{'fraction_non_alphanumeric': 0.014373716632443531, 'fraction_numerical': 0.0, 'mean_word_length': 5.337662337662338, '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}}
Latent class analysis in chronic disease epidemiology.

Latent class analysis provides a useful framework for the analysis of epidemiological data which may have been mismeasured. In this paper, the latent class model is described in the context of logistic regression with categorical variables, and some examples of its application are provided. In particular, it is shown that adjustment for a misclassified confounding variable can be greatly improved by using the methods presented.