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Related Concept Videos

Sample Size Calculation01:19

Sample Size Calculation

6.4K
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...
6.4K
One-Way ANOVA: Equal Sample Sizes01:15

One-Way ANOVA: Equal Sample Sizes

4.0K
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.
Different sample means can result in different values for the variance estimate: variance between samples. This is because the variance between samples is calculated as the product of the sample size and the variance between the...
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One-Way ANOVA: Unequal Sample Sizes01:15

One-Way ANOVA: Unequal Sample Sizes

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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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Power01:08

Power

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The concept of work involves force and displacement; meanwhile, the work-energy theorem relates the net work done on a body to the difference in its kinetic energy, calculated between two points on its trajectory. While none of these quantities or relations involves time explicitly, we know that the time available to accomplish work is often just as important as the amount of work itself. For example, sprinters in a race may have achieved the same velocity at the finish, therefore,...
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What are Estimates?01:06

What are Estimates?

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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...
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Nuclear Power02:36

Nuclear Power

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Controlled nuclear fission reactions are used to generate electricity. Any nuclear reactor that produces power via the fission of uranium or plutonium by bombardment with neutrons has six components: nuclear fuel consisting of fissionable material, a nuclear moderator, a neutron source, control rods, reactor coolant, and a shield and containment system.
Nuclear Fuels
Nuclear fuel consists of a fissile isotope, such as uranium-235, which must be present in sufficient quantity to provide a...
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Related Experiment Videos

Estimation of sample size and testing power (part 2).

Liang-ping Hu1, Xiao-lei Bao, Li-xin Tao

  • 1Consulting Center of Biomedical Statistics, Academy of Military Medical Sciences, Beijing 100850, China. lphu812@sina.com

Zhong Xi Yi Jie He Xue Bao = Journal of Chinese Integrative Medicine
|November 18, 2011
PubMed
Summary

This study defines non-inferiority, equivalence, and superiority tests for drug efficacy. It details sample size estimation methods and SAS implementation for these crucial clinical trial designs.

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Area of Science:

  • Clinical Trials
  • Biostatistics
  • Pharmaceutical Research

Background:

  • Determining drug efficacy requires rigorous statistical testing.
  • Specialized tests like non-inferiority, equivalence, and superiority are essential in clinical trials.
  • Accurate sample size estimation is critical for the validity and power of these tests.

Purpose of the Study:

  • To define and differentiate non-inferiority, equivalence, and superiority tests in drug development.
  • To present methodologies for sample size estimation tailored to each test type.
  • To demonstrate the practical application of these methods using SAS software.

Main Methods:

  • Detailed definitions of non-inferiority, equivalence, and superiority tests.
  • Formulas and procedures for sample size calculation under different test conditions.
  • Illustrative examples and SAS code for implementing sample size estimations.

Main Results:

  • Clear distinctions between the three special tests based on efficacy comparisons.
  • Accessible formulas for calculating sample sizes for each test scenario.
  • Practical SAS code enabling researchers to perform sample size estimations.

Conclusions:

  • Understanding these special tests is fundamental for designing robust clinical trials.
  • Appropriate sample size calculation ensures reliable assessment of experimental drug efficacy.
  • The provided SAS examples facilitate the implementation of these statistical methods in pharmaceutical research.