title: Modeling human fertility in the presence of measurement error.
abstract: The probability of conception in a given menstrual cycle is closely related to the timing of intercourse relative to ovulation. Although commonly used markers of time of ovulation are known to be error prone, most fertility models assume the day of ovulation is measured without error. We develop a mixture model that allows the day to be misspecified. We assume that the measurement errors are i.i.d. across menstrual cycles. Heterogeneity among couples in the per cycle likelihood of conception is accounted for using a beta mixture model. Bayesian estimation is straightforward using Markov chain Monte Carlo techniques. The methods are applied to a prospective study of couples at risk of pregnancy. In the absence of validation data or multiple independent markers of ovulation, the identifiability of the measurement error distribution depends on the assumed model. Thus, the results of studies relating the timing of intercourse to the probability of conception should be interpreted cautiously.
authors.affiliation: Biostatistics Branch, National Institute of Environmental Health Sciences, Research Triangle Park, North Carolina 27709, USA. dunson1@niehs.nih.gov
authors.forename: D B
authors.initials: DB
authors.lastname: Dunson
authors.forename: C R
authors.initials: CR
authors.lastname: Weinberg
mesh_terms: D000465:Algorithms; D001499:Bayes Theorem; D001699:Biometry; D015331:Cohort Studies; D003627:Data Interpretation, Statistical; D005260:Female; D005298:Fertility; D006801:Humans; D008297:Male; D008390:Markov Chains; D008597:Menstrual Cycle; D008954:Models, Biological; D009010:Monte Carlo Method; D010060:Ovulation; D011247:Pregnancy; D013997:Time Factors