Interval Estimates of Event Probability from Pairwise Correlated Data: Application in Epidemiology of Birth Defects

0522798 - ÚI 2020 GB eng A - Abstract
Klaschka, Jan - Malý, Marek - Šípek, A.
Interval Estimates of Event Probability from Pairwise Correlated Data: Application in Epidemiology of Birth Defects.
CFE-CMStatistics 2019: Book of Abstracts. London: Ecosta Econometrics and Statistics, 2019. s. 143-143. ISBN 978-9963-2227-8-0
Institutional support: RVO:67985807

Interval estimates of event probability are studied within a generalization of Bernoulli trials model: n = 2m zero-one-valued variables consist of m pairs with correlation phi between the two components. Independence between the m pairs and a common expectation theta of all n variables are assumed. The primary motivation and the main application field is in the epidemiology of congenital anomalies (birth defects) in twins. Occurrence of birth defects in both twins is known to be more frequent than under independence. Ignoring the fact and applying the binomial model would lead to over-liberal inferences. The focus is on the computation of exact interval estimates of theta - so far for fixed phi. Numerical procedures have been designed for the calculation of confidence bounds of Clopper-Pearson, Sterne and Blaker types. The key building block is the calculation of the probability mass function (pmf) of the number of events. Several pmf calculation methods have been tested. Among them, the numerical inversion (via iFFT) of the characteristic function appears to be the most computationally effective. A quasi-symbolic calculation based on the pmf representation as a matrix of polynomial coefficients is competitive under some (but not all) settings.
Permanent Link: http://hdl.handle.net/11104/0307227