A Proportional Hazards Model for the Subdistribution of a Competing Risk

# A Proportional Hazards Model for the Subdistribution of a Competing Risk > OpenAlex Metadata Hub · https://openalex.org/W2038981426 ## Bibliographic - **DOI:** 10.1080/01621459.1999.10474144 - **Year:** 1999 - **Citations:** 13621 - **Open Access:** No (closed) - **License:** — - **Source:** https://doi.org/10.1080/01621459.1999.10474144 ## Authors - Jason P. Fine - Malcolm H. Ray ## Abstract Abstract With explanatory covariates, the standard analysis for competing risks data involves modeling the cause-specific hazard functions via a proportional hazards assumption. Unfortunately, the cause-specific hazard function does not have a direct interpretation in terms of survival probabilities for the particular failure type. In recent years many clinicians have begun using the cumulative incidence function, the marginal failure probabilities for a particular cause, which is intuitively appealing and more easily explained to the nonstatistician. The cumulative incidence is especially relevant in cost-effectiveness analyses in which the survival probabilities are needed to determine treatment utility. Previously, authors have considered methods for combining estimates of the cause-specific hazard functions under the proportional hazards formulation. However, these methods do not allow the analyst to directly assess the effect of a covariate on the marginal probability function. In this article we propose a novel semiparametric proportional hazards model for the subdistribution. Using the partial likelihood principle and weighting techniques, we derive estimation and inference procedures for the finite-dimensional regression parameter under a variety of censoring scenarios. We give a uniformly consistent estimator for the predicted cumulative incidence for an individual with certain covariates; confidence intervals and bands can be obtained analytically or with an easy-to-implement simulation technique. To contrast the two approaches, we analyze a dataset from a breast cancer clinical trial under both models. Key Words: Hazard of subdistributionMartingalePartial likelihoodTransformation model ## Keywords Covariate, Proportional hazards model, Censoring (clinical trials), Inverse probability weighting, Statistics, Estimator, Weighting, Econometrics, Cumulative incidence, Nominal level, Inference, Hazard, Mathematics, Confidence interval, Computer science, Medicine, Artificial intelligence ## Concepts - Covariate - Proportional hazards model - Censoring (clinical trials) - Inverse probability weighting - Statistics - Estimator - Weighting - Econometrics - Cumulative incidence - Nominal level - Inference - Hazard - Mathematics - Confidence interval - Computer science - Medicine - Artificial intelligence - Cohort - Radiology - Chemistry - Organic chemistry --- *Metadata only — full text not imported unless Open Access license permits.*

Bài “A Proportional Hazards Model for the Subdistribution of a Competing Risk” được TradingBase chuyển thành Knowledge Product cho trader — không phải trang đọc abstract OpenAlex. Tóm lược học thuật (đã diễn giải): Abstract With explanatory covariates, the standard analysis for competing risks data involves modeling the cause-specific hazard functions via a proportional hazards assumption. Unfortunately, the cause-specific hazard function does not have a direct interpretation in terms of survival probabilities for the particular failure type. In recent years many clinicians have begun using the cumulative incidence function, the marginal failure probabilities for a particular cause, which is intuitively appealing and more easily explained to the nonstatistician. The cumulative incidence is especially relevant in cost-effectiveness analyses in which the survival probabilities are needed to determine treatment utility. Previously, authors have considered methods for combining estimates of the cause-specific hazard functions under the proportional hazards formulation. However, these methods do not all… Phần Trading Insights bên dưới nối nghiên cứu với Forex, vàng, USD, lãi suất và risk regime — để bạn đưa vào journal và playbook. Metadata DOI/OA chỉ là rail tham chiếu; nội dung chính là summary, takeaways và ứng dụng thị trường do Content Factory sinh.