Causal inference for the expected number of recurrent events in the presence of a terminal event
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arXiv
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| Autori principali: | , , , , |
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| Natura: | Preprint |
| Pubblicazione: |
2023
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| _version_ | 1866909810936512512 |
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| author | Baer, Benjamin R. Bui, Trang Mork, Daniel Strawderman, Robert L. Ertefaie, Ashkan |
| author_facet | Baer, Benjamin R. Bui, Trang Mork, Daniel Strawderman, Robert L. Ertefaie, Ashkan |
| contents | While recurrent event analyses have been extensively studied, limited attention has been given to causal inference within the framework of recurrent event analysis. We develop a multiply robust estimation framework for causal inference in recurrent event data with a terminal failure event. We define our estimand as the vector comprising both the expected number of recurrent events and the failure survival function evaluated along a sequence of landmark times. We show that the estimand can be identified under a weaker condition than conditionally independent censoring and derive the associated class of influence functions under general censoring and failure distributions (i.e., without assuming absolute continuity). We propose a particular estimator within this class for further study, conduct comprehensive simulation studies to evaluate the small-sample performance of our estimator, and illustrate the proposed estimator using a large Medicare dataset to assess the causal effect of PM$_{2.5}$ on recurrent cardiovascular hospitalization. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2306_16571 |
| institution | arXiv |
| publishDate | 2023 |
| record_format | arxiv |
| spellingShingle | Causal inference for the expected number of recurrent events in the presence of a terminal event Baer, Benjamin R. Bui, Trang Mork, Daniel Strawderman, Robert L. Ertefaie, Ashkan Methodology Statistics Theory Machine Learning While recurrent event analyses have been extensively studied, limited attention has been given to causal inference within the framework of recurrent event analysis. We develop a multiply robust estimation framework for causal inference in recurrent event data with a terminal failure event. We define our estimand as the vector comprising both the expected number of recurrent events and the failure survival function evaluated along a sequence of landmark times. We show that the estimand can be identified under a weaker condition than conditionally independent censoring and derive the associated class of influence functions under general censoring and failure distributions (i.e., without assuming absolute continuity). We propose a particular estimator within this class for further study, conduct comprehensive simulation studies to evaluate the small-sample performance of our estimator, and illustrate the proposed estimator using a large Medicare dataset to assess the causal effect of PM$_{2.5}$ on recurrent cardiovascular hospitalization. |
| title | Causal inference for the expected number of recurrent events in the presence of a terminal event |
| topic | Methodology Statistics Theory Machine Learning |
| url | https://arxiv.org/abs/2306.16571 |