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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 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.
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Sample Size Calculation01:19

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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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Randomized Experiments

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The randomization process involves assigning study participants randomly to experimental or control groups based on their probability of being equally assigned. Randomization is meant to eliminate selection bias and balance known and unknown confounding factors so that the control group is similar to the treatment group as much as possible. A computer program and a random number generator can be used to assign participants to groups in a way that minimizes bias.
Simple randomization
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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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Random Sampling Method01:09

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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. Among the various sampling methods used by...
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Methods for sample size determination in cluster randomized trials.

Clare Rutterford1, Andrew Copas2, Sandra Eldridge3

  • 1Centre for Primary Care and Public Health, Barts and the London School of Medicine and Dentistry, Queen Mary University of London, London, UK and c.m.rutterford@qmul.ac.uk.

International Journal of Epidemiology
|July 16, 2015
PubMed
Summary
This summary is machine-generated.

Calculating sample sizes for cluster randomized trials (CRTs) requires careful consideration beyond simple inflation. This resource details various methods for accurate sample size determination in CRTs, addressing complex designs and analyses.

Keywords:
Sample sizecluster randomizationdesign effect

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Area of Science:

  • Biostatistics
  • Clinical Trials Methodology
  • Epidemiology

Background:

  • Cluster randomized trials (CRTs) are increasingly utilized, exhibiting diverse designs and analytical approaches.
  • Traditional sample size calculations for CRTs often rely on a simplified design effect, which may not always be appropriate.
  • Advanced methods are necessary when the assumptions of simple design effects are not met.

Purpose of the Study:

  • To provide a comprehensive summary of available sample size calculation methods for cluster randomized trials (CRTs).
  • To offer practical guidance and formulae for various CRT scenarios, catering to researchers with differing levels of experience.
  • To serve as a valuable resource for designing robust CRTs.

Main Methods:

  • Systematic review and synthesis of existing sample size methodologies for CRTs.
  • Presentation of formulae and explanations for a wide range of scenarios, from simple to complex designs.
  • Categorization of methods based on design features, outcome types, and analysis strategies.

Main Results:

  • Detailed methods for the basic two-arm, parallel-group, completely randomized design are presented.
  • Methods are extended to accommodate variations such as unequal cluster sizes, attrition, and non-compliance.
  • Approaches for incorporating baseline covariates and repeated measures, as well as alternative trial designs, are discussed.

Conclusions:

  • A substantial body of methodology exists for CRT sample size calculations.
  • This paper offers the most extensive compilation of published methods for CRT sample size determination.
  • The manuscript serves as a critical resource for researchers engaged in the design of cluster randomized trials.