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Bayesian inferences on predictors of conception probabilities.

David B Dunson1, Joseph B Stanford

  • 1Biostatistics Branch, National Institute of Environmental Health Sciences, MD A3-03, P.O. Box 12233, Research Triangle Park, North Carolina 27709, USA. dunson1@niehs.nih.gov

Biometrics
|March 2, 2005
PubMed
Summary

This study introduces a new Bayesian model to predict conception probability based on intercourse timing. The model accounts for multiple intercourse days per cycle, aiding reproductive science and fertility research.

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Area of Science:

  • Reproductive biology
  • Biostatistics
  • Statistical modeling

Background:

  • Predicting conception probability is crucial for reproductive science and fertility planning.
  • Analyzing conception data is complex due to multiple intercourse days per cycle and inter-cycle dependencies.
  • Existing models face challenges in accurately capturing these data structures.

Purpose of the Study:

  • To propose a novel Bayesian approach for estimating day-specific probabilities of conception.
  • To develop a model that incorporates woman-specific frailty and day-specific covariates.
  • To provide a computationally efficient method for analyzing conception data.

Main Methods:

  • A generalized Bayesian approach based on the Barrett and Marshall model.

Related Experiment Videos

  • Incorporation of woman-specific frailty and day-specific covariates.
  • Utilizing an auxiliary variables formulation for efficient posterior computation.

    • Main Results:

    • The proposed model yields a simple closed-form expression for the marginal probability of conception.
    • The method facilitates efficient posterior computation.
    • Demonstrates applicability beyond fecundability studies for variable selection and model averaging.

    Conclusions:

    • The developed Bayesian model offers an effective method for analyzing conception probabilities.
    • The approach is adaptable for various discrete event time data analyses.
    • Enhances understanding of conception timing and reproductive outcomes.