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(Circulation. 2006;114:2.)
© 2006 American Heart Association, Inc.

Editors' Note

Martin G. Larson, SD; Lisa M. Sullivan, PhD

Series Editors, Statistical Primer for Cardiovascular Research

An extract of the first 250 words of the full text is provided, because this article has no abstract.

Recent rapid progress in cardiovascular research has been characterized by increasingly complex study designs and data acquisition techniques. Recognizing the importance of statistics in obtaining valid inferences from these data, especially the need for fundamentally sound, context-appropriate statistical analyses, the Editors of Circulation commissioned a series of review articles on statistical topics of interest to its readers. The present issue introduces that series, called the "Statistical Primer for Cardiovascular Research."

Articles in the series—written by statisticians actively engaged in cardiovascular research, including recognized experts in statistical specialties—will be published monthly over the next 2 years. These articles will be at an introductory or intermediate statistical level for a target readership comprising physicians and biologists. They will explain and illustrate principles of statistical estimation and inference; they will enumerate assumptions inherent in each statistical model; they will identify methods to check validity of key assumptions; they will discuss consequences of departure from those assumptions; and they will provide guidelines for selecting among alternative procedures to analyze and display the data.

In the first year, introductory topics will include descriptive statistics and graphics and estimation and hypothesis testing for continuous and discrete variables (1- and 2-sample settings, including independent and paired samples). These will be followed by correlation and regression, nonparametric hypothesis tests, survival analysis techniques, receiver-operating characteristic curves in the context of diagnostic tests, design and evaluation of randomized controlled clinical trials, propensity score methods for nonrandomized studies, and meta-analysis to aggregate results from multiple studies.

In the second year, the . . . [Full Text of this Article]

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