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Parametric models for combined failure time data from an incident cohort study and a prevalent cohort study with follow-up

Author

Listed:
  • McVittie James
  • Wolfson David
  • Stephens David

    (McGill University, Mathematics and Statistics, 805 Sherbrooke Street West, Montreal, QuebecCanada)

  • Addona Vittorio

    (Macalester College, Mathematics, Statistics and Computer Science, St.Paul, Minnesota, United States)

  • Buckeridge David

    (McGill University, Epidemiology, Biostatistics and Occupational Health, Montreal, QuebecCanada)

Abstract

A classical problem in survival analysis is to estimate the failure time distribution from right-censored observations obtained from an incident cohort study. Frequently, however, failure time data comprise two independent samples, one from an incident cohort study and the other from a prevalent cohort study with follow-up, which is known to produce length-biased observed failure times. There are drawbacks to each of these two types of study when viewed separately. We address two main questions here: (i) Can our statistical inference be enhanced by combining data from an incident cohort study with data from a prevalent cohort study with follow-up? (ii) What statistical methods are appropriate for these combined data? The theory we develop to address these questions is based on a parametrically defined failure time distribution and is supported by simulations. We apply our methods to estimate the duration of hospital stays.

Suggested Citation

  • McVittie James & Wolfson David & Stephens David & Addona Vittorio & Buckeridge David, 2021. "Parametric models for combined failure time data from an incident cohort study and a prevalent cohort study with follow-up," The International Journal of Biostatistics, De Gruyter, vol. 17(2), pages 283-293, November.
    • Handle: RePEc:bpj:ijbist:v:17:y:2021:i:2:p:283-293:n:10
    DOI: 10.1515/ijb-2020-0042
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