Modelling multiple ovulation, fertilization, and embryo loss in human fertility studies

Area of Science:

• Reproductive biology
• Biostatistics
• Human fertility modeling

Background:

• Existing human fertility models often assume single ovum release per cycle.
• This assumption is inaccurate, as multiple ovulations occur in some pregnancies, notably dizygotic twins.
• This misspecification can lead to errors in fertility predictions.

Purpose of the Study:

• To propose a novel statistical model for human fertility that incorporates multiple ovulations.
• To improve the accuracy of fertility models by accounting for variations in ovum release.
• To provide a framework for analyzing factors influencing multiple ovulation and fertilization.

Main Methods:

• Developed a multinomial distribution model for unobservable viable ova per cycle.
• Incorporated Bernoulli trials to represent fertilization success based on intercourse timing.
• Utilized Markov chain Monte Carlo (MCMC) for parameter estimation.
• Applied the model to data from a North Carolina pregnancy study.

Main Results:

• The proposed model accommodates covariate effects, couple heterogeneity, and a sterile subpopulation.
• It allows for the incorporation of early pregnancy detection data to estimate embryo loss probability.
• Demonstrated the model's application to real-world pregnancy data and assisted reproduction studies.

Conclusions:

• The new model offers a more realistic representation of human ovulation and fertilization processes.
• It enhances the understanding of factors affecting fertility, including multiple ovulations.
• The methodology is applicable to various reproductive health studies, including assisted reproduction.