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Monitoring Blood Glucose in Mouse Offspring After Intracytoplasmic Sperm Injection
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Reevaluating statistical methods for proportion-based laboratory outcomes in assisted reproduction.

Chaofeng Wei1,2, Hsun-Ming Chang3,4, Chenggang Wang5

  • 1First College of Clinical Medicine, Shandong University of Traditional Chinese Medicine, Jinan, China.

Journal of Ovarian Research
|May 23, 2026
PubMed
Summary
This summary is machine-generated.

Statistical methods for assisted reproductive technology (ART) laboratory outcomes are often misused, leading to inaccurate results. This review highlights appropriate statistical approaches for proportion-based ART data to improve research validity.

Keywords:
Assisted reproductive technologyBeta regressionGeneralized estimating equationGeneralized linear mixed modelLaboratory outcome analysisProportion-based outcomesStatistical methodology

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Area of Science:

  • Reproductive Medicine
  • Biostatistics
  • Laboratory Science

Background:

  • Assisted reproductive technology (ART) laboratory outcomes, such as fertilization and blastocyst rates, are crucial for evaluating treatment effectiveness.
  • These proportion-based outcomes are often bounded, skewed, and hierarchically structured (oocytes/embryos within cycles/patients), posing statistical challenges.
  • Inappropriate statistical analysis can lead to biased conclusions regarding treatment efficacy.

Purpose of the Study:

  • To identify common statistical misuses in analyzing proportion-based ART laboratory outcomes.
  • To summarize appropriate statistical methodologies for handling ART laboratory data.

Main Methods:

  • A narrative review of ART studies published between 2000 and 2025.
  • Extraction and categorization of statistical practices related to data reporting, modeling, and handling of clustering and boundary values.
  • Synthesis of recommended analytical frameworks for binary outcomes presented as proportions or counts.

Main Results:

  • Common issues include pooling data, using chi-square tests, omitting denominators, and employing linear models for skewed data.
  • Failure to account for intra-patient/cycle clustering is prevalent.
  • Appropriate methods include beta regression for proportions (0,1), zero-one-inflated beta models for boundary values, and binomial generalized linear mixed models (GLMMs) or generalized estimating equations (GEEs) when counts are available.

Conclusions:

  • Misapplication of statistical methods in ART laboratory research is widespread, potentially compromising reproducibility and clinical interpretation.
  • Adopting statistically sound, model-data aligned approaches and transparent reporting is essential.
  • Improved statistical practices will enhance the validity of ART research and support evidence-based decision-making in reproductive medicine.