title: Estimating age at onset distributions: the bias from prevalent cases and its impact on risk estimation.
abstract: Since many disorders have a variable age at onset, knowing the age at onset distribution of a disease facilitates epidemiologic analyses in several ways. The age at onset distribution is commonly used to estimate morbidity risks or the recurrence risks in genetic counseling. Unfortunately, estimation of a disease's age at onset distribution is not straightforward. The observed age at onset distribution obtained from prevalent cases is usually used in these epidemiologic analyses. Through simulation studies, we show that, in certain situations, the observed age at onset distribution has a non-negligible downward bias. This bias can lead to a substantial underestimation of the morbidity risk or the recurrence risk. The simulations also demonstrate that a non-parametric approach for correcting the age at onset distribution works well even when mortality increases after onset. The results have implications for diseases that have adult onset and/or increased mortality after onset. We suggest that researchers should use corrected age at onset distributions, rather than relying on observed distributions, in the calculation of either morbidity risks or recurrence risks.
authors: Department of Epidemiology, Harvard School of Public Health, Boston, Massachusetts.
authors: W J
authors: WJ
authors: Chen
authors: S V
authors: SV
authors: Faraone
authors: E J
authors: EJ
authors: Orav
authors: M T
authors: MT
authors: Tsuang
mesh_terms: D000293:Adolescent; D000328:Adult; D000367:Age Factors; D000368:Aged; D000369:Aged, 80 and over; D015982:Bias; D002648:Child; D002675:Child, Preschool; D003198:Computer Simulation; D006801:Humans; D007223:Infant; D007231:Infant, Newborn; D008875:Middle Aged; D008954:Models, Biological; D009017:Morbidity; D009026:Mortality; D012008:Recurrence; D012307:Risk Factors