Jove
Visualize
Contact Us
JoVE
x logofacebook logolinkedin logoyoutube logo
ABOUT JoVE
OverviewLeadershipBlogJoVE Help Center
AUTHORS
Publishing ProcessEditorial BoardScope & PoliciesPeer ReviewFAQSubmit
LIBRARIANS
TestimonialsSubscriptionsAccessResourcesLibrary Advisory BoardFAQ
RESEARCH
JoVE JournalMethods CollectionsJoVE Encyclopedia of ExperimentsArchive
EDUCATION
JoVE CoreJoVE BusinessJoVE Science EducationJoVE Lab ManualFaculty Resource CenterFaculty Site
Terms & Conditions of Use
Privacy Policy
Policies

Related Experiment Videos

Heterogeneity in fecundability studies: issues and modelling.

René Ecochard1

  • 1Department of Biostatistics Hospices Civils de Lyon, Laboratoire de Biométrie et Biologie Evolutive (UMR 5558), CNRS, University Lyon 1, 162 Avenue Lacassagne 69424, Lyon Cedex 03, France. rene.ecochard@chu-lyon.fr

Statistical Methods in Medical Research
|April 18, 2006
PubMed
Summary

This study addresses heterogeneity in fecundability modeling for infertility treatments. It proposes mixed models to account for unexplained variations, improving the reliability of fertility research and clinical practice.

Related Concept Videos

You might also read

Related Articles

Articles linked to this work by shared authors, journal, and citation graph.

Sort by
Same author

Using corpus luteum formation with dominant follicle collapse to improve the criteria for identifying ovulation.

Reproductive biomedicine online·2026
Same author

DASS-21 Stress, but Not Anxiety or Depression, Is Associated with Premenstrual Stress.

Journal of clinical medicine·2025
Same author

Could the Contraceptive Pill Pose a Risk to Brain Development in Young Girls?

The Linacre quarterly·2025
Same author

Accuracy of an Overnight Axillary-Temperature Sensor for Ovulation Detection: Validation in 194 Cycles.

Sensors (Basel, Switzerland)·2025
Same author

Identifying malaria epidemic periods in Togo by health district and target group: a generalised additive model approach.

BMC infectious diseases·2025
Same author

Participation of general practitioners and therapeutic patient education in the care of infertile couples.

European journal of obstetrics, gynecology, and reproductive biology·2025

Area of Science:

  • Reproductive Biology and Medicine
  • Biostatistics and Statistical Modeling

Background:

  • Fecundability modeling has evolved from demography to reproductive biology, necessitating flexible and robust approaches.
  • Heterogeneity, both explained and unexplained, poses significant bias risks in fertility studies and reproductive technique assessments.
  • Key sources of heterogeneity include biological variations and differences in sexual behavior.

Purpose of the Study:

  • To provide a unified presentation of time-to-pregnancy and Barrett-Marshall models, highlighting their similarities and differences in handling fecundability heterogeneity.
  • To introduce mixed models as advanced tools for analyzing fecundability, accommodating unexplained heterogeneity and quantifying its impact.
  • To suggest criteria for selecting appropriate modeling strategies in fecundability research, emphasizing the unit-treatment additivity principle.

Related Experiment Videos

Main Methods:

  • Comparative analysis of time-to-pregnancy and Barrett-Marshall models for fecundability heterogeneity.
  • Application of mixed-effects models to address unexplained variability and quantify heterogeneity in observed factor effects.
  • Discussion of selection processes (including cross-selection) for observed and unobserved fecundability factors.

Main Results:

  • Mixed models offer a robust framework for managing unexplained heterogeneity in fecundability studies.
  • Quantification of heterogeneity in factor effects and variability in unexplained heterogeneity across subpopulations is achievable.
  • Understanding complex selection processes is crucial for accurate statistical inference.

Conclusions:

  • Flexible and robust modeling is essential for accurate fecundability research, particularly in the context of infertility treatment.
  • Mixed models are valuable tools for dissecting and accounting for various sources of heterogeneity.
  • Establishing consensus guidelines for data collection and statistical analysis is vital for enhancing result comparability and clinical reliability.