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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

Testing a Claim about Population Proportion

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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.
The first method uses normal distribution as an approximation to the binomial distribution. The requirements are as follows: sample size is large...
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Sample Proportion and Population Proportion01:20

Sample Proportion and Population Proportion

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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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Contaminants and Errors01:16

Contaminants and Errors

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Effective sample preparation is crucial for accurate and reliable laboratory analysis. During this process, two significant sources of error can arise: concentration bias from improper sample splitting and contamination caused by methods used to reduce particle size, such as grinding or homogenization. Identifying and minimizing these potential errors is crucial to ensuring the validity of the analysis.
Another key consideration is determining the appropriate number of samples required to...
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Estimating Population Mean with Known Standard Deviation01:16

Estimating Population Mean with Known Standard Deviation

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To construct a confidence interval for a single unknown population mean μ, where the population standard deviation is known, we need sample mean as an estimate for μ and we need the margin of error. Here, the margin of error (EBM) is called the error bound for a population mean (abbreviated EBM). The sample mean is the point estimate of the unknown population mean μ.
The confidence interval estimate will have the form as follows:
(point estimate - error bound, point estimate +...
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Margin of Error01:27

Margin of Error

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The margin of error is also called the maximum error of an estimate. The margin of error is the maximum possible or expected difference between the observed sample parameter value and the actual population parameter value. For proportion, it is the maximum difference between the value of sample proportion obtained from the data and the true value of population proportion. As the true value of the population parameter is not known, the margin of error is calculated using the sample statistic.
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Sample size planning for estimating the global win probability with precision and assurance.

Di Shu1, Guangyong Zou2

  • 1Department of Biostatistics, Epidemiology and Informatics, University of Pennsylvania Perelman School of Medicine, Philadelphia, PA, USA; Clinical Futures, Children's Hospital of Philadelphia, Philadelphia, PA, USA.

Contemporary Clinical Trials
|August 23, 2024
PubMed
Summary

This study introduces a new sample size formula for clinical trials assessing multiple disease aspects using global win probability (WinP). The formula ensures desired precision and assurance for intervention effects, aiding trial design.

Keywords:
Area under the receiver operating characteristic curveCohen's effect sizeMultiple endpointsSample size calculationWilcoxon-Mann-Whitney testWin probability

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Area of Science:

  • Biostatistics
  • Clinical Trial Design
  • Medical Research

Background:

  • Randomized controlled trials (RCTs) often use multiple endpoints to evaluate interventions comprehensively.
  • Estimating the overall treatment effect across various disease aspects is crucial for accurate assessment.

Purpose of the Study:

  • To propose a closed-form sample size formula for global win probability (WinP) in RCTs with multiple endpoints.
  • To incorporate pre-specified precision and assurance into the sample size calculation for global WinP.
  • To provide a method that accounts for unequal variances between treatment groups.

Main Methods:

  • The study defines global WinP as the mean of individual endpoint WinPs, representing the probability of a treated participant outperforming a control participant.
  • It leverages the equivalence between WinP and the area under the receiver operating characteristic curve (AUC).
  • A sample size formula is adapted from methods used for comparing two AUCs, allowing for unequal variances.

Main Results:

  • The proposed closed-form sample size formula effectively estimates the global win probability.
  • Simulation results indicate that the method performs well in practice.
  • The formula is illustrated with a practical example in Parkinson's disease clinical trial design.

Conclusions:

  • The developed sample size formula offers a precise and reliable method for planning RCTs with multiple endpoints using global WinP.
  • This approach enhances the statistical rigor for evaluating interventions across diverse disease outcomes.
  • The method is applicable to various therapeutic areas, including neurological disorders like Parkinson's disease.