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.
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Acknowledgements
This research was partially sponsored by the project ‘Physical growth, body composition and nutritional status of the Bengal school aged children, adolescents, and young adults of Calcutta, India: Effects of socioeconomic factors on secular trends,’ funded by the Neys-Van Hoogstraten Foundation of the Netherlands. The authors thank Professor Parasmani Dasgupta, leader of the project, for making the data available for this research. Also, the first author thanks Dr. Bibhas Chakrobarty for his financial support through the Duke-NUS start-up grant R-913-200-074-263, the NIH grant 1 R01 DE023072-01 and the Singapore Ministry of Education grant MOE2015-T2-2-056.
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Salehabadi, S.M., Sengupta, D. (2018). Recent Advances in the Statistical Analysis of Retrospective Time-to-Event Data. In: Dasgupta, R. (eds) Advances in Growth Curve and Structural Equation Modeling. Springer, Singapore. https://doi.org/10.1007/978-981-13-1843-6_9
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