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Potential for bias in estimating human fecundability parameters: a comparison of statistical models
1Department of Biostatistics, University of North Carolina at Chapel Hill 27599-7400, USA. zhou@bios.unc.edu
Statistics in Medicine
|March 10, 1999
Summary
This study introduces a subject-specific random effects model for fecundability research, improving upon traditional methods. The new model offers more consistent parameter estimation, enhancing the generalizability of findings in fertility studies.
Area of Science:
- Reproductive Epidemiology
- Biostatistics
- Statistical Modeling
Background:
- Fecundability studies model conception over time, presenting discrete failure-time scenarios.
- Traditional analyses often assume independence between menstrual cycle outcomes, which may not hold true due to biological variability.
Purpose of the Study:
- To contrast traditional statistical models with a random effects model for analyzing fecundability.
- To clarify parameter interpretations and address limitations of time-dependent effects in traditional fertility models.
Main Methods:
- Comparison of traditional independence-based models with a subject-specific random effects model.
- Application of models to illustrate differences using data from a North Carolina fecundability study.
Main Results:
- Traditional models can produce regression parameters dependent on follow-up time, limiting generalizability.
- The subject-specific random effects model provides consistent parameter estimation when the distribution is correctly specified.
Conclusions:
- Subject-specific modeling offers a more robust approach for analyzing fecundability data compared to traditional methods.
- Accurate statistical modeling is crucial for reliable inferences in fertility and reproductive health research.