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Activity Number: 495
Type: Contributed
Date/Time: Wednesday, August 12, 2015 : 8:30 AM to 10:20 AM
Sponsor: SSC
Abstract #315842
Title: Modeling Length-Biased Multistate Data from Prospective Cohort Studies
Author(s): Nathalie Moon* and Leilei Zeng and Richard Cook
Companies: University of Waterloo and University of Waterloo and University of Waterloo
Keywords: length-bias ; response-dependent sampling ; multistate model ; relative efficiency ; biostatistics ; model misspecification
Abstract:

The prevalent cohort study is an efficient framework for studying the rate of disease progression for many chronic conditions. However, in the analysis of survival data from such studies, the hazard in the sample is not equal to the hazard in the population because prevalent cohort sampling schemes are length biased. This is also true for transition intensities in multistate processes where a prevalent cohort sampling scheme is adopted. Interest here lies in developing suitable methods for the analysis of transition intensities in a general multistate framework for modelling progression through stages of chronic disease processes such as diabetes or dementia. We examine a hierarchy of conditional models relying on different assumptions about the available data. By directly modelling the distribution of disease onset times, efficiency gains are realized and quantified through use of augmented conditional likelihoods, paralleling the unconditional approach for survival data. Methods for weakly parametric analyses using piecewise constant transition intensities are developed and assessed by simulation.


Authors who are presenting talks have a * after their name.

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