Event-weighted proportional hazards modelling for recurrent gap time data

2012 ◽  
Vol 32 (1) ◽  
pp. 124-130 ◽  
Author(s):  
G. A. Darlington ◽  
S. N. Dixon
Keyword(s):  
Proportional Hazards ◽  
Time Data ◽  
Gap Time

A unified approach to variable selection for Cox’s proportional hazards model with interval-censored failure time data

2021 ◽  
pp. 096228022110092
Author(s):  
Mingyue Du ◽  
Hui Zhao ◽  
Jianguo Sun
Keyword(s):  
Variable Selection ◽  
Proportional Hazards ◽  
Proportional Hazards Model ◽  
Failure Time ◽  
Significant Feature ◽  
Failure Time Data ◽  
Time Data ◽  
Hazards Model ◽  
Interval Censored ◽  
Cox's Proportional Hazards Model

Cox’s proportional hazards model is the most commonly used model for regression analysis of failure time data and some methods have been developed for its variable selection under different situations. In this paper, we consider a general type of failure time data, case K interval-censored data, that include all of other types discussed as special cases, and propose a unified penalized variable selection procedure. In addition to its generality, another significant feature of the proposed approach is that unlike all of the existing variable selection methods for failure time data, the proposed approach allows dependent censoring, which can occur quite often and could lead to biased or misleading conclusions if not taken into account. For the implementation, a coordinate descent algorithm is developed and the oracle property of the proposed method is established. The numerical studies indicate that the proposed approach works well for practical situations and it is applied to a set of real data arising from Alzheimer’s Disease Neuroimaging Initiative study that motivated this study.

Additive mixed effect model for recurrent gap time data

2015 ◽  
Vol 23 (2) ◽  
pp. 223-253 ◽  
Author(s):  
Jieli Ding ◽  
Liuquan Sun
Keyword(s):  
Mixed Effect Model ◽  
Time Data ◽  
Mixed Effect ◽  
Effect Model ◽  
Gap Time

scholarly journals An Inference-based Prognostic Framework for Health Management of Automotive Systems

2020 ◽  
Vol 7 (2) ◽  
Author(s):  
Chaitanya Sankavaram ◽  
Anuradha Kodali ◽  
Krishna Pattipati ◽  
Satnam Singh ◽  
Yilu Zhang ◽  
...  
Keyword(s):  
Proportional Hazards ◽  
Proportional Hazards Model ◽  
Failure Time ◽  
Remaining Useful Life ◽  
Survival Functions ◽  
Time Data ◽  
Automotive Systems ◽  
Multiple Fault Diagnosis ◽  
Hazards Model ◽  
Useful Life

This paper presents a unified data-driven prognostic framework that combines failure time data, static parameter data and dynamic time-series data. The framework employs proportional hazards model and a soft dynamic multiple fault diagnosis algorithm for inferring the degraded state trajectories of components and to estimate their remaining useful life times. The framework takes into account the cross-subsystem fault propagation, a case prevalent in any networked and embedded system. The key idea is to use Cox proportional hazards model to estimate the survival functions of error codes and symptoms (probabilistic test outcomes/prognostic indicators) from failure time data and static parameter data, and use them to infer the survival functions of components via soft dynamic multiple fault diagnosis algorithm. The average remaining useful life and its higher-order central moments (e.g., variance, skewness, kurtosis) can be estimated from these component survival functions. The framework is demonstrated on datasets derived from two automotive systems, namely hybrid electric vehicle regenerative braking system, and an electronic throttle control subsystem simulator. Although the proposed framework is validated on automotive systems, it has the potential to be applicable to a wide variety of systems, ranging from aerospace systems to buildings to power grids.

Properties of the Estimators for Additive-Accelerated Hazard Ratio Model

2012 ◽  
Vol 263-266 ◽  
pp. 175-178
Author(s):  
Huan Bin Liu
Keyword(s):  
Unknown Parameter ◽  
Recurrent Events ◽  
Asymptotic Properties ◽  
Hazard Ratio ◽  
Estimation Methods ◽  
Ratio Model ◽  
Time Data ◽  
Multiple Type Recurrent Events ◽  
Parameter Function ◽  
Gap Time

Recurrent events gap time is the time difference between two adjacent failures of recurrent events. In this paper, an additive-accelerated hazard ratio model is presented for multiple type recurrent events gap time data, and the estimation methods of unknown parameter and non-parameter function is given. Moreover, the asymptotic properties of estimators are proved.

scholarly journals Pharmacodynamic Evaluation of RWJ-270201, a Novel Neuraminidase Inhibitor, in a Lethal Murine Model of Influenza Predicts Efficacy for Once-Daily Dosing

2001 ◽  
Vol 45 (7) ◽  
pp. 2115-2118 ◽  
Author(s):  
G. L. Drusano ◽  
S. L. Preston ◽  
D. Smee ◽  
K. Bush ◽  
K. Bailey ◽  
...  
Keyword(s):  
Drug Exposure ◽  
Proportional Hazards ◽  
Explanatory Power ◽  
Neuraminidase Inhibitor ◽  
Cox Proportional Hazards ◽  
Time Data ◽  
Time Curve ◽  
Once Daily ◽  
Concentration Time ◽  
Drug Challenge

ABSTRACT We examined RWJ-270201 in a lethal model of influenza in BALB/c mice. The aim was to delineate the pharmacodynamically linked variable for the drug. Challenge was performed with influenza virus A/Shongdong/09/93 (H3N2). Treatment was administered by gavage. Five doses (1 to 10 mg/kg of body weight) and three schedules (every 24, 12, and 8 h) were evaluated with 10 mice per group. There were 39 placebo-treated mice. Drug exposure was evaluated for infected mice. Exposures were calculated after population modeling of all the plasma concentration-time data simulataneously using the NPEM3 program. Evaluation of dose and schedule with Kaplan-Meier analysis and Cox proportional hazards modeling demonstrated that schedule offered no explanatory power relative to dose alone. Evaluation of peak concentration, trough concentration, and area under the concentration-time curve (AUC) by the same methods revealed that AUC was the dynamically linked variable. Again, schedule offered no further explanatory power when included in the model with AUC. This indicates that AUC is the linked variable and that the anti-influenza effect of RWJ-270201 is independent of schedule. We conclude that once-daily dosing of RWJ-270201 should be evaluated in clinical trials of influenza therapy.

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