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Related Concept Videos

Sign Test for Matched Pairs01:17

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The sign test for matched pairs offers a robust method for comparing two paired samples, often for the effects of an intervention in one of them. This method is very useful in situations where the underlying distribution of the data is unknown. The test compares two related samples—often pre- and post-treatment measurements on the same subjects—to determine if there are significant differences in their median values.
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One-Way ANOVA can be performed on three or more samples with equal or unequal sample sizes. When one-way ANOVA is performed on two datasets with samples of equal sizes, it can be easily observed that the computed F statistic is highly sensitive to the sample mean.
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Sample size determination for a matched-pairs study with incomplete data using exact approach.

Guogen Shan1, Charles Bernick2, Sarah Banks2

  • 1Epidemiology and Biostatistics Program, Department of Environmental and Occupational Health, School of Community Health Sciences, University of Nevada Las Vegas, Nevada, USA.

The British Journal of Mathematical and Statistical Psychology
|July 1, 2017
PubMed
Summary
This summary is machine-generated.

This study introduces an exact unconditional approach for sample size calculation in matched-pairs cognitive studies with missing data. This method offers more accurate results than asymptotic approaches for small to medium sample sizes.

Keywords:
cognitive studyexact approachincomplete datamissing completely at randompaired datasample size

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Area of Science:

  • Biostatistics
  • Clinical Trial Design
  • Cognitive Science

Background:

  • Pilot studies for cognitive research often employ matched-pairs designs.
  • Missing data, particularly at the study's end, is common in such designs.
  • Current sample size calculations rely on asymptotic methods like generalized estimating equations (GEE), which may be inaccurate for small to medium sample sizes.

Purpose of the Study:

  • To develop and evaluate an exact unconditional approach for sample size computation in matched-pairs studies with incomplete data.
  • To compare the performance of the exact approach against the GEE approach for various missingness patterns.
  • To provide practical recommendations for sample size determination in real-world clinical trial designs.

Main Methods:

  • The study proposes an exact unconditional method for sample size calculation.
  • It considers matched-pairs designs with data missing at the end of the study.
  • Three common test statistics are used to compare exact sample sizes with the GEE approach.

Main Results:

  • Asymptotic methods (GEE) may yield unsatisfactory Type I and II error rates for small to medium sample sizes.
  • The exact unconditional approach provides more appropriate sample size recommendations for incomplete matched-pairs data.
  • Comparisons highlight differences in sample size requirements based on the chosen test statistic and missingness pattern.

Conclusions:

  • The exact unconditional approach is recommended for sample size calculation in matched-pairs studies with missing data, especially when sample sizes are small to medium.
  • The findings offer guidance for designing cognitive studies and other clinical trials with similar data characteristics.
  • Accurate sample size determination is crucial for ensuring adequate statistical power and reliable study outcomes.