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Ann Thorac Surg 2004;77:1142-1144
© 2004 The Society of Thoracic Surgeons

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The statistician's page

Bootstrap resampling methods: something for nothing?

^a Providence Health System, Portland, Oregon, USA

^* Address reprint requests to Dr Grunkemeier, Providence St. Vincent Hospital and Medical Center, 9205 SW Barnes Rd, Suite 33, Portland, OR 97225, USA
e-mail: gary.grunkemeier@providence.org

The first 300 words of the full text of this article appear below.

The paper by Brunelli and colleagues [1] in this issue of The Annals of Thoracic Surgery used bootstrap resampling to select the final variables for a logistic regression model to predict air leak after pulmonary lobectomy. Bootstrapping is a generic methodology, whose implementation involves a simple yet powerful principle: creating many repeated data samples from the single one in hand, and making inference from those samples. The name derives from the expression "pulling oneself up by one's bootstraps," meaning no outside help (additional data, parametric assumptions) is involved. It seems almost magical, like getting something for nothing. Bootstrapping can be used for many statistical purposes besides variable selection. One of the most common is to compute a confidence interval (CI). As a demonstration of the technique, we will compute a bootstrap CI for the O/E (observed-to-expected) ratio of mortality after myocardial infarction.

See page 1205

Background and rationale

Bootstrap methods are more than 20 years old [2], but they are computer-intensive and have only recently become widely available in statistical programs. Powerful and widely available computing capability is transforming statistics. Originally, computers just speeded up data handling and statistical computations. Now they enable exploratory analysis of gigantic data sets (data mining), creating neural network algorithms, analyzing complex genomic data, and supplementing traditional statistical methods using large numbers of data simulations.

Simulation

Typically, we collect a data sample and compute a statistic of interest, say the mean value of the individual data points. We realize there is variability associated with this estimate, such that if we, or others, repeated our experiment or data collection maneuvers, the estimate would be different. We need a measure of the variability of this statistic, say the standard deviation (SD) or a CI. The conventional method is to assume we know the distribution of the statistic of interest, often . . . [Full Text of this Article]

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