A test for publication bias in meta-analysis with sparse binary data

Abstract

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.