Bayesian methods for searching for optimal rules for timing intercourse to achieve pregnancy.

Area of Science:

• Reproductive Medicine
• Biostatistics
• Decision Theory

Background:

• Increasing maternal age at conception attempts raises concerns about natural fertility.
• Infertility affects couples attempting conception for over 12 months, often leading to assisted reproductive therapy (ART).
• ART is costly and carries potential risks for offspring, highlighting the need for natural conception optimization.

Purpose of the Study:

• To develop optimal rules for timing intercourse to maximize conception probability.
• To minimize the number of intercourse attempts required for conception.
• To utilize cycle day and biomarker information for improved fertility prediction.

Main Methods:

• A Bayesian decision theoretic framework was employed to develop optimal intercourse timing rules.
• A biologically based statistical model was used to link cycle day and biomarkers to conception probability.
• A stochastic search procedure identified rules maximizing expected utility, applied to Italian study data.

Main Results:

• The proposed Bayesian approach can identify effective intercourse timing strategies.
• Optimal rules aim for high conception probabilities with reduced intercourse frequency.
• The model effectively relates cycle-specific data to conception likelihood.

Conclusions:

• Bayesian decision theory offers a robust method for optimizing natural conception timing.
• Effective intercourse timing can reduce the need for costly and risky assisted reproductive therapies.
• Personalized rules based on individual cycle data can enhance fertility outcomes.