on the time of occurrence of the landmark event can provide more information in this regard. Even so, the subjects may not be able to recall the time precisely. This type of incompleteness is a peculiarity of recall data, which poses a challenge to analysis. Valid likelihood-based procedures for inference have emerged in a number of papers published only recently. In this article, we review these papers and show how one can estimate the time-to-event distribution parametrically or nonparametrically, and also assess the effect of covariates, by using current status data or incompletely recalled data. The methods are illustrated through the analysis of menarcheal data from a recent anthropometric study of adolescent and young adult females in Kolkata, India.

Suggested Citation

  • Sedigheh Mirzaei Salehabadi & Debasis Sengupta, 2018. "Recent Advances in the Statistical Analysis of Retrospective Time-to-Event Data," Springer Books, in: Ratan Dasgupta (ed.), Advances in Growth Curve and Structural Equation Modeling, pages 137-150, Springer.
    • Handle: RePEc:spr:sprchp:978-981-13-1843-6_9
    DOI: 10.1007/978-981-13-1843-6_9
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