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Related Concept Videos

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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 small or...
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An R-Based Landscape Validation of a Competing Risk Model
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Published on: September 16, 2022

Non-parametric methods for cost-effectiveness analysis: the central limit theorem and the bootstrap compared.

Richard M Nixon1, David Wonderling, Richard D Grieve

  • 1MRC Biostatistics Unit, Institute of Public Health, University Forvie Site, Robinson Way, Cambridge, UK. richard.nixon@mrc-bsu.cam.ac.uk

Health Economics
|April 21, 2009
PubMed
Summary
This summary is machine-generated.

For cost-effectiveness analyses, the central limit theorem (CLT) and bootstrap methods both accurately estimate uncertainty. The CLT is simpler and provides equally accurate standard errors, even with skewed data.

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Area of Science:

  • Health Economics
  • Biostatistics
  • Clinical Trials

Background:

  • Cost-effectiveness analysis (CEA) alongside randomized controlled trials commonly estimates incremental net benefits (INB).
  • Non-parametric methods like the central limit theorem (CLT) and bootstrap are used to estimate INB uncertainty.
  • The preference between CLT and bootstrap for INB estimation is unclear.

Purpose of the Study:

  • To describe the statistical rationale for CLT and bootstrap methods in CEA.
  • To compare the sampling uncertainty estimates from CLT and bootstrap.
  • To evaluate method performance under varying sample sizes and cost distributions.

Main Methods:

  • Application of CLT and bootstrap methods to trial-based CEA.
  • Monte Carlo simulation to compare sampling uncertainty.
  • Experiments varied sample size and skewness of cost data.

Main Results:

  • Both CLT and bootstrap accurately estimated standard errors (SEs) with moderate to large sample sizes (n>50), even with highly skewed data.
  • Both methods provided good estimates for small datasets with low skewness.
  • CLT yielded slightly more accurate SEs than bootstrap for small sample sizes with highly skewed data.

Conclusions:

  • Both CLT and bootstrap are appropriate for estimating INB uncertainty in CEA.
  • The CLT method is easier to implement.
  • CLT provides standard errors that are at least as accurate as the bootstrap method.