Literature DB >> 16615654

Heterogeneity in fecundability studies: issues and modelling.

René Ecochard1.   

Abstract

Modelization of fecundability stepped recently from demography and population-based contexts to reproductive biology and treatment of infertility. This created a strong call for flexibility and robustness. Indeed, explained and unexplained heterogeneities are non-negligible sources of bias that result in false conclusions as to the determinants of fertility or to the success rates of reproductive techniques, among other examples. There are two main sources of heterogeneity: biological heterogeneity and heterogeneity of sexual behaviour. A uniform presentation of time-to-pregnancy and Barrett-Marshall models is proposed to enlighten their similarities and differences in modelling heterogeneity of fecundability. Mixed models for fecundability studies are presented as tools to allow for unexplained heterogeneity and to quantify heterogeneity of the effect of observed factors and variability of size of this unexplained heterogeneity between subpopulations. Some criteria for the modelling strategy in fecundability studies are suggested with emphasis on the unit-treatment additivity criterion. The strong and complex selection process resulting from heterogeneity is described as well as the selection and cross-selection processes of observed and unobserved fecundability factors. Consequences regarding data collection and statistical inference are discussed. In the current context, a consensus setting general rules for data collection and statistical analysis would be useful to compare the results and increase the reliability of these results in medical practice.

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Year:  2006        PMID: 16615654     DOI: 10.1191/0962280206sm436oa

Source DB:  PubMed          Journal:  Stat Methods Med Res        ISSN: 0962-2802            Impact factor:   3.021


  8 in total

1.  Anti-Müllerian hormone: a potential new tool in epidemiologic studies of female fecundability.

Authors:  Donna D Baird; Anne Z Steiner
Journal:  Am J Epidemiol       Date:  2012-01-12       Impact factor: 4.897

2.  A Bayesian joint model of menstrual cycle length and fecundity.

Authors:  Kirsten J Lum; Rajeshwari Sundaram; Germaine M Buck Louis; Thomas A Louis
Journal:  Biometrics       Date:  2015-08-21       Impact factor: 2.571

3.  Stochasticity among Victims of COVID-19 Pandemic.

Authors:  Ramalingam Shanmugam; Gerald Ledlow; Karan P Singh
Journal:  J Multidiscip Healthc       Date:  2022-01-04

4.  Flexible Bayesian Human Fecundity Models.

Authors:  Sungduk Kim; Rajeshwari Sundaram; Germaine M Buck Louis; Cecilia Pyper
Journal:  Bayesian Anal       Date:  2012-11-27       Impact factor: 3.728

5.  Time to pregnancy: a computational method for using the duration of non-conception for predicting conception.

Authors:  Peter D Sozou; Geraldine M Hartshorne
Journal:  PLoS One       Date:  2012-10-04       Impact factor: 3.240

6.  A comparison of the beta-geometric model with landmarking for dynamic prediction of time to pregnancy.

Authors:  Rik van Eekelen; Hein Putter; David J McLernon; Marinus J Eijkemans; Nan van Geloven
Journal:  Biom J       Date:  2019-11-18       Impact factor: 2.207

7.  Fecundability and Sterility by Age: Estimates Using Time to Pregnancy Data of Japanese Couples Trying to Conceive Their First Child with and without Fertility Treatment.

Authors:  Shoko Konishi; Fumiko Kariya; Kisuke Hamasaki; Lena Takayasu; Hisashi Ohtsuki
Journal:  Int J Environ Res Public Health       Date:  2021-05-20       Impact factor: 3.390

8.  kmlShape: An Efficient Method to Cluster Longitudinal Data (Time-Series) According to Their Shapes.

Authors:  Christophe Genolini; René Ecochard; Mamoun Benghezal; Tarak Driss; Sandrine Andrieu; Fabien Subtil
Journal:  PLoS One       Date:  2016-06-03       Impact factor: 3.240

  8 in total

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