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Related Concept Videos

Sample Size Calculation01:19

Sample Size Calculation

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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.
The sample size for the given experiment or sampling effort is fundamental to any study design. Sample size decides the number of...
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Testing a Claim about Population Proportion01:24

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A complete procedure for testing a claim about a population proportion is provided here.
There are two methods of testing a claim about a population proportion: (1) Using the sample proportion from the data where a binomial distribution is approximated to the normal distribution and (2) Using the binomial probabilities calculated from the data.
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Sample Proportion and Population Proportion01:20

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Collecting samples or responses from an entire population takes significant time and effort, so a researcher collects responses from only a sample of that population. Suppose a study needs to collect information about a specific mobile application. After sample collection, the researcher analyzes the data and discovers that most individuals in the sample use that specific mobile application. The sample proportion measures the number of individuals in a sample who either use or don't use the...
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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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One-Way ANOVA: Unequal Sample Sizes01:15

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One-way ANOVA can be performed on three or more samples of unequal sizes. However, calculations get complicated when sample sizes are not always the same. So, while performing ANOVA with unequal samples size, the following equation is used:
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Given simple random samples of size n from a given population with a measured characteristic such as mean, proportion, or standard deviation for each sample, the probability distribution of all the measured characteristics is called a sampling distribution. How much the statistic varies from one sample to another is known as the sampling variability of a statistic. You typically measure the sampling variability of a statistic by its standard error. The standard error of the mean is an example...
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A Within-Subject Experimental Design using an Object Location Task in Rats
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Sample size formula for general win ratio analysis.

Lu Mao1, KyungMann Kim1, Xinran Miao1

  • 1Department of Biostatistics and Medical Informatics, University of Wisconsin-Madison, Madison, Wisconsin.

Biometrics
|May 28, 2021
PubMed
Summary
This summary is machine-generated.

This study introduces new formulas for sample size estimation in win ratio analysis, a method for comparing outcomes in clinical trials. These accurate, easy-to-use formulas address a gap in statistical methodology for diverse outcome types.

Keywords:
U-statisticWilcoxon-Mann-Whitney testcomposite outcomespartial orderpower analysisstandard rank deviation

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Area of Science:

  • Biostatistics
  • Clinical Trial Design
  • Statistical Methodology

Background:

  • The win ratio method, initially for composite endpoints, now uses general pairwise comparisons.
  • Sample size estimation for win ratio analysis is complex due to non-i.i.d. test statistics.

Purpose of the Study:

  • To develop general, user-friendly formulas for sample size calculation in win ratio analysis.
  • To address the need for accurate sample size estimation across various outcome types.

Main Methods:

  • Derivation of null variance using U-statistic theory and standard rank deviation.
  • Formulation of effect sizes on original or outcome-specific scales (e.g., odds, hazard ratios).

Main Results:

  • Developed accurate, generalizable formulas for win ratio sample size estimation.
  • Formulas validated through simulation studies across diverse settings.
  • Demonstrated application using pilot data from hepatic and cardiovascular disease trials.

Conclusions:

  • The proposed formulas provide reliable sample size estimates for win ratio analyses.
  • Facilitates robust clinical trial design and planning for various outcome measures.