scholarly journals Time-varying coefficient proportional hazards model with missing covariates

2012 ◽  
Vol 32 (12) ◽  
pp. 2013-2030 ◽  
Author(s):  
Xiao Song ◽  
Ching-Yun Wang
Keyword(s):  
Proportional Hazards ◽  
Proportional Hazards Model ◽  
Missing Covariates ◽  
Time Varying ◽  
Varying Coefficient ◽  
Hazards Model

The Identifiability of the Mixed Proportional Hazards Model with Time-Varying Coefficients

1996 ◽  
Vol 12 (4) ◽  
pp. 733-738 ◽  
Author(s):  
Brian P. McCall
Keyword(s):  
Proportional Hazards ◽  
Proportional Hazards Model ◽  
Time Varying ◽  
Varying Coefficients ◽  
Hazards Model ◽  
Time Varying Coefficients ◽  
Mixed Proportional Hazards

This paper establishes conditions for the nonparametric identifiability of the mixed proportional hazards model with time-varying coefficients. Unlike the mixed proportional hazards model, a regressor with two distinct values is not sufficient to identify this model. An unbounded regressor, however, is sufficient for identification.

scholarly journals Moving beyond the Cox proportional hazards model in survival data analysis: a cervical cancer study

BMJ Open ◽  
2020 ◽  
Vol 10 (7) ◽  
pp. e033965
Author(s):  
Lixian Li ◽  
Zijing Yang ◽  
Yawen Hou ◽  
Zheng Chen
Keyword(s):  
Lymph Node ◽  
Lymph Node Metastasis ◽  
Proportional Hazards ◽  
Proportional Hazards Model ◽  
Figo Stage ◽  
Cox Proportional Hazards ◽  
Cox Proportional Hazards Model ◽  
Time Varying ◽  
Node Metastasis ◽  
Hazards Model

ObjectivesThis study explored the prognostic factors and developed a prediction model for Chinese-American (CA) cervical cancer (CC) patients. We compared two alternative models (the restricted mean survival time (RMST) model and the proportional baselines landmark supermodel (PBLS model, producing dynamic prediction)) versus the Cox proportional hazards model in the context of time-varying effects.Setting and data sourcesA total of 713 CA women with CC and available covariates (age at diagnosis, International Federation of Gynecology and Obstetrics (FIGO) stage, lymph node metastasis and radiation) from the Surveillance, Epidemiology and End Results database were included.DesignWe applied the Cox proportional hazards model to analyse the all-cause mortality with the proportional hazards assumption. Additionally, we applied two alternative models to analyse covariates with time-varying effects. The performances of the models were compared using the C-index for discrimination and the shrinkage slope for calibration.ResultsOlder patients had a worse survival rate than younger patients. Advanced FIGO stage patients showed a relatively poor survival rate and low life expectancy. Lymph node metastasis was an unfavourable prognostic factor in our models. Age at diagnosis, FIGO stage and lymph node metastasis represented time-varying effects from the PBLS model. Additionally, radiation showed no impact on survival in any model. Dynamic prediction presented a better performance for 5-year dynamic death rates than did the Cox proportional hazards model.ConclusionsWith the time-varying effects, the RMST model was suggested to explore diagnosis factors, and the PBLS model was recommended to predict a patient’s w-year dynamic death rate.

scholarly journals A competing risks model with time‐varying heterogeneity and simultaneous failure

2020 ◽  
Vol 11 (2) ◽  
pp. 535-577 ◽  
Author(s):  
Ruixuan Liu
Keyword(s):  
Competing Risks ◽  
Proportional Hazards ◽  
Proportional Hazards Model ◽  
Estimation Procedure ◽  
Time Varying ◽  
Competing Risks Model ◽  
Hazards Model ◽  
Competing Risks Data ◽  
Passage Times ◽  
Lévy Subordinators

This paper proposes a new bivariate competing risks model in which both durations are the first passage times of dependent Lévy subordinators with exponential thresholds and multiplicative covariates effects. Our specification extends the mixed proportional hazards model, as it allows for the time‐varying heterogeneity represented by the unobservable Lévy processes and it generates the simultaneous termination of both durations with positive probability. We obtain nonparametric identification of all model primitives given competing risks data. A flexible semiparametric estimation procedure is provided and illustrated through the analysis of a real dataset.

scholarly journals Survival on four-times-per-week compared with three times a week haemodialysis in high ultrafiltration patients: an observational study

2020 ◽  
Author(s):  
James Fotheringham ◽  
Nicholas Latimer ◽  
Marc Froissart ◽  
Florian Kronenberg ◽  
Peter Stenvinkel ◽  
...  
Keyword(s):  
Observational Study ◽  
Vascular Access ◽  
Proportional Hazards ◽  
Proportional Hazards Model ◽  
Cox Proportional Hazards ◽  
Cox Proportional Hazards Model ◽  
Time Varying ◽  
Cardiovascular Comorbidities ◽  
Hazards Model ◽  
Access Complications

Abstract Background The harm caused by the long interdialytic interval in three-times-per-week haemodialysis regimens (3×WHD) may relate to fluid accumulation and associated high ultrafiltration rate (UFR). Four-times-per-week haemodialysis (4×WHD) may offer a solution, but its impact on mortality, hospitalization and vascular access complications is unknown. Methods From the AROii cohort of incident in-centre haemodialysis patients, 3×WHD patients with a UFR >10 mL/kg/h were identified. The hazard for the outcomes of mortality, hospitalization and vascular access complications in those who switched to 4×WHD compared with staying on 3×WHD was estimated using a marginal structural Cox proportional hazards model. Adjustment included baseline patient and treatment characteristics with inverse probability weighting used to adjust for time-varying UFR and cardiovascular comorbidities. Results From 10 637 European 3×WHD patients, 3842 (36%) exceeded a UFR >10 mL/kg/h. Of these, 288 (7.5%) started 4×WHD and at baseline were more comorbid. Event rates while receiving 4×WHD compared with 3×WHD were 12.6 compared with 10.8 per 100 patient years for mortality, 0.96 compared with 0.65 per year for hospitalization and 14.7 compared with 8.0 per 100 patient years for vascular access complications. Compared with 3×WHD, the unadjusted hazard ratio (HR) for mortality on 4×WHD was 1.05 [95% confidence interval (CI) 0.78–1.42]. Following adjustment for baseline demographics, time-varying treatment probability and censoring risks, this HR was 0.73 (95% CI 0.50–1.05; P = 0.095). Despite these adjustments on 4×WHD, the HR for hospitalization remained elevated and vascular access complications were similar to 3×WHD. Conclusions This observational study was not able to demonstrate a mortality benefit in patients switched to 4×WHD. To demonstrate the true benefits of 4×WHD requires a large, well-designed clinical trial. Our data may help in the design of such a study.

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