Scholarlink

Background <p>Proportion-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.</p> Methods <p>This 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.</p> Results <p>Common 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.</p> Conclusions <p>Misapplication 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.</p>

错误：搜索内容不能为空，请输入英文关键词
错误：关键词超出字数限制，请精简
高级检索

Reevaluating statistical methods for proportion-based laboratory outcomes in assisted reproduction

  • 期刊论文

  • 出版模式：

    开放获取

  • 发表日期：

    2026年5月23日

查看全文
  • Chaofeng Wei,
  • Hsun-Ming Chang,
  • Chenggang Wang,
  • Peter C. K. Leung,
  • Fang Lian

摘要

Background

Proportion-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.

Methods

This 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.

Results

Common 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.

Conclusions

Misapplication 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.