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Related Concept Videos

Sample Size Calculation01:19

Sample Size Calculation

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...
Errors In Hypothesis Tests01:14

Errors In Hypothesis Tests

When performing a hypothesis test, there are four possible outcomes depending on the actual truth (or falseness) of the null hypothesis and the decision to reject or not.
Accuracy and Errors in Hypothesis Testing01:13

Accuracy and Errors in Hypothesis Testing

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.
In hypothesis testing, the probability of making a Type I error, denoted as α, is commonly set at 0.05. This significance level indicates a 5% chance...
What are Estimates?01:06

What are Estimates?

It isn't easy to measure a parameter such as the mean height or the mean weight of a population. So, we draw samples from the population and calculate the mean height or mean weight of the individuals in the sample. This sample data acts as a representative measure of the population parameter. These sample statistics are known as estimates. 
The estimate for the mean of a sample is denoted by ͞x, whereas the mean of the population is designated as μ. Further, parameters such as the mean,...
Testing a Claim about Population Proportion01:24

Testing a Claim about Population Proportion

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...
One-Way ANOVA: Unequal Sample Sizes01:15

One-Way ANOVA: Unequal Sample Sizes

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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Estimation of sample size and testing power (Part 3).

Liang-ping Hu1, Xiao-lei Bao, Xue Guan

  • 1Academy of Military Medical Sciences, Beijing, China. lphu812@sina.com

Zhong Xi Yi Jie He Xue Bao = Journal of Chinese Integrative Medicine
|December 14, 2011
PubMed
Summary

This study details sample size estimation for non-inferiority, equivalence, and superiority tests in clinical trials with binary outcomes. It provides practical formulas and SAS code for these essential statistical methods.

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Area of Science:

  • Biostatistics
  • Clinical Trial Design
  • Pharmaceutical Research

Background:

  • Accurate sample size estimation is crucial for the validity and efficiency of clinical trials.
  • Specialized statistical tests are required to evaluate drug efficacy beyond simple superiority.
  • Qualitative data with binary outcomes are common in clinical research.

Purpose of the Study:

  • To define and illustrate non-inferiority, equivalence, and superiority tests for binary data.
  • To provide formulas for sample size estimation for these three test types.
  • To demonstrate the practical implementation of these calculations using SAS.

Main Methods:

  • Description of study designs involving one factor with two levels and a binary response variable.
  • Formulation of sample size estimation equations for non-inferiority, equivalence, and superiority testing.
  • Application of SAS software for realizing the sample size calculations.

Main Results:

  • Detailed formulas for sample size estimation are presented for each of the three test types.
  • SAS code examples are provided for practical application in clinical trial planning.
  • The methodology facilitates appropriate sample size determination for various drug evaluation scenarios.

Conclusions:

  • This article offers a comprehensive guide to sample size estimation for key clinical trial comparison tests.
  • The provided methods and SAS implementations support researchers in designing robust clinical studies.
  • Understanding and applying these specialized tests ensures appropriate evaluation of experimental drug efficacy.