{'fraction_non_alphanumeric': 0.020389249304911955, 'fraction_numerical': 0.0037071362372567192, 'mean_word_length': 5.206896551724138, 'pattern_counts': {'":': 0, '<': 0, '<?xml version=': 0, '>': 0, 'https://': 0, 'lorem ipsum': 0, 'www.': 0, 'xml': 0}, 'pii_count': 0, 'substrings_counts': 0, 'word_list_counts': {'cursed_substrings.json': 0, 'profanity_word_list.json': 0, 'sexual_word_list.json': 0, 'zh_pornsignals.json': 0}}
Evaluation of a new version of I2 with emphasis on diagnostic problems

Abstract This paper describes results stemming from an in-depth analysis of Higgins’ measure of heterogeneity for a meta-analysis applied in the context of meta-analysis for diagnostic problems. Higgins measure of heterogeneity I2 has been criticized for being confounded by the study-specific sample size, in the sense that different I2 –values can be achieved for the same value of across-study variance if only the study-specific variance is varying enough. In particular, I2 approaches one for any value of the heterogeneity variance (variance across studies) if the within-study variance becomes large. The paper proposes a measure which is unconfounded by sample size. It is essentially a philosophical question which heterogeneity measure is chosen. Nevertheless, a detailed simulation study has been launched and the results indicate that the newly suggest measure of heterogeneity has beneficial statistical properties. Both measures are also exemplified at hand of some meta-analytic case studies.