Joint Modeling Of Hierarchical Data With Application To Prospective Pregnancy Studies

Although fecundity has been studied for decades, the heterogeneity among couples, both biologically and behaviorally, is not well understood. The length of the woman's menstrual cycle has been shown to play an important role; however, a complete assessment of this role requires a model that accounts for both male and female risk factors and the couple's intercourse pattern. We develop and implement a Bayesian joint modeling approach to estimate the woman's underlying distribution of cycle length and assess its relation with couple fecundity while accounting for risk factors of both partners and intercourse frequency and timing relative to ovulation. We apply our approach to prospective pregnancy studies in which couples may enroll when they learn of the study as opposed to waiting for the start of a new menstrual cycle. Due to length-bias, the enrollment cycle will be stochastically larger than the general run of cycles, a typical property of prevalent cohort studies. We develop and evaluate an approach for unbiased estimation of the cycle length distribution for the study population that accounts for length-bias and selection effects, where the probability of enrollment may depend on the time since the last menstrual period. We find that shorter and longer cycle lengths are negatively associated with fecundity even with adjustment for semen quality, age, smoking, and intercourse pattern. This finding motivates investigation of environmental chemicals, in particular perfluoroalkyl surfactants (PFASs), and their potential role in cycle length and fecundity. We extend the joint model to include exposure to PFASs and find that 2-(N-methyl-perfluorooctane sulfonamido) acetate (MeFOSAA) and perfluorooctanoate (PFOA) are associated with shorter cycles while perfluorodecanoate (PFDA) is associated with longer cycles. Further, we find perfluorononanoate (PFNA) and perfluorooctane sulfonamide (PFOSA) are adversely associated with fecundity

A BAYESIAN APPROACH TO JOINT MODELING OF MENSTRUAL CYCLE LENGTH AND FECUNDITY

Female menstrual cycle length is thought to play an important role in couple fecundity, or the biologic capacity for reproduction irrespective of pregnancy intentions. A complete assessment of the association between menstrual cycle length and fecundity requires a model that accounts for multiple risk factors (both male and female) and the couple\u27s intercourse pattern relative to ovulation. We employ a Bayesian joint model consisting of a mixed effects accelerated failure time model for longitudinal menstrual cycle lengths and a hierarchical model for the conditional probability of pregnancy in a menstrual cycle given no pregnancy in previous cycles of trying, in which we include covariates for the male and the female and a flexible spline function of intercourse timing. Using our joint modeling approach to analyze data from the Longitudinal Investigation of Fertility and the Environment Study, a couple based prospective pregnancy study, we found a significant quadratic relation between menstrual cycle length and the probability of pregnancy even with adjustment for other risk factors, including male semen quality, age, and smoking status

Joint Modeling Of Hierarchical Data With Application To Prospective Pregnancy Studies

Although fecundity has been studied for decades, the heterogeneity among couples, both biologically and behaviorally, is not well understood. The length of the woman's menstrual cycle has been shown to play an important role; however, a complete assessment of this role requires a model that accounts for both male and female risk factors and the couple's intercourse pattern. We develop and implement a Bayesian joint modeling approach to estimate the woman's underlying distribution of cycle length and assess its relation with couple fecundity while accounting for risk factors of both partners and intercourse frequency and timing relative to ovulation. We apply our approach to prospective pregnancy studies in which couples may enroll when they learn of the study as opposed to waiting for the start of a new menstrual cycle. Due to length-bias, the enrollment cycle will be stochastically larger than the general run of cycles, a typical property of prevalent cohort studies. We develop and evaluate an approach for unbiased estimation of the cycle length distribution for the study population that accounts for length-bias and selection effects, where the probability of enrollment may depend on the time since the last menstrual period. We find that shorter and longer cycle lengths are negatively associated with fecundity even with adjustment for semen quality, age, smoking, and intercourse pattern. This finding motivates investigation of environmental chemicals, in particular perfluoroalkyl surfactants (PFASs), and their potential role in cycle length and fecundity. We extend the joint model to include exposure to PFASs and find that 2-(N-methyl-perfluorooctane sulfonamido) acetate (MeFOSAA) and perfluorooctanoate (PFOA) are associated with shorter cycles while perfluorodecanoate (PFDA) is associated with longer cycles. Further, we find perfluorononanoate (PFNA) and perfluorooctane sulfonamide (PFOSA) are adversely associated with fecundity