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May 15, 2000Statistics in Medicine1,687 citations

Bootstrap confidence intervals: when, which, what? A practical guide for medical statisticians

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JCJames R. CarpenterJBJohn F. Bithell

Key Points

  • The article aims to clarify when and how to appropriately use bootstrap confidence intervals in medical statistics.
  • Reviews common algorithms for resampling and confidence interval construction.
  • Presents a simulation study to explore different methods.
  • Includes a flow chart for selecting suitable methods with a practical survival analysis example.
  • Highlights the strengths and weaknesses of various bootstrap methods.
  • Demonstrates how to implement the chosen methods effectively.
  • Provides guidance for selecting the appropriate bootstrap approach based on specific scenarios.

Abstract

Since the early 1980s, a bewildering array of methods for constructing bootstrap confidence intervals have been proposed. In this article, we address the following questions. First, when should bootstrap confidence intervals be used. Secondly, which method should be chosen, and thirdly, how should it be implemented. In order to do this, we review the common algorithms for resampling and methods for constructing bootstrap confidence intervals, together with some less well known ones, highlighting their strengths and weaknesses. We then present a simulation study, a flow chart for choosing an appropriate method and a survival analysis example.

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Cite This Study

Carpenter et al. (2000) studied this question.

synapsesocial.com/papers/69d6a8bfa0177bf533ed87e3https://doi.org/10.1002/(sici)1097-0258(20000515)19:9<1141::aid-sim479>3.0.co;2-f
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