scholarly journals Cox Piecewise Constant Hazard Model with Bayesian Method

2021 ◽  
pp. 83-95
Author(s):  
Amanda Putri Tiyas Pratiwi ◽  
Sarini Abdullah ◽  
Ida Fithriani
Keyword(s):  
Bayesian Method ◽  
Survival Models ◽  
Posterior Density ◽  
Hazard Functions ◽  
Piecewise Constant ◽  
Baseline Hazard ◽  
Explanatory Variables ◽  
Time To Event Data ◽  
Dependent Component ◽  
Main Components

Cox PH model is one of the survival models that is widely used for analyzing time-to-event data. Cox PH model consists of two main components, the baseline hazard consisting of time-dependent component; and the exponential function accomodating explanatory variables. The baseline hazard is not estimated in the Cox PH model, thus not accommodating the need for hazard rate estimation. Therefore, in this paper we discuss the estimation of baseline hazard through piecewise constant hazard using Bayesian method. Gamma distribution is assumed for the piecewise constant baseline hazard, and normal distribution is assumed for the regression coefficient. Sampling from the posterior is conducted using Markov chain Monte Carlo through Gibbs sampling. Echocardiogram data containing 106 observations and 6 explanatory variables were used in analysis. The result showed that the baseline hazard functions were estimated and each of parameters in the model is converged as shown by the trace plot and posterior density plot.    

scholarly journals Detecting multiple change points in piecewise constant hazard functions

2011 ◽  
Vol 38 (11) ◽  
pp. 2523-2532 ◽  
Author(s):  
Melody S. Goodman ◽  
Yi Li ◽  
Ram C. Tiwari
Keyword(s):  
Change Points ◽  
Hazard Functions ◽  
Piecewise Constant

scholarly journals ODACH: A One-shot Distributed Algorithm for Cox model with Heterogeneous Multi-center Data

2021 ◽  
Author(s):  
Chongliang Luo ◽  
Rui Duan ◽  
Yong Chen
Keyword(s):  
Likelihood Function ◽  
Cox Model ◽  
Meta Analysis ◽  
Event Data ◽  
Time To Event ◽  
Hazard Functions ◽  
Patient Level ◽  
Time To Event Data ◽  
Level Data ◽  
Center Time

Objective: We developed and evaluated a privacy-preserving One-shot Distributed Algorithm for Cox model to analyze multi-center time-to-event data without sharing patient-level information across sites, while accounting for heterogeneity across sites by allowing site-specific baseline hazard functions and feature distributions. Materials and Methods: We constructed a surrogate likelihood function to approximate the Cox log partial likelihood function which is stratified by site, using patient-level data from a single site and aggregated information from other sites. The ODAC estimator was obtained by maximizing the surrogate likelihood function. We evaluated and compare the performance of ODACH with meta-analysis by extensive numerical studies. Results: The simulation study showed that ODACH provided estimates close to the pooled estimator, which is obtained by directly analyzing patient-level data from all sites via a stratified Cox model. The relative bias was <1% across all scenarios. As a comparison, the meta-analysis estimator, which was obtained by the inverse variance weighted average of the site-specific estimates, had substantial bias when the event rate is <5%, with the relative bias reaching 12% when the event rate is 1%. Conclusions: ODACH is a privacy-preserving and communication-efficient method for analyzing multi-center time-to-event data, which allows the baseline hazard functions as well as the distribution of covariate variables to vary across sites. It provides estimates that is close to the pooled estimator and substantially outperforms the meta-analysis estimator when the event is rare. It is thus extremely suitable for studying rare events with heterogeneous baseline hazards across sites in a distributed manner.

scholarly journals Lifetime risk of knee and hip replacement following a diagnosis of RA: findings from a cohort of 13 961 patients from England

Rheumatology ◽  
2019 ◽  
Vol 58 (11) ◽  
pp. 1950-1954 ◽  
Author(s):  
Edward Burn ◽  
Christopher J Edwards ◽  
David W Murray ◽  
Alan Silman ◽  
Cyrus Cooper ◽  
...  
Keyword(s):  
Hip Replacement ◽  
Smoking Status ◽  
Lifetime Risk ◽  
Survival Models ◽  
Explanatory Variables ◽  
Patient Profile ◽  
Average Patient ◽  
Charlson Comorbidity Score ◽  
Estimate Lifetime Risk ◽  
Long Term Prognosis

Abstract Objective To estimate the lifetime risk of knee and hip replacement following a diagnosis of RA. Methods The analysis was undertaken using routinely collected data from the English NHS. Diagnosis of RA was identified using primary care records, with knee and hip replacement observed in linked hospital records. Parametric survival models were fitted for up to 15 years of follow-up, with age, sex, Charlson comorbidity score, socioeconomic status, BMI and smoking status included as explanatory variables. A decision model was used to combine and extrapolate survival models to estimate lifetime risk. Results The number of individuals with a diagnosis of RA and included in the study was 13 961. Lifetime risk of knee replacement and hip replacement was estimated to be 22% (95% CI: 16, 29%) and 17% (95% CI: 11, 26%) following a diagnosis of RA for the average patient profile (non-smoking women aged 64 with no other comorbidities, BMI of 27 and in the top socioeconomic quintile). Risks were higher for younger patients. Conclusion The lifetime risk of knee and hip replacement for individuals with a diagnosis of RA is approximately double that of the general population. These findings allow for a better understanding of long-term prognosis and healthcare resource use, and highlight the importance of timely diagnosis and effective treatment.

Research and analysis of stiffness-enhanced soft gripper

2020 ◽  
pp. 1-8
Author(s):  
Hui Tian ◽  
Zhujun Zhang ◽  
Zhihua Yuan ◽  
Xiaochan Liu ◽  
Yuyan Qi ◽  
...  
Keyword(s):  
Soft Part ◽  
Optimization Method ◽  
Element Analysis ◽  
Bending Performance ◽  
Piecewise Constant ◽  
Soft Actuator ◽  
3D Printed ◽  
Large Balloon ◽  
Low Stiffness ◽  
Main Components

In view of the problems of low stiffness, small driving force and large balloon effect existing in the current soft actuator, this paper proposes an optimization method to enhance the overall stiffness of the soft gripper by using rigid components based on the multi-cavity soft pneumatic actuator. This paper introduces the main components of the actuator: the soft part poured by liquid silica gel, and the open rectangular rigid structures by 3D printed. The kinematics model of the finger is established based on the Piecewise Constant Curvature model(PCC). The bending performance of the enhanced stiffness gripper is verified by finite element analysis(FEA): the tip force of actuator increased with the increase of the number of rigid structures when the bending angle is constant. According to the and experimental data, the overall stiffness of soft gripper is increased by the rigid structure without affecting the flexibility of operation. And the maximum weight which can grasp is 3.4 times that of the traditional soft gripper, improved the grasping range of the soft gripper effectively.

scholarly journals Understanding the elevated suicide risk of female soldiers during deployments

2014 ◽  
Vol 45 (4) ◽  
pp. 717-726 ◽  
Author(s):  
A. E. Street ◽  
S. E. Gilman ◽  
A. J. Rosellini ◽  
M. B. Stein ◽  
E. J. Bromet ◽  
...  
Keyword(s):  
Suicide Rate ◽  
Administrative Database ◽  
Survival Models ◽  
Future Research ◽  
Risk And Resilience ◽  
Us Army ◽  
Explanatory Variables ◽  
The Us ◽  
Regular Army ◽  
History Of

BackgroundThe Army Study to Assess Risk and Resilience in Servicemembers (Army STARRS) has found that the proportional elevation in the US Army enlisted soldier suicide rate during deployment (compared with the never-deployed or previously deployed) is significantly higher among women than men, raising the possibility of gender differences in the adverse psychological effects of deployment.MethodPerson-month survival models based on a consolidated administrative database for active duty enlisted Regular Army soldiers in 2004–2009 (n = 975 057) were used to characterize the gender × deployment interaction predicting suicide. Four explanatory hypotheses were explored involving the proportion of females in each soldier's occupation, the proportion of same-gender soldiers in each soldier's unit, whether the soldier reported sexual assault victimization in the previous 12 months, and the soldier's pre-deployment history of treated mental/behavioral disorders.ResultsThe suicide rate of currently deployed women (14.0/100 000 person-years) was 3.1–3.5 times the rates of other (i.e. never-deployed/previously deployed) women. The suicide rate of currently deployed men (22.6/100 000 person-years) was 0.9–1.2 times the rates of other men. The adjusted (for time trends, sociodemographics, and Army career variables) female:male odds ratio comparing the suicide rates of currently deployed v. other women v. men was 2.8 (95% confidence interval 1.1–6.8), became 2.4 after excluding soldiers with Direct Combat Arms occupations, and remained elevated (in the range 1.9–2.8) after adjusting for the hypothesized explanatory variables.ConclusionsThese results are valuable in excluding otherwise plausible hypotheses for the elevated suicide rate of deployed women and point to the importance of expanding future research on the psychological challenges of deployment for women.

A fully nonparametric approach in survival models with explanatory variables

1995 ◽  
Vol 24 (12) ◽  
pp. 3027-3054 ◽  
Author(s):  
Sylvie Escolano ◽  
Jean-Louis Golmard ◽  
Alain Mallet
Keyword(s):  
Survival Models ◽  
Explanatory Variables ◽  
Nonparametric Approach

scholarly journals Introduction to Survival Analysis in Practice

2019 ◽  
Vol 1 (3) ◽  
pp. 1013-1038 ◽  
Author(s):  
Frank Emmert-Streib ◽  
Matthias Dehmer
Keyword(s):  
Survival Analysis ◽  
Event History Analysis ◽  
Proportional Hazard ◽  
Cox Models ◽  
Hazard Functions ◽  
Time To Event Data ◽  
History Analysis ◽  
Statistical Programming ◽  
Analysis Survival ◽  
Definition Of

The modeling of time to event data is an important topic with many applications in diverse areas. The collective of methods to analyze such data are called survival analysis, event history analysis or duration analysis. Survival analysis is widely applicable because the definition of an ’event’ can be manifold and examples include death, graduation, purchase or bankruptcy. Hence, application areas range from medicine and sociology to marketing and economics. In this paper, we review the theoretical basics of survival analysis including estimators for survival and hazard functions. We discuss the Cox Proportional Hazard Model in detail and also approaches for testing the proportional hazard (PH) assumption. Furthermore, we discuss stratified Cox models for cases when the PH assumption does not hold. Our discussion is complemented with a worked example using the statistical programming language R to enable the practical application of the methodology.

Sign in / Sign up

Export Citation Format

Share Document