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Combined survival analysis of cardiac patients by a cox ph model and a Markov chain

LIFETIME DATA ANALYSIS 2011年第4期17卷 496-513页

作者： Shauly, Michal Rabinowitz, Gad Gilutz, Harel Parmet, Yisrael Ben Gurion Univ Negev Dept Ind Engn & Management IL-84105 Beer Sheva Israel Soroka Univ Med Ctr Beer Sheva Israel

The control and treatment of dyslipidemia is a major public health challenge, particularly for patients with coronary heart diseases. In this paper we propose a framework for survival analysis of patients who had a major cardiac event, focusing on assessment of the effect of changing LDL-cholesterol level and statins consumption on survival. This framework includes a cox ph model and a Markov chain, and combines their results into reinforced conclusions regarding the factors that affect survival time. We prospectively studied 2,277 cardiac patients, and the results show high congruence between the Markov model and the ph model;both evidence that diabetes, history of stroke, peripheral vascular disease and smoking significantly increase hazard rate and reduce survival time. On the other hand, statin consumption is correlated with a lower hazard rate and longer survival time in both models. The role of such a framework in understanding the therapeutic behavior of patients and implementing effective secondary and primary prevention of heart diseases is discussed here.

关键词： Expected survival time Homogenous Markov chain Dyslipidemia cox ph model Major cardiac event Coronary heart disease

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Identification of influential observations based on binary particle swarm optimization in the cox ph model

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COMMUNICATIONS IN STATISTICS-SIMULATION AND COMPUTATION 2020年第3期49卷 567-590页

作者： Sancar, Nuriye Inan, Deniz Near East Univ Dept Math CY-99138 Nicosia Cyprus Marmara Univ Dept Stat Istanbul Turkey

Proper identification of influential observations should be an integral and significant part of the cox modeling process. This is because the failure to identify influential observations may have negative effect on the estimates acquired from the process. Furthermore, in survival analysis, influential observations frequently propose that the model has imperfections, including poorly specified factor, non-proportional hazards, loss of important information and/or a variable that has been omitted. Nevertheless, many procedures have been developed for the identification of a single influential observation based on the leave-one-out method. However, the results from leave-one-out diagnostic techniques are often misleading as a result of swamping and masking problems in the presence of multiple influential observations in the dataset. In this paper, identification of the optimal set of influential observations problem has been considered as the combinatorial optimization problem and a new simultaneous approach for identification of the optimal set of influential observations is proposed based on Binary Particle Swarm Optimization (BPSO) approach in the cox ph model. The performance of the proposed BPSO-based approach and conventional diagnostic techniques have been compared according to various evaluation criteria by simulation studies. The performance of the BPSO-based approach has been also demonstrated by the clinical real dataset.

关键词： cox ph model Influential Observations Binary Particle Swarm Optimization Likelihood Displacement Masking Effect Swamping Effect

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Profile likelihood estimation for the cox proportional hazards (ph) cure model and standard errors

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STATISTICAL PAPERS 2024年第1期65卷 181-201页

作者： Mohammad, Khandoker Akib Hirose, Yuichi Surya, Budhi Yao, Yuan WorkSafe New Zealand Wellington New Zealand Victoria Univ Wellington Wellington New Zealand

In survival analysis, cox ph cure model has attracted attention for decades. The profile likelihood approach is one of the well known estimation methods employed within the cox ph cure modeling framework. Typically, the parameters in the profile likelihood are estimated using expectation maximization (EM) algorithm. However, there is a challenge in estimating the standard error (SE) of the estimator in the profile likelihood method. In this paper, we propose a new approach to show the asymptotic normality of the maximum profile likelihood estimator and obtain the closed form expression for the SE of the estimator. Real data example and simulation studyshowed that our method is more computationally efficient and performs better in estimating standard errors compared with the bootstrap methods in "smcure' package (Cai et al. 2012).

关键词： cox ph model Cure model Efficient score function EM algorithm Profile likelihood

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A transfer learning approach for remaining useful life prediction subject to hard failure considering within and between population variations

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RELIABILITY ENGINEERING & SYSTEM SAFETY 2025年 261卷

作者： Guo, Xinxing Huang, Song Wu, Jianguo Wang, Chao Peking Univ Dept Ind Engn & Management Beijing Peoples R China Univ Iowa Dept Ind & Syst Engn Iowa City IA 52242 USA

Accurate prediction of remaining useful life (RUL) of a unit plays a critical role in condition-based maintenance, especially for hard failure cases. In industrial practice, due to differences in units' types and working environments, there may exist multiple populations, and even within the same population, there are also variations among units. However, existing methods either assume that different units share the same population characteristics and ignore the between-population variations, or solely focus on between-population knowledge transfer while neglecting the within-population variations. To address this issue, this article proposes a transfer learning approach by integrating a cox Proportional Hazards (ph) model with a Bayesian hierarchical model, which considers both within and between population variations. Specifically, a shared prior distribution is deployed to the parameters of the cox model in each population, which builds the foundation for transfer learning across different populations. To model within-population variations, a linear mixed-effects model is utilized to represent heterogeneous degradation data of each unit. The effectiveness of the proposed method is demonstrated and compared with various benchmarks through a simulation study and a case study of turbine engines.

关键词： Bayesian hierarchical model Hard failure cox ph model Transfer learning Remaining useful life prediction

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On estimating stratified ph model with single covariate from sparse data with application to brain metastases study

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BIOMETRICAL JOURNAL 2000年第5期42卷 569-581页

作者： Chang, YC Perng, CH Shiau, CY Tamkang Univ Dept Math Taipei 25137 Taiwan Vet Gen Hosp Ctr Canc Dept Radiat Oncol Taipei Taiwan

The estimation of the unknown parameters in the stratified cox's proportional hazard model is a typical example of the trade-off between bias and precision. The stratified partial likelihood estimator is unbiased when the number of strata is large but suffer from being unstable when many strata are noninformative about the unknown parameters. The estimator obtained by ignoring the heterogeneity among strata, on the other hand, increases the precision of estimates although pays the price for being biased. An estimating procedure, based on the asymptotic properties of the above two estimators, serving to compromise between bias and precision is proposed. Two examples in a radiosurgery for brain metastases study provide some interesting demonstration of such applications.

关键词： cox ph model stratification sparse data brain metastases

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modeling High Wind Speed Shut-Down Events Using Meso-Scale Wind Profiles and Survival Analysis

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IEEE TRANSACTIONS ON POWER SYSTEMS 2019年第6期34卷 4955-4963页

作者： Groch, Matthew Vermeulen, Hendrik J. Stellenbosch Univ Dept Elect & Elect Engn ZA-7600 Stellenbosch South Africa

Optimal siting is of paramount importance for managing the grid impacts of variable wind energy resources, especially in view of the increasing penetration of wind energy as part of the renewable energy mix. High variability of the renewable energy generation component requires that the system operator respond to generation deficits through frequent cycling of conventional generation and increased use of expensive peaking generation plants. Limited research has been conducted into methodologies whereby which the frequency, severity and relative risk of the loss of generation events can be quantified. This paper proposes a technique to empirically evaluate the risk of loss of generation events introduced by a singular site based on its simulated, or measured, historicity. This is accomplished through the statistical translation of meso-scale wind speeds into a number of wind speeds representative of those experienced by the turbines at a wind farm. An event series is derived describing the potential number of turbines in high wind speed shut down mode at the wind farm. Once the event series is obtained, survival analysis and the cox proportional hazards model are utilized to characterize a site and contrast multiple sites in terms of relative risk. The methodology is demonstrated for high wind speed shutdown events, but can be applied for any event of interest, such as ramp-rate exceedances and drop below cut-in.

关键词： Event modeling cox ph model survival analysis wind farm site characterization wind farm siting

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Survival analysis of breast cancer patients using machine learning models

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MULTIMEDIA TOOLS AND APPLICATIONS 2023年第20期82卷 30909-30928页

作者： Evangeline, I. Keren Kirubha, S. P. Angeline Precious, J. Glory SRM Inst Sci & Technol Dept Biomed Engn Chennai India SRM Inst Sci & Technol Dept Elect & Commun Engn Chennai India

Breast cancer is a fatal disease. There is no one treatment for breast cancer due to its heterogeneity in terms of response to treatment and prognosis. This study deals with identifying the key covariates responsible for the prognosis of breast cancer patients so that proper treatment can be administered which can improve the overall survival of the patients. The study utilizes the clinical and pathological features from the Molecular Taxonomy of Breast Cancer International Consortium dataset (METABRIC). Three models namely the cox Proportional hazards (coxph) model, random survival forests (RSF) model, and Deephit were utilized for survival prediction. Both the Random survival forests and Deephit model gave a Concordance Index (C-Index) of 0.86 and performed better than the cox ph model which provided a C-Index of 0.85. The most important covariate in the random survival forests model with the maximum absolute value was relapse-free status. Relapse-free status had a high positive correlation of 88% with the survival status of the patient. The cox model gave four important statistically significant covariates with P < 0.05. They are Age at Diagnosis, Estrogen Receptor (ER) Status, Progesterone Receptor (PR) Status, and tumor stage. Among these ER and PR status have a negative regression coefficient value which reduces the risk of hazard for the patients. Thus, the proposed work helps identify the important prognostic covariates and also aids clinicians in determining the type of treatment to be administered to the patients. Both the Random survival forests model and Deephit performed the best for survival prediction.

关键词： Breast cancer Survival prediction cox ph model Random survival forests model Deephit

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Prevalence and associated factors of teenage childbearing among Ethiopian women using semi-parametric and parametric proportional hazard and accelerated failure time models

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BMC WOMENS HEALTH 2024年第1期24卷 1-13页

作者： Agete, Ashefet Ayalew, Mesfin M. Admassu, Sebsebe Dessie, Zelalem G. Bahir Dar Univ Coll Sci Bahir Dar Ethiopia Univ KwaZulu Natal Sch Math Stat & Comp Sci Durban South Africa

BackgroundTeenage childbearing is a common issue for young people's sexual and reproductive health in the world, particularly in low-income countries, and affects teenagers between the ages of 13 and 19. According to several academics, adolescent pregnancy accounts for the majority of Ethiopia's population increase, and there has been little effort to address this threat. This study aimed to determine the prevalence and associated factors of the time to teenage childbearing in *** paper compares the results of the semi-parametric proportional hazard (ph), parametric ph, and accelerated failure time (AFT) models to find the model that best fits the data. The Akaike Information Criterion (AIC) was used to evaluate the performance of models examined in this investigation. Time to teenage childbearing was the study's outcome variable, while the analysis considered various independent variables. We analyze data from the 2016 National Demographic Health Survey to assess the influence of different risk factors on teenage pregnancy among Ethiopian *** of the 10,274 teenagers (aged between 13 and 19) who participated in the 2016 survey, 6,430 (62.59%) were parents. The study findings revealed that these teenage parents were influenced by various time-related factors before becoming parents. The log-normal AFT model has the lowest AIC value and hence it is the best fit for this data. Results from this model indicated that significant factors influencing the time to teenage childbearing include the age of the household head, current age of the respondents, region, religion, educational attainment, wealth status, intention to use contraception, and recent sexual *** study reveals that 62.59% of surveyed teenagers aged 13 to 19 were parents. Various factors at both the individual and community levels: including the age of the household head, regional differences, religious affiliation, educational level, economic status, contracep

关键词： AFT model cox ph model Parametric ph model Teenage childbearing

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Influence of contraceptive use and other socio-demographic factors on under-five child mortality in Bangladesh: semi-parametric and parametric approaches

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CONTRACEPTION AND REPRODUCTIVE MEDICINE 2023年第1期8卷 1-11页

作者： Khan, Golam Rabbi Baten, Abdul Azad, Md. Abul Kalam Jagannath Univ Dept Stat Dhaka 1100 Bangladesh

BackgroundThe under-five child mortality rate is a widely accepted indicator of the development of a country as well as reflects the country's health care system and quality of life. Although the child mortality rate is decreasing over time in Bangladesh, the rate is still high among South Asian countries. The target of the Sustainable Development Goal-3.2 is to reduce the under-five mortality rate in all countries of the world to 25 or fewer per 1000 live births by 2030. The purpose of this study is to identify the socio-demographic factors which have an influence on under-five child mortality in Bangladesh as well as to examine whether contraceptive use has any effect on under-five mortality in Bangladesh. Finally, a comparison has been made between the results obtained from the cox proportional Hazard model and Weibull model to find out which model is more efficient for the study *** the study, data was extracted from Bangladesh Demographic Health Surveys 2017-2018 (BDHS 2017-2018). The Kaplan-Meier survival function has been used to demonstrate the survival probabilities of under-five children. While multivariate analyses of the cox Proportional Hazard model and Weibull model are used to estimate the under-five mortality risks for various *** study results show consistently higher survival probabilities for children of mothers who used modern contraceptives during survival periods. Other significant predictors for under-five child mortality include mother's education level (higher education), mother's age (> 20), wealth index (rich), source of drinking water (tube well), and division (Chittagong, Khulna, Mymensingh). Weibull model has given more efficient results than the cox Proportional Hazard model except for one covariate (water source).ConclusionContraceptives use significantly improves the survival chances of children under-five age. This underscores the importance of contraceptive use in the pursuit of a sustainable reduction

关键词： Under-five mortality Bangladesh Contraceptive use Socio-demographic cox ph model Weibull model

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On the bankruptcy of internet companies: An empirical inquiry

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JOURNAL OF BUSINESS RESEARCH 2006年第1-11期59卷 1193-1200页

作者： Cochran, James J. Darrat, Ali F. Elkhal, Khaled Univ So Indiana Coll Business Dept Econ & Finance Evansville IN 47712 USA Louisiana Tech Univ Dept Mkt & Anal Ruston LA 71272 USA Louisiana Tech Univ Dept Econ & Finance Ruston LA 71272 USA

In this paper we investigate bankruptcy of Internet (dotcoms) companies. Using a calendar-time model, we identify three key predictors of company failure;net income to total assets, cash flow to total liabilities, and total assets. In addition, we use an event-time model and find that liquidity becomes more important as a predictor than profit potential about one year prior to the failure, but that this finding is reversed (i.e. liquidity is less important than profit potential) three years prior to bankruptcy. Our results also suggest that three years prior to bankruptcy, a higher ratio of total liabilities to total assets is associated with lower odds of survival. (c) 2006 Elsevier Inc. All rights reserved.

关键词： dotcom bankruptcy cox ph model calendar-time model event-time model sensitivity tests

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