{'fraction_non_alphanumeric': 0.01431297709923664, 'fraction_numerical': 0.0, 'mean_word_length': 5.515527950310559, '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}}
Semiparametric estimation of differences in treatment-specific recurrent event means with a terminal event

Recurrent event data often arise from biomedical studies and a terminal event may preclude further occurrence of recurrent events. In comparing treatments, the marginal mean is frequently of interest, and treatment-speciﬁc diﬀerences in the mean number of events are often not constant over time. In this article, we propose a semiparametric method to compare treatment-speciﬁc recurrent event means by combining an additive hazards model for the terminal event and an additive rates model for the conditional recurrent event rate. The treatment eﬀect is measured by the diﬀerence between treatment-speciﬁc recurrent event means. Estimation procedures are developed for the measure and the asymptotic properties of the proposed estimators are established. The ﬁnite sample performance of the proposed estimators is ex-amined through simulation studies, and an application to a bladder cancer study demonstrates the usefulness of our method.