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Related Concept Videos

One-Way ANOVA: Equal Sample Sizes01:15

One-Way ANOVA: Equal Sample Sizes

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...
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:
Bonferroni Test01:10

Bonferroni Test

The Bonferroni test is a statistical test named after Carlo Emilio Bonferroni, an Italian mathematician best known for Bonferroni inequalities. This statistical test is a type of multiple comparison test to determine which means are different than the rest. Bonferroni test can minimize the Type 1 error by reducing the significance level alpha, which otherwise increases with sample pairs.
The means of different samples are first paired in all possible combinations.
The null hypothesis of the...
Testing a Claim about Mean: Unknown Population SD01:21

Testing a Claim about Mean: Unknown Population SD

A complete procedure of testing a hypothesis about a population mean when the population standard deviation is unknown is explained here.
Estimating a population mean requires the samples to be approximately normally distributed. The data should be collected from the randomly selected samples having no sampling bias. There is no specific requirement for sample size. But if the sample size is less than 30, and we don't know the population standard deviation, a different approach is used; instead...
Testing a Claim about Mean: Known Population SD01:11

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A complete procedure of testing the hypothesis about a population mean is explained here.
Estimating a population mean requires the samples to be distributed normally. The data should be collected from the randomly selected samples having no sampling bias. The sample size needed to be higher than 30, and most importantly, the population standard deviation should be already known.
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Comparing Experimental Results: Student's t-Test01:09

Comparing Experimental Results: Student's t-Test

The t-test is a statistical method used to compare the sample mean with a population mean or compare two means from two data sets. The test statistic is calculated from the standard deviation, mean, and number of measurements in the data set at a selected confidence interval and then compared to a table of critical values at this confidence level. If the test statistic is smaller than the critical value, the null hypothesis is accepted. In this case, we state that the difference between the...

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Adaptive blinded sample size adjustment for comparing two normal means--a mostly Bayesian approach.

Andrew M Hartley1

  • 1PPD, Biostatistics, Wilmington, NC, USA. khahstats@yahoo.com.

Pharmaceutical Statistics
|March 17, 2012
PubMed
Summary
This summary is machine-generated.

This study introduces a new blinded sample size redetermination (SSR) method for clinical trials. It improves accuracy by combining trial data with prior knowledge, reducing errors in sample size calculations.

Keywords:
Bayesian analysisadaptive designsclinical trialssample sizesample size re-estimation

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Area of Science:

  • Clinical Trials Methodology
  • Biostatistics
  • Statistical Inference

Background:

  • Adaptive sample size redetermination (SSR) adjusts trial parameters using early data.
  • Blinded SSR is increasingly popular due to logistical advantages and minimal bias.
  • Existing blinded SSR methods may use inaccurate treatment effect (TE) estimates.

Purpose of the Study:

  • To propose a novel blinded SSR method for clinical trials.
  • To formally integrate on-trial data with prior knowledge of TE and variance.
  • To evaluate the performance of the proposed method.

Main Methods:

  • Developed a blinded SSR approach synthesizing sample data and prior knowledge.
  • Evaluated type 1 error rate, bias in TE estimation, and deviation from targeted power.
  • Compared the proposed method against an established SSR technique.

Main Results:

  • The proposed blinded SSR method reduces average deviation from targeted power.
  • Demonstrated effectiveness across various simulated scenarios.
  • The method offers a more reliable approach to sample size adjustment.

Conclusions:

  • The novel blinded SSR method provides a statistically sound way to adjust sample sizes.
  • It mitigates the risk of inaccurate TE estimates in blinded SSR.
  • This approach enhances the efficiency and reliability of clinical trial design.