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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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One-Way ANOVA: Equal Sample Sizes01:15

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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.
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Sampling Plans01:23

Sampling Plans

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Sampling is a crucial step in analytical chemistry, allowing researchers to collect representative data from a large population. Common sampling methods include random, judgmental, systematic, stratified, and cluster sampling.
Random sampling is a method where each member of the population has an equal chance of being selected for the sample. It involves selecting individuals randomly, often using random number generators or lottery-type methods. For example, when analyzing the properties of a...
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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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Systematic Sampling Method01:17

Systematic Sampling Method

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Sampling is a technique to select a portion (or subset) of the larger population and study that portion (the sample) to gain information about the population. Data are the result of sampling from a population. The sampling method ensures that samples are drawn without bias and accurately represent the population. Because measuring the entire population in a study is not practical, researchers use samples to represent the population of interest.
Systematic sampling is one of the simplest methods...
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Cluster Sampling Method01:20

Cluster Sampling Method

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Appropriate sampling methods ensure that samples are drawn without bias and accurately represent the population. Because measuring the entire population in a study is not practical, researchers use samples to represent the population of interest.
To choose a cluster sample, divide the population into clusters (groups) and then randomly select some of the clusters. All the members from these clusters are in the cluster sample. For example, if you randomly sample four departments from your...
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A Machine Learning Approach to Design an Efficient Selective Screening of Mild Cognitive Impairment
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Stepwise two-stage sample size adaptation.

Hong Wan1, Susan S Ellenberg, Keaven M Anderson

  • 1Shire, 735 Chesterbrook Blvd., Wayne, 19087-5637, PA, U.S.A.

Statistics in Medicine
|September 25, 2014
PubMed
Summary
This summary is machine-generated.

This study introduces a novel stepwise two-stage sample size adaptation method for clinical trials. It improves interim treatment effect blinding while maintaining statistical power and efficiency.

Keywords:
adaptive designoptimal designsample size reestimation

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Area of Science:

  • Biostatistics
  • Clinical Trial Design
  • Statistical Methods

Background:

  • Adaptive clinical trial designs allow sample size reestimation based on interim data.
  • Some existing methods risk unblinding treatment effects at interim analyses.
  • Preserving the overall type I error rate is crucial during sample size reestimation.

Purpose of the Study:

  • To propose a novel adaptive sample size reestimation method that enhances blinding of interim treatment effects.
  • To evaluate the statistical properties and efficiency of the proposed stepwise two-stage sample size adaptation.
  • To compare the new method against existing adaptive and group sequential designs.

Main Methods:

  • Development of a stepwise two-stage sample size adaptation using an inverted U-shaped step function for treatment difference.
  • Application of group sequential design calculation methods for sample size adjustments.
  • Minimization of expected sample size across a class of designs.
  • Comparative analysis with fully optimized two-stage designs and those based on conditional power.

Main Results:

  • The proposed method effectively lessens the information on treatment effect revealed at the interim analysis.
  • Efficiency of the stepwise design is evaluated against other adaptive and group sequential designs.
  • The trade-off between improved blinding and statistical efficiency is quantified.

Conclusions:

  • Stepwise two-stage sample size adaptation offers a valuable alternative for adaptive clinical trials.
  • This method balances the need for efficient sample size reestimation with the critical requirement of maintaining treatment effect blinding.
  • The findings support the use of this method in clinical trial design to improve trial integrity.