Incidence is an important epidemiologic concept particularly useful in assessing an intervention, quantifying disease risk, and planning health resources. Incident cohort studies constitute the gold-standard in estimating disease incidence. However, due to material constraints, data are often collected from prevalent cohort studies whereby diseased individuals are recruited through a cross-sectional survey and followed forward in time. We discuss the identifiability of measures of incidence in the context of prevalent cohort survival studies and derive nonparametric maximum likelihood estimators and their asymptotic properties. The proposed methodology accounts for calendar-time and age-at-onset variation in disease incidence while also addressing common complications arising from the sampling scheme, hence providing flexible and robust estimates. We also discuss age-specific incidence and adjustments for temporal variations in survival. We apply our methodology to data from the Canadian Study of Health and Aging and provide insight into temporal trends in the incidence of dementia in the Canadian elderly population.



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