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A test for publication bias in meta‐analysis with sparse binary data
Author(s) -
Schwarzer Guido,
Antes Gerd,
Schumacher Martin
Publication year - 2006
Publication title -
statistics in medicine
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 1.996
H-Index - 183
eISSN - 1097-0258
pISSN - 0277-6715
DOI - 10.1002/sim.2588
Subject(s) - statistics , type i and type ii errors , statistic , test statistic , computer science , test (biology) , binary data , variance (accounting) , meta analysis , binary number , contrast (vision) , sample size determination , statistical hypothesis testing , mathematics , artificial intelligence , medicine , paleontology , arithmetic , accounting , business , biology
A new test for the detection of publication bias in meta‐analysis with sparse binary data is proposed. The test statistic is based on observed and expected cell frequencies, and the variance of the observed cell frequencies. These quantities are utilized in a rank correlation test. Type I error rate and power of the test are evaluated in simulations; results are compared to those of two other commonly used test procedures. Sample sizes were generated according to findings in a survey of eight German medical journals. Simulation results indicate that, in contrast to existing test procedures, the new test holds the prescribed significance level when data are sparse. However, the power of all tests is low in many situations of practical importance. Copyright © 2006 John Wiley & Sons, Ltd.