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Related Concept Videos

Odds Ratio01:09

Odds Ratio

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The odds ratio (OR) is a statistical measure used extensively in epidemiology and research to quantify the strength of association between exposure and outcome across different groups. Unlike relative risk, which compares the probabilities of an event occurring, the odds ratio compares the odds of an event occurring in the exposed group to the odds of it occurring in the unexposed group. The odds, in this context, are calculated as the probability of the event happening divided by the...
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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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A complete procedure for testing a claim about a population proportion is provided here.
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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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Knowledge of the sample size is the first requirement to conduct random sampling or an experiment. The sample size is the total number of units, observations, or groups (in some cases) used to get the data to estimate a population parameter. As the name suggests, the sample size is that of the sample drawn from the population and differs from the population size.
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A Clinical Trial Assessing the Safety, Efficacy, and Delivery of Olive-Oil-Based Three-Chamber Bags for Parenteral Nutrition
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Sample size considerations in active-control non-inferiority trials with binary data based on the odds ratio.

Arminda Lucia Siqueira1, Susan Todd2, Anne Whitehead3

  • 1Departamento de Estatística, Universidade Federal de Minas Gerais, Brazil arminda@est.ufmg.br.

Statistical Methods in Medical Research
|February 5, 2014
PubMed
Summary

This study provides approximate sample size formulas for binary non-inferiority trials using odds ratios. The score test formula is accurate for specific margins, while the Wald test formula also shows reasonable performance in similar scenarios.

Keywords:
binary datanon-inferiorityodds ratiosample size

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Area of Science:

  • Biostatistics
  • Clinical Trials
  • Statistical Methods

Background:

  • Non-inferiority trials are crucial for evaluating new treatments against existing ones.
  • Accurate sample size calculation is essential for the validity and efficiency of clinical trials.
  • Binary data analysis in non-inferiority trials often relies on measures like the odds ratio.

Purpose of the Study:

  • To present and evaluate approximate closed-form sample size formulas for non-inferiority trials with binary outcomes.
  • To compare a novel score test-based formula with a well-established Wald test-based formula.
  • To assess the accuracy of these formulas under various parameter settings.

Main Methods:

  • Derivation of a sample size formula based on the score test for non-inferiority using odds ratios.
  • Comparison of the score test formula with a Wald test-based formula.
  • Validation through simulations using the likelihood ratio test.

Main Results:

  • The score test closed-form formula demonstrates reasonable accuracy when non-inferiority margins are odds ratios >= 0.5 and the alternative odds ratio is between 1 and 2.5.
  • Formula accuracy decreases as the alternative odds ratio increases beyond 2.5.
  • Both score and Wald test formulas provide satisfactory sample size calculations within their validated ranges for binary non-inferiority trials.

Conclusions:

  • Approximate closed-form sample size formulas, particularly the score test-based one, can be valuable for non-inferiority trials with binary data.
  • Careful consideration of the non-inferiority margin and expected treatment effect is necessary for accurate sample size estimation.
  • Both presented formulas offer practical tools for sample size determination in relevant clinical trial settings.