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Related Concept Videos

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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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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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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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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Comparing the Survival Analysis of Two or More Groups01:20

Comparing the Survival Analysis of Two or More Groups

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Survival analysis is a cornerstone of medical research, used to evaluate the time until an event of interest occurs, such as death, disease recurrence, or recovery. Unlike standard statistical methods, survival analysis is particularly adept at handling censored data—instances where the event has not occurred for some participants by the end of the study or remains unobserved. To address these unique challenges, specialized techniques like the Kaplan-Meier estimator, log-rank test, and...
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Power and sample size for reversible linear mixed models with clustering and longitudinality: GLIMMPSE Version 3.

Deborah H Glueck1, Qian Li2, Alasdair J Macleod3

  • 1Department of Pediatrics, University of Colorado Denver, Aurora, Colorado, United States of America.

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GLIMMPSE Version 3 is a free, open-source software for calculating statistical power and sample size for complex study designs. This updated version enhances usability and accuracy for general linear mixed models, supporting diverse research needs.

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Area of Science:

  • Biostatistics
  • Statistical Software Development
  • Clinical Trial Design

Background:

  • General linear mixed models (GLMMs) are crucial for analyzing complex data structures in research.
  • Accurate power and sample size calculations are essential for efficient study design and resource allocation.
  • Existing software may have limitations in handling multilevel, longitudinal, or combined data structures.

Purpose of the Study:

  • To introduce GLIMMPSE Version 3, an updated, free, web-based, open-source software tool.
  • To enhance the calculation of power and sample size for general linear mixed models with Gaussian errors.
  • To provide a user-friendly interface and advanced features for complex study designs.

Main Methods:

  • Refactored back end in Python for improved performance.
  • Simplified user interface with single-topic screens for ease of use.
  • Implemented a recursive algorithm for computing covariances up to ten levels of clustering.
  • Utilized updated Monte Carlo simulations for accuracy validation.

Main Results:

  • GLIMMPSE Version 3 offers power calculations for studies with clustering, repeated measures, or both.
  • The software supports a wide range of testable hypotheses within various study designs.
  • Updated simulations demonstrate power approximation accuracy within 0.01.
  • Five new examples showcase applications in cluster-randomized trials, longitudinal, multilevel, and complex studies.

Conclusions:

  • GLIMMPSE Version 3 provides a robust and accessible tool for power and sample size calculations in complex research.
  • The software's new features and improved accuracy support the design of efficient and statistically sound studies.
  • Its widespread use and NIH funding underscore its importance in biomedical and experimental research.