 Non‐parametric estimation of the age‐at‐onset distribution from a cross‐sectional sampleS. Mandal, J. Qin and R.M. Pfeiffer Biometrics, 2023, vol. 79, issue 3, 1701-1712 Abstract: We propose and study a simple and innovative non‐parametric approach to estimate the age‐of‐onset distribution for a disease from a cross‐sectional sample of the population that includes individuals with prevalent disease. First, we estimate the joint distribution of two event times, the age of disease onset and the survival time after disease onset. We accommodate that individuals had to be alive at the time of the study by conditioning on their survival until the age at sampling. We propose a computationally efficient expectation–maximization (EM) algorithm and derive the asymptotic properties of the resulting estimates. From these joint probabilities we then obtain non‐parametric estimates of the age‐at‐onset distribution by marginalizing over the survival time after disease onset to death. The method accommodates categorical covariates and can be used to obtain unbiased estimates of the covariate distribution in the source population. We show in simulations that our method performs well in finite samples even under large amounts of truncation for prevalent cases. We apply the proposed method to data from female participants in the Washington Ashkenazi Study to estimate the age‐at‐onset distribution of breast cancer associated with carrying BRCA1 or BRCA2 mutations. Date: 2023 Downloads: (external link) Related works: Export reference: BibTeX RIS (EndNote, ProCite, RefMan) HTML/Text Persistent link: https://EconPapers.repec.org/RePEc:bla:biomet:v:79:y:2023:i:3:p:1701-1712 Ordering information: This journal article can be ordered from Access Statistics for this article More articles in Biometrics from The International Biometric Society |
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