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Relevant bibliographies by topics / Subdistribution Proportional Hazard

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Academic literature on the topic 'Subdistribution Proportional Hazard'

Author: Grafiati

Published: 3 June 2025

Last updated: 31 July 2025

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Journal articles on the topic "Subdistribution Proportional Hazard"

1

Berger, Moritz, Matthias Schmid, Thomas Welchowski, Steffen Schmitz-Valckenberg, and Jan Beyersmann. "Subdistribution hazard models for competing risks in discrete time." Biostatistics 21, no. 3 (2018): 449–66. http://dx.doi.org/10.1093/biostatistics/kxy069.

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Summary A popular modeling approach for competing risks analysis in longitudinal studies is the proportional subdistribution hazards model by Fine and Gray (1999. A proportional hazards model for the subdistribution of a competing risk. Journal of the American Statistical Association94, 496–509). This model is widely used for the analysis of continuous event times in clinical and epidemiological studies. However, it does not apply when event times are measured on a discrete time scale, which is a likely scenario when events occur between pairs of consecutive points in time (e.g., between two f

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2

Molydah S, Molydah S., and Danardono Danardono. "AN ADDITIVE SUBDISTRIBUTION HAZARDS MODEL FOR COMPETING RISKS DATA." MEDIA STATISTIKA 16, no. 2 (2024): 194–205. http://dx.doi.org/10.14710/medstat.16.2.194-205.

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Competing risk failure time data occur frequently in medical a number of methods have been proposed for the analysis of these data. The classic approach is to model all cause-specific hazards and then estimate the cumulative incidence curve based on these cause-specific hazards. Unfortunately, the cause-specific hazard function does not have a direct interpretation in terms of survival probabilities for the particular failure type. In this paper, we consider a more flexible model for the subdistribution. It is a combination of the additive model and the Cox model and allows one to perform a mo

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3

Diao, Guoqing, and Joseph G. Ibrahim. "Quantifying time-varying cause-specific hazard and subdistribution hazard ratios with competing risks data." Clinical Trials 16, no. 4 (2019): 363–74. http://dx.doi.org/10.1177/1740774519852708.

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Various non-proportional hazard models have been developed in the literature for competing risks data. The regression coefficients under these models, however, typically cannot be compared directly. We propose new methods to quantify the average of the time-varying cause-specific hazard ratios and subdistribution hazard ratios through two general classes of transformations and weight functions that are chosen to reflect the relative importance of the hazard ratios in different time periods. We further propose an [Formula: see text] -norm type of test statistic that incorporates the test statis

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4

Rerkasem, Amaraporn, Ampica Mangklabruks, Supawan Buranapin, et al. "Incidence and predictors of cardiovascular disease mortality and all-cause mortality in patients with type II diabetes with peripheral arterial disease." PLOS One 20, no. 5 (2025): e0322502. https://doi.org/10.1371/journal.pone.0322502.

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Objective This cohort study estimated the incidence and predictors of cardiovascular disease (CVD) and all-cause mortality among patients with type 2 diabetes mellitus (T2DM) and various stages of peripheral arterial disease (PAD) at the largest tertiary referral hospitals in upper-northern Thailand. Methods This study recruited 278 T2DM and PAD patients for a 7-year cohort study. These patients completed health questionnaires and underwent physical examinations including ankle-brachial index measurements and clinical assessment to determine PAD severity. Mortality endpoints were determined us

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Yousif, Yosra, Faiz Ahmed Mohamed Elfaki, and Meftah Hrairi. "Regression Analysis of Masked Competing Risks Data under Cumulative Incidence Function Framework." Austrian Journal of Statistics 49, no. 3 (2020): 25–29. http://dx.doi.org/10.17713/ajs.v49i3.1026.

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In the studies that involve competing risks, somehow, masking issues might arise. That is, the cause of failure for some subjects is only known as a subset of possible causes. In this study, a Bayesian analysis is developed to assess the effect of risks factor on the Cumulative Incidence Function (CIF) by adopting the proportional subdistribution hazard model. Simulation is conducted to evaluate the performance of the proposed model and it shows that the model is feasible for the possible applications.

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6

Tai, BC, ZJ Chen, and D. Machin. "Estimating sample size in the presence of competing risks – Cause-specific hazard or cumulative incidence approach?" Statistical Methods in Medical Research 27, no. 1 (2015): 114–25. http://dx.doi.org/10.1177/0962280215623107.

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In designing randomised clinical trials involving competing risks endpoints, it is important to consider competing events to ensure appropriate determination of sample size. We conduct a simulation study to compare sample sizes obtained from the cause-specific hazard and cumulative incidence (CMI) approaches, by first assuming exponential event times. As the proportional subdistribution hazard assumption does not hold for the CMI exponential (CMIExponential) model, we further investigate the impact of violation of such an assumption by comparing the results obtained from the CMI exponential mo

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7

Spencer, Katie L., Martyn P. T. Kennedy, Katie L. Lummis, et al. "Surgery or radiotherapy for stage I lung cancer? An intention-to-treat analysis." European Respiratory Journal 53, no. 6 (2019): 1801568. http://dx.doi.org/10.1183/13993003.01568-2018.

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IntroductionSurgery is the standard of care for early-stage lung cancer, with stereotactic ablative body radiotherapy (SABR) a lower morbidity alternative for patients with limited physiological reserve. Comparisons of outcomes between these treatment options are limited by competing comorbidities and differences in pre-treatment pathological information. This study aims to address these issues by assessing both overall and cancer-specific survival for presumed stage I lung cancer on an intention-to-treat basis.MethodsThis retrospective intention-to-treat analysis identified all patients treat

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8

Uchida, Yushi, Toshiaki Nakano, Hiroto Hiyamuta, et al. "Association between Serum C-Reactive Protein Concentrations and Risk of Cancer-Related Mortality in Patients Undergoing Hemodialysis: 10-Year Outcomes of the Q-Cohort Study." Blood Purification 52, no. 7-8 (2023): 694–701. http://dx.doi.org/10.1159/000530846.

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<b><i>Introduction:</i></b> Cancer constitutes a major source of morbidity and mortality among people undergoing hemodialysis (HD). A systemic inflammatory response is associated with the incidence and prognosis of cancer in the general population. However, the effect of systemic inflammation on cancer-related mortality in patients undergoing HD remains unclear. <b><i>Methods:</i></b> We analyzed 3,139 patients registered in the Q-Cohort Study, which is a multicenter, observational cohort study of patients on hemodialysis in Japan. The primary ou

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9

Luksiene, Dalia, Abdonas Tamosiunas, Dalia Virviciute, and Ricardas Radisauskas. "The Prognostic Value of Combined Smoking and Alcohol Consumption Habits for the Estimation of Cause-Specific Mortality in Middle-Age and Elderly Population: Results from a Long-Term Cohort Study in Lithuania." BioMed Research International 2017 (2017): 1–12. http://dx.doi.org/10.1155/2017/9654314.

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Aim. To evaluate the prognostic value of combined smoking and alcohol consumption habits for the estimation of cause-specific mortality risk in middle-age and elderly population. Methods. The study presents data from the four surveys. A random sample of 6,729 subjects aged 35–64 years was selected for statistical analysis. During the follow-up of 31 years (1983–2014), there were 2,158 deaths from any cause. Multivariate Cox’s proportional hazards models were used to estimate hazard ratios (HR) for all-cause mortality and Competing Risk Regression analysis was used to estimate subdistribution h

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Nolley, Eric P., Sarina K. Sahetya, Chad H. Hochberg, et al. "Outcomes Among Mechanically Ventilated Patients With Severe Pneumonia and Acute Hypoxemic Respiratory Failure From SARS-CoV-2 and Other Etiologies." JAMA Network Open 6, no. 1 (2023): e2250401. http://dx.doi.org/10.1001/jamanetworkopen.2022.50401.

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ImportanceEarly observations suggested that COVID-19 pneumonia had a higher mortality rate than other causes of pneumonia.ObjectiveTo compare outcomes between mechanically ventilated patients with pneumonia due to COVID-19 (March 2020 to June 2021) and other etiologies (July 2016 to December 2019).Design, Setting, and ParticipantsThis retrospective cohort study was conducted at the Johns Hopkins Healthcare System among adult patients (aged ≥18 years) with pneumonia who required mechanical ventilation in the first 2 weeks of hospitalization. Clinical, laboratory, and mechanical ventilation data

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