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Related Concept Videos

Bootstrapping01:24

Bootstrapping

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The term "bootstrap" originated in the 19th century as a metaphor for self-improvement or achieving something independently, without external assistance. This concept extends to statistical bootstrapping, a self-contained method for estimating population parameters through resampling, even though it can be computationally intensive. Developed by the American statistician Dr. Bradley Efron in 1979, bootstrapping provides a robust way to perform inference when the original sample size is...
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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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Randomized Experiments01:13

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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Testing a Claim about Population Proportion01:24

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A complete procedure for testing a claim about a population proportion is provided here.
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Types of Biopharmaceutical Studies: Controlled and Non-Controlled Approaches01:23

Types of Biopharmaceutical Studies: Controlled and Non-Controlled Approaches

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Biopharmaceutical studies constitute a vital field aiming to enhance drug delivery methods and refine therapeutic approaches, drawing upon diverse interdisciplinary knowledge. In research methodologies, the choice between controlled and non-controlled studies significantly influences the study's reliability and accuracy.
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Study Design in Statistics01:15

Study Design in Statistics

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A study design is a set of techniques that allow a researcher to collect and analyze data from different variables defined for a specific research problem. Statistics is commonly for effective study design and more robust experiments,
Does aspirin reduce the risk of heart attacks? Is one brand of fertilizer more effective at growing roses than another? Is fatigue as dangerous to a driver as the influence of alcohol? Questions like these are answered using randomized experiments with proper...
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Related Experiment Video

Updated: Jan 11, 2026

An R-Based Landscape Validation of a Competing Risk Model
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Calculating Power by Bootstrap, with an Application to Cluster-Randomized Trials.

Ken Kleinman1, Susan S Huang2

  • 1Department of Biostatistics and Epidemiology, University of Massachusetts Amherst School of Public Health and Health Sciences; Department of Population Medicine, Harvard Medical School.

EGEMS (Washington, DC)
|March 18, 2017
PubMed
Summary

Bootstrap power calculation offers a more accurate alternative to traditional methods for complex study designs like cluster-randomized trials. This resampling approach mimics data analysis, improving power calculations without external parameter estimates.

Keywords:
2014 Group Health Seattle SymposiumPower and sample sizecluster randomized trials, bootstrapresampling

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Area of Science:

  • Statistical methodology
  • Clinical trial design
  • Resampling techniques

Background:

  • Accurate power calculations are crucial for study design, ideally mimicking the planned data analysis.
  • Analytic solutions for power calculations can be difficult for complex designs like cluster-randomized trials.
  • Monte Carlo methods, including simulation and bootstrap resampling, offer attractive alternatives.

Purpose of the Study:

  • To propose and describe bootstrap approaches for power calculation, particularly for complex study designs.
  • To demonstrate the implementation of bootstrap power calculation for cluster-randomized trials.

Main Methods:

  • Bootstrap resampling involves resampling real data to create datasets that resemble the intended analyses.
  • This contrasts with simulation methods, which estimate parameters from real data and then simulate new data.
  • The study details the implementation of bootstrap power calculation methods.

Main Results:

  • Bootstrap power calculation was demonstrated for a cluster-randomized trial with censored survival outcomes and baseline data.
  • Simulation studies confirmed that bootstrap power calculation can replicate analytic power where analytic methods are accurate.
  • The method successfully calculated power for correlated censored survival outcomes in cluster-randomized trials.

Conclusions:

  • Bootstrap power calculation is a simple, accurate resampling-based solution, avoiding assumptions of analytic methods.
  • It offers high fidelity to planned data analysis and eliminates the need for external parameter estimates.
  • This method is particularly useful when preliminary data are available, such as from electronic medical records.