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Usefulness of statistics for establishing evidence-based reproductive medicine

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Reproductive Medicine and Biology

Abstract

During the last decade, evidence-based medicine has been described as a paradigm shift in clinical practice, and as “the conscientious, explicit, and judicious use of current best evidence in making decisions about the care of individual patients”. Appropriate statistical methods for analyzing data are critical for the correct interpretation of the results in proof of the evidence. However, in the medical literature, these statistical methods are often incorrectly interpreted or misinterpreted, leading to serious methodological errors and misinterpretations. This review highlights several important aspects related to the design and statistical analysis for evidence-based reproductive medicine. First, we clarify the distinction between ratios, proportions, and rates, and then provide a definition of pregnancy rate. Second, we focus on a special type of bias called ‘confounding bias’, which occurs when a factor is associated with both the exposure and the disease but is not part of the causal pathway. Finally, we present concerns regarding misuse of statistical software or application of inappropriate statistical methods, especially in medical research.

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Abbreviations

ANCOVA:

Analysis of covariance

ANOVA:

Analysis of variance

ART:

Assisted reproduction technologies

BMI:

Body mass index

EBM:

Evidence-based medicine

EDA:

Exploratory data analysis

HCG:

Human chorionic gonadotropin

NEJM:

New England Journal of Medicine

ROC:

Receiver operating characteristic curves

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Acknowledgments

We are grateful to Dr. Takao Miyake at Miyake Women’s Clinic, Dr. Maki Murakami at Pharmaceuticals and Medical Devices Agency, Dr. Chizuru Ito at Department of Anatomy and Developmental Biology, Graduate School of Medicine, Chiba University, and Professor Isao Yoshimura at Tokyo University of Science for their valuable advice and suggestions.

Conflict of interest

None of the authors have a duality of interest with regard to this work.

Author information

Authors and Affiliations

  1. Clinical Research Center, Chiba University Hospital, 1-8-1 Inohana, Chuo-ku, Chiba, 260-8677, Japan

    Yasunori Sato

  2. Department of Biostatistics, Harvard School of Public Health, Boston, MA, USA

    Yasunori Sato

  3. Department of Management Science, Graduate School of Engineering, Tokyo University of Science, Tokyo, Japan

    Masahiko Gosho

  4. Department of Anatomy and Developmental Biology, Chiba University Graduate School of Medicine, Chiba, Japan

    Kiyotaka Toshimori

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  1. Yasunori Sato
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  2. Masahiko Gosho
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  3. Kiyotaka Toshimori
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Correspondence to Yasunori Sato.

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Sato, Y., Gosho, M. & Toshimori, K. Usefulness of statistics for establishing evidence-based reproductive medicine. Reprod Med Biol 11, 49–58 (2012). https://doi.org/10.1007/s12522-011-0106-5

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