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Related Concept Videos

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The randomization process involves assigning study participants randomly to experimental or control groups based on their probability of being equally assigned. Randomization is meant to eliminate selection bias and balance known and unknown confounding factors so that the control group is similar to the treatment group as much as possible. A computer program and a random number generator can be used to assign participants to groups in a way that minimizes bias.
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Wald-Wolfowitz Runs Test I01:17

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The Wald-Wolfowitz test, also known as the runs test, is a nonparametric statistical test used to assess the randomness of a sequence of two different types of elements (e.g., positive/negative values, successes/failures). It examines whether the order of the elements in a sequence is random or if there is a pattern or trend present. This nonparametric test applies to any ordered data despite the population and sample data distribution, even if a higher sample size is available.
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Wald-Wolfowitz Runs Test II01:17

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The Wald-Wolfowitz runs test, commonly referred to as the runs test, is a nonparametric test used to assess the randomness of ordered data. The test evaluates the number of runs, which are consecutive sequences of similar elements within the data. If the number of runs is significantly higher or lower than expected, the data is considered non-random, indicating a detectable pattern or structure.
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McNemar's Test01:23

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McNemar's Test is a nonparametric statistical test used to determine if there is a significant difference in proportions between two related groups when the outcome is binary (e.g., yes/no, success/failure). It is beneficial when we have paired data, such as pre-test/post-test designs, where the same subjects are measured under two different conditions. The test is named after the statistician Quinn McNemar, who introduced it in 1947. It is commonly used in situations where subjects are...
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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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Test for Homogeneity01:23

Test for Homogeneity

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The goodness–of–fit test can be used to decide whether a population fits a given distribution, but it will not suffice to decide whether two populations follow the same unknown distribution. A different test, called the test for homogeneity, can be used to conclude whether two populations have the same distribution. To calculate the test statistic for a test for homogeneity, follow the same procedure as with the test of independence. The hypotheses for the test for homogeneity can...
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Frequency and Distribution of Crossovers in Caenorhabditis elegans Meiosis by SNP Genotyping using Real-time PCR
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swpermute: Permutation tests for Stepped-Wedge Cluster-Randomised Trials.

Jennifer Thompson1, Calum Davey1, Richard Hayes1

  • 1London School of Hygiene and Tropical Medicine London, UK.

The Stata Journal
|June 23, 2020
PubMed
Summary
This summary is machine-generated.

Permutation tests are crucial for stepped-wedge trials. The new swpermute command correctly permutes clusters, ensuring valid statistical tests for intervention effects in stepped-wedge study designs.

Keywords:
cluster-randomisedpermutation testrandomisation testrandomization testst0001stepped-wedgeswpermute

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Area of Science:

  • Biostatistics
  • Epidemiology
  • Clinical Trials

Background:

  • Permutation tests offer robust statistical analysis for intervention effects in stepped-wedge trials.
  • Existing methods like the Stata 'permute' command are inadequate for stepped-wedge designs due to non-exchangeable observations.

Purpose of the Study:

  • Introduce the 'swpermute' command for valid permutation testing in stepped-wedge trials.
  • Provide enhanced functionality for analyzing stepped-wedge trial data.

Main Methods:

  • The 'swpermute' command permutes clusters to sequences, preserving exchangeability.
  • Includes a 'withinperiod' option for period-specific analyses and weighted averaging of estimates.
  • Supports testing non-zero null hypotheses for confidence interval construction.

Main Results:

  • Demonstrates the correct application of permutation tests in stepped-wedge trials using the 'swpermute' command.
  • Validates intervention-effect estimates by maintaining cluster exchangeability.

Conclusions:

  • The 'swpermute' command provides a valid and versatile tool for statistical analysis in stepped-wedge trials.
  • Facilitates robust estimation and inference of intervention effects, including confidence interval construction.