A Random-Effects Model for Cycle Viability in Fertility Studies
该研究提出一个随机效应模型，用于分析夫妻间月经周期可行性的异质性，并区分生物因素与性行为差异对生育能力的影响。
Abstract Models for fertility that take into account the timing of intercourse relative to ovulation are needed to estimate the influence of both endogenous and exogenous factors on human fertility. The classical model assumes that some menstrual cycles are "viable" and some are not, where "viability" is determined by whether hormonal, uterine, and gamete-related factors are favorable to gestation. Within each viable cycle, the various days with intercourse are assumed to act independently; within each nonviable cycle, the days with intercourse can have no effect. Cycle viability for individual cycles is latent in that it is not ascertainable when conception does not occur. The classical model neglects the statistical dependency of outcomes among menstrual cycles within individual couples. Current marginal approaches cannot determine the degree to which heterogeneity in fecundability is biologically based versus the degree to which it is secondary to variation in intercourse behavior from couple to couple. We describe a random-effects model based on assuming that the cycle viability probability varies from couple to couple according to a beta distribution, and we use an EM algorithm to fit the model. The proposed estimating procedure is fully expandable to allow covariate effects on the beta variate. Our method can be applied more generally whenever dependency among Bernoulli trials is induced by a susceptibility state and the outcomes can be observed only in the aggregate. Based on data from a cohort of couples with no known fertility problems who were attempting pregnancy, cycle viability is found to be heterogeneous among couples. Stratification on the presence or absence of prenatal exposure of the woman to her mother's cigarette smoking revealed a statistically significant difference in the two cycle viability distributions. We discuss differences in the interpretation of the beta model compared to the marginal approach based on generalized estimating equations. Key Words: Aggregated Bernoulli outcomesBeta distributionEM algorithmFertilityLatent variablesRandom effect