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A complete procedure to test a claim about population standard deviation or population variance is explained here.
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Hypothesis testing is a fundamental statistical tool that begins with the assumption that the null hypothesis H0 is true. During this process, two types of errors can occur: Type I and Type II. A Type I error refers to the incorrect rejection of a true null hypothesis, while a Type II error involves the failure to reject a false null hypothesis.
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Body:Bioequivalence experimental study designs play a pivotal role in testing the effectiveness of various treatments. Key among these are the repeated measures, cross-over, carry-over, and Latin square designs. In the repeated measures design, each subject receives all treatments, allowing for temporal comparisons. This type of design is useful in reducing variability but requires careful planning to avoid bias.The cross-over design, an economical method, involves sequential administration of...
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The t-test is a statistical method used to compare the sample mean with a population mean or compare two means from two data sets. The test statistic is calculated from the standard deviation, mean, and number of measurements in the data set at a selected confidence interval and then compared to a table of critical values at this confidence level. If the test statistic is smaller than the critical value, the null hypothesis is accepted. In this case, we state that the difference between the...
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Once data is collected from both the experimental and the control groups, a statistical analysis is conducted to find out if there are meaningful differences between the two groups. A statistical analysis determines how likely any difference found is due to chance (and thus not meaningful). In psychology, group differences are considered meaningful, or significant, if the odds that these differences occurred by chance alone are 5 percent or less. Stated another way, if we repeated this...
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Choosing the target difference ('effect size') for a randomised controlled trial - DELTA2 guidance protocol.

Jonathan A Cook1, Steven A Julious2, William Sones3

  • 1Centre for Statistics in Medicine, Botnar Research Centre, Nuffield Department of Orthopaedics, Rheumatology and Musculoskeletal Sciences, University of Oxford, Nuffield Orthopaedic Centre, Windmill Road, Oxford, OX3 7LD, UK. jonathan.cook@ndorms.ox.ac.uk.

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|June 14, 2017
PubMed
Summary

The Difference ELicitation in TriAls (DELTA^2) project aims to improve sample size calculations for randomized controlled trials (RCTs). It will develop guidance on specifying the target difference, a crucial but often overlooked aspect of RCT design.

Keywords:
Clinically important differenceEffect sizeGuidancePilot studyRandomised controlled trialSample sizeTarget difference

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Area of Science:

  • Clinical Trials Methodology
  • Biostatistics
  • Health Research Ethics

Background:

  • Sample size estimation is critical for randomized controlled trials (RCTs), directly impacting statistical power and study conduct.
  • Specifying the target difference for the primary outcome is a key component of sample size calculation, yet it receives insufficient attention.
  • The current approach to determining target differences lacks standardized guidance, potentially affecting trial validity and resource allocation.

Framework:

  • The DELTA^2 project employs a multi-component framework to develop evidence-based guidance.
  • This includes systematic literature reviews on methodological advancements and funder policies.
  • A Delphi study and consensus meeting with diverse stakeholders will inform the final guidance.

Implementation:

  • The project involves systematic literature reviews (stages 1 & 2) to assess current methods and funder requirements.
  • A Delphi study (stage 3) will gather expert opinions on specifying target differences.
  • Consensus meetings and stakeholder engagement (stage 4) will refine recommendations before dissemination (stage 5).

Implications:

  • Improved guidance on target difference specification will enhance the rigor of RCT design.
  • This will lead to more reliable sample size calculations and better-resourced trials.
  • Clearer reporting standards will benefit researchers, funders, and ultimately, patient care.