ReviewJournal of ovarian research2026
Reevaluating statistical methods for proportion-based laboratory outcomes in assisted reproduction.
Review in Journal of ovarian research, 2026. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.
What it found
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The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
The trial behind it
Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.
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Authors and funding
5 authors.
Funding
Abstract
backgroundProportion-based laboratory outcomes in assisted reproductive technology (ART), including two-pronuclear fertilization rate, good-quality embryo rate, and blastocyst formation rate, are widely used to evaluate treatment effectiveness and laboratory performance. These outcomes are bounded between 0 and 1, frequently skewed, and arise from hierarchical data structures in which oocytes or embryos are nested within cycles and patients. Inappropriate statistical handling of such data can lead to biased inference and misinterpretation of treatment effects. This narrative review aimed to identify common methodological misuses in the analysis of proportion-based ART laboratory outcomes and to summarize appropriate statistical approaches.
methodsThis narrative review examined methodological and applied ART studies published between 2000 and 2025. Statistical practices were extracted and categorized according to data reporting (numerator-denominator structure), modeling strategies, and handling of clustering and boundary values. Recommended analytical frameworks were synthesized for binary outcomes reported either as a proportion or a count.
resultsCommon analytical issues included pooling embryo- or oocyte-level observations and applying chi-square tests, reporting absolute counts without denominators, describing skewed proportion outcomes using mean ± standard deviation and linear models, and failing to account for intra-patient or intra-cycle clustering. Appropriate approaches depended on data structure. Beta regression was suitable for continuous proportions within the open interval (0,1), while zero-one-inflated beta models accommodated observed boundary values. When numerator and denominator counts were available, binomial generalized linear mixed models (GLMMs) and generalized estimating equations (GEEs) preserved denominator information and accounted for hierarchical clustering.
conclusionsMisapplication of statistical methods to proportion-based ART laboratory outcomes remains common and may compromise reproducibility and clinical interpretation. Adoption of model-data alignment and transparent reporting practices will improve the validity of ART laboratory research and support evidence-based decision-making in reproductive medicine.
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