Heterogeneity in fecundability studies: issues and modelling
Heterogeneity in fecundability studies: issues and modelling
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DOI：
10.1191/0962280206sm436oa
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发表时间：
2006-04-01
影响因子：
2.3
通讯作者：
Ecochard, R
中科院分区：
文献类型：
--
作者：
Ecochard, R
生育力的模型化最近从人口统计学和基于人口的背景下，生殖生物学和不孕症的治疗。这就产生了对灵活性和鲁棒性的强烈要求。事实上，解释和无法解释的异质性是不可忽视的偏见来源，导致对生育率的决定因素或生殖技术的成功率等得出错误的结论。异质性有两个主要来源：生物异质性和性行为异质性。提出了一个统一的介绍时间到怀孕和Barrett-Marshall模型，启发他们的相似性和差异性的建模异质性的繁殖力，混合模型的繁殖力研究的工具，允许无法解释的异质性和量化的异质性的影响观察因素和变异的大小，这种无法解释的异质性之间的亚群。在繁殖力研究的建模策略的一些标准，建议强调单位处理加和性标准。描述了由异质性引起的强烈而复杂的选择过程，以及观察到的和未观察到的繁殖力因素的选择和交叉选择过程。关于数据收集和统计推断的后果进行了讨论。在目前的情况下，一个共识的数据收集和统计分析的一般规则，将有助于比较结果，并增加这些结果的可靠性医疗实践。
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 ill medical practice.