Background. Incidence and lifetime risk of diabetes are important public health measures. Traditionally, nonparametric estimates are obtained from survey data by means of a Nelson-Aalen estimator which requires data information on both incident events and risk sets from the entire cohort. Such data information is rarely available in real studies. Methods. We compare two different approaches for obtaining nonparametric estimates of age-specific incidence and lifetime risk with emphasis on required assumptions. The first and novel approach only considers incident cases occurring within a fixed time window-we have termed this cohort-of-cases data-which is linked explicitly to the birth process in the past. The second approach is the usual Nelson-Aalen estimate which requires knowledge on observed time at risk for the entire cohort and their incident events. Both approaches are used on data on anti-diabetic medications obtained from Odense Pharmacoepidemiological Database, which covers a population of approximately 470,000 over the period 1993-2003. For both methods we investigate if and how incidence rates can be projected. Results. Both the new and standard method yield similar sigmoidal shaped estimates of the cumulative distribution function of age-specific incidence. The Nelson-Aalen estimator gives somewhat higher estimates of lifetime risk (15.65% (15.14%; 16.16%) for females, and 17.91% (17.38%; 18.44%) for males) than the estimate based on cohort-of-cases data (13.77% (13.74%; 13.81%) for females, 15.61% (15.58%; 15.65%) for males). Accordingly the projected incidence rates are higher based on the Nelson-Aalen estimate-also too high when compared to observed rates. In contrast, the cohort-of-cases approach gives projections that fit observed rates better. Conclusion. The developed methodology for analysis of cohort-of-cases data has potential to become a cost-effective alternative to a traditional survey based study of incidence. To allow more general use of the methodology, more research is needed on how to relax stationarity assumptions.
ASJC Scopus subject areas
- Health Informatics