تقرير
Inference via Wild Bootstrap and Multiple Imputation under Fine-Gray Models with Incomplete Data
| العنوان: | Inference via Wild Bootstrap and Multiple Imputation under Fine-Gray Models with Incomplete Data |
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| المؤلفون: | Dietrich, Marina T., Dobler, Dennis, de Gunst, Mathisca C. M. |
| سنة النشر: | 2023 |
| المجموعة: | Mathematics Statistics |
| مصطلحات موضوعية: | Statistics - Methodology, Mathematics - Statistics Theory, 62N01, 62N02, 62J99, 62G09, 62G15 |
| الوصف: | Fine-Gray models specify the subdistribution hazards for one out of multiple competing risks to be proportional. The estimators of parameters and cumulative incidence functions under Fine-Gray models have a simpler structure when data are censoring-complete than when they are more generally incomplete. This paper considers the case of incomplete data but it exploits the above-mentioned simpler estimator structure for which there exists a wild bootstrap approach for inferential purposes. The present idea is to link the methodology under censoring-completeness with the more general right-censoring regime with the help of multiple imputation. In a simulation study, this approach is compared to the estimation procedure proposed in the original paper by Fine and Gray when it is combined with a bootstrap approach. An application to a data set about hospital-acquired infections illustrates the method. Comment: 32 pages, 2 figures, 1 table |
| نوع الوثيقة: | Working Paper |
| URL الوصول: | http://arxiv.org/abs/2310.18422 |
| رقم الأكسشن: | edsarx.2310.18422 |
| قاعدة البيانات: | arXiv |
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