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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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Fisher's Exact Test01:08

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Fisher's exact test is a statistical significance test widely used to analyze 2x2 contingency tables, particularly in situations where sample sizes are small. Unlike the chi-squared test, which approximates P-values and assumes minimum expected frequencies of at least five in each cell, Fisher's exact test calculates the exact probability (P-value) of observing the data or more extreme results under the null hypothesis. This feature makes it especially valuable when the assumptions of...
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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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Testing a Claim about Mean: Unknown Population SD01:21

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A complete procedure of testing a hypothesis about a population mean when the population standard deviation is unknown is explained here.
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Accuracy and Errors in Hypothesis Testing01:13

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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%...
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Decision Making: P-value Method01:09

Decision Making: P-value Method

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The process of hypothesis testing based on the P-value method includes calculating the P- value using the sample data and interpreting it.
First, a specific claim about the population parameter is proposed. The claim is based on the research question and is stated in a simple form. Further, an opposing statement to the claim  is also stated. These statements can act as null and alternative hypotheses:  a null hypothesis would be a neutral statement while the alternative hypothesis can...
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Power and sample size determination for noninferiority trials using an exact method.

Ivan S F Chan1

  • 1Clinical Biostatistics, Merck Research Laboratories, UN-A102, PO Box 4, West Point, PA 19486, USA. ivan_chan@merck.com

Journal of Biopharmaceutical Statistics
|December 13, 2002
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Exact methods offer a reliable approach for planning noninferiority trials, especially when dealing with small sample sizes or sparse data. These methods ensure accurate sample size and power calculations for new drugs and vaccines.

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Area of Science:

  • Biostatistics
  • Clinical Trials

Background:

  • Noninferiority studies are crucial for evaluating new drugs and vaccines against standard treatments.
  • Existing asymptotic methods may falter with small sample sizes or skewed/sparse data.

Purpose of the Study:

  • To explore and develop exact methods for sample size and power calculations in noninferiority studies.
  • To address limitations of asymptotic methods in specific clinical trial scenarios.

Main Methods:

  • Developed methodology for sample size and power calculations based on an exact unconditional test of noninferiority.
  • Focused on noninferiority studies analyzing the difference between two proportions.

Main Results:

  • The proposed exact unconditional method demonstrates strong performance across various scenarios.
  • This method shows favorable sensitivity compared to asymptotic counterparts.

Conclusions:

  • Exact methods are a desirable tool for planning noninferiority trials, particularly when asymptotic methods are unreliable.
  • The developed method is suitable for clinical trial examples, such as in childhood nephroblastoma.