{'fraction_non_alphanumeric': 0.013062409288824383, 'fraction_numerical': 0.0, 'mean_word_length': 5.699029126213592, '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}}
STRATIFIED TESTS, STRATIFIED SLOPES, AND RANDOM EFFECTS MODELS FOR CLINICAL TRIALS WITH MISSING DATA

Because missing observations may affect the size and power of statistical tests of equality, various analytical techniques explicitly or implicitly condition the analysis on the amount of information available per person. We illustrate the difference between stratifying a slope estimate and stratifying a test statistic based on slopes. We compare a nonparametric version of the latter approach with the parametric tests available from SAS Proc Mixed. Power and size of these two approaches are considered under different parametric settings, distributions, and missing data mechanisms.