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Related Concept Videos

Decision Making: P-value Method01:09

Decision Making: P-value Method

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The process of hypothesis testing based on the P-value method includes calculating the P- value using the sample data and interpreting it.
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Types of Biopharmaceutical Studies: Controlled and Non-Controlled Approaches01:23

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Biopharmaceutical studies constitute a vital field aiming to enhance drug delivery methods and refine therapeutic approaches, drawing upon diverse interdisciplinary knowledge. In research methodologies, the choice between controlled and non-controlled studies significantly influences the study's reliability and accuracy.
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Regression Toward the Mean01:52

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Regression toward the mean (“RTM”) is a phenomenon in which extremely high or low values—for example, and individual’s blood pressure at a particular moment—appear closer to a group’s average upon remeasuring. Although this statistical peculiarity is the result of random error and chance, it has been problematic across various medical, scientific, financial and psychological applications. In particular, RTM, if not taken into account, can interfere when...
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Survival analysis is a cornerstone of medical research, used to evaluate the time until an event of interest occurs, such as death, disease recurrence, or recovery. Unlike standard statistical methods, survival analysis is particularly adept at handling censored data—instances where the event has not occurred for some participants by the end of the study or remains unobserved. To address these unique challenges, specialized techniques like the Kaplan-Meier estimator, log-rank test, and...
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Decision Making: Traditional Method01:14

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The process of hypothesis testing based on the traditional method includes calculating the critical value, testing the value of the test statistic using the sample data, and interpreting these values.
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Hazard Ratio01:12

Hazard Ratio

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The hazard ratio (HR) is a widely used measure in clinical trials to compare the risk of events, such as death or disease recurrence, between two groups over time. It reflects the ratio of hazard rates—the instantaneous risk of the event occurring—between a treatment group and a control group. This measure provides valuable insights into the relative effectiveness of a treatment by assessing how the risk of an event differs between the two groups.
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Related Experiment Video

Updated: Dec 31, 2025

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Wrangling with p-values versus effect sizes to improve medical decision-making: A tutorial.

Helena C Kraemer1, Eric Neri1, David Spiegel1

  • 1Department of Psychiatry and Behavioral Sciences, Stanford University, Cupertino, California.

The International Journal of Eating Disorders
|January 11, 2020
PubMed
Summary
This summary is machine-generated.

The p-value in clinical research is often misinterpreted. This study proposes using the success rate difference, or number needed to treat/take (NNT), to improve research design and avoid misleading conclusions.

Keywords:
p-valuesNNTROC curvesSRDeffect sizes

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Area of Science:

  • Clinical research methodology
  • Statistical inference in medicine

Background:

  • The common interpretation of p-values in clinical research is flawed, equating smaller p-values with stronger hypotheses.
  • P-values primarily indicate the quality of research design rather than the strength of evidence.
  • While effect sizes are recommended to prevent misleading conclusions, their appropriate application remains unclear.

Purpose of the Study:

  • To address the misuse of p-values in clinical research.
  • To propose a practical solution for effect size calculation in common clinical research scenarios.
  • To introduce the success rate difference (SRD) and number needed to treat/take (NNT) as key metrics.

Main Methods:

  • Focuses on the common clinical research problem of comparing two populations.
  • Proposes the success rate difference (SRD) as a key metric.
  • Highlights the equivalence of SRD to the number needed to treat/take (NNT).

Main Results:

  • The p-value's limitation in reflecting hypothesis strength is demonstrated.
  • The success rate difference (SRD) is presented as a robust effect size measure.
  • SRD is shown to be equivalent to the number needed to treat/take (NNT).

Conclusions:

  • Misinterpretation of p-values can damage clinical research integrity.
  • The success rate difference (SRD) offers a clear and interpretable effect size for comparing two populations.
  • Adoption of SRD/NNT can lead to more accurate conclusions in clinical trials and epidemiological studies.