studies involve screening a sample of individuals from a population for
disease, recruiting affected individuals, and prospectively following the
cohort of individuals to record the occurrence of disease-related complications
or death. This design features a response-biased sampling scheme since
individuals living a long time with the disease are preferentially sampled, so
naive analysis of the time from disease onset to death will over-estimate
survival probabilities. Unconditional and conditional analyses of the resulting
data can yield consistent estimates of the survival distribution subject to the
validity of their respective model assumptions. The time of disease onset is
retrospectively reported by sampled individuals, however, this is often
associated with measurement error. In this article we present a framework for
studying the effect of measurement error in disease onset times in prevalent
cohort studies, report on empirical studies of the effect in each framework of
analysis, and describe likelihood-based methods to address such a measurement
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