Regression Model For Survival Data Research Articles

Mortality and health-related quality of life in older adults with long-term use of opioids, z-hypnotics or benzodiazepines: a prospective observational study at 5 years follow-up

ObjectivesDisease and medication use in older age is a consequence of age-related declining health. Multimorbidity followed by polypharmacy is common. Central nervous system depressing (CNSD) drugs such as opioids, benzodiazepines...

Quantile regression models for survival data with missing censoring indicators.

The quantile regression model has increasingly become a useful approach for analyzing survival data due to its easy interpretation and flexibility in exploring the dynamic relationship between a time-to-event outcome and the covariates. In this paper, we consider the quantile regression model for survival data with missing censoring indicators. Based on the augmented inverse probability weighting technique, two weighted estimating equations are developed and corresponding easily implemented algorithms are suggested to solve the estimating equations. Asymptotic properties of the resultant estimators and the resampling-based inference procedures are established. Finally, the finite sample performances of the proposed approaches are investigated in simulation studies and a real data application.

Censored quantile regression survival models with a cure proportion

A new quantile regression model for survival data is proposed that permits a positive proportion of subjects to become unsusceptible to recurrence of disease following treatment or based on other observable characteristics. In contrast to prior proposals for quantile regression estimation of censored survival models, we propose a new “data augmentation” approach to estimation. Our approach has computational advantages over earlier approaches proposed by Wu and Yin (2013, 2017). We compare our method with the two estimation strategies proposed by Wu and Yin and demonstrate its advantageous empirical performance in simulations. The methods are also illustrated with data from a Lung Cancer survival study.

ANALISIS SURVIVAL UNTUK DURASI PROSES KELAHIRAN MENGGUNAKAN MODEL REGRESI HAZARD ADDITIF

Survival data is the length of time until an event occurs. If the survival time is affected by other factor, it can be modeled with a regression model. The regression model for survival data is commonly based on the Cox proportional hazard model. In the Cox proportional hazard model, the covariate effect act multiplicatively on unknown baseline hazard. Alternative to the multiplicative hazard model is the additive hazard model. One of the additive hazard models is the semiparametric additive hazard model that introduced by Lin Ying in 1994. The regression coefficient estimates in this model mimic the scoring equation in the Cox model. Score equation of Cox model is the derivative of the Partial Likelihood and methods to maximize partial likelihood with Newton Raphson iterasi. Subject from this paper is describe the multiplicative and additive hazard model that applied to the duration of the birth process. The data is obtained from two different clinics,there are clinic that applies gentlebirth method while the other one no gentlebirth. From the data processing obtained the factors that affect on the duration of the birth process are baby’s weight, baby’s height and method of birth. Keywords: survival, additive hazard model, cox proportional hazard, partial likelihood, gentlebirth, duration

Joint Modeling of a Longitudinal Measurement and Parametric Survival Data with Application to Primary Biliary Cirrhosis Study

Although longitudinal and survival data are collected in the same study, they are usually analyzed separately. Measurement errors and missing data problems arise because of separate analysis of these two data. Therefore, joint model should be used instead of separate analysis. The standard joint model frequently used in the literature is obtained by combining the linear mixed effect model of longitudinal data and Cox regression model with survival data. Nevertheless, to use the Cox regression model for survival data, the assumption of proportional hazards must be provided. Parametric survival sub-models should be used instead of the Cox regression model for the survival sub-model of the JM where the assumption is not provided. In this article, parametric joint modeling of longitudinal data and survival data that do not provide the assumption of proportional hazards are investigated. For the survival data, the model with Exponential, Weibull, Log-normal, Log-logistic, and Gamma accelerated failure time models and the linear mixed effect model are combined with random effects and the models were applied in primary biliary cirrhosis data set obtained from Mayo Clinic. After determining the best parametric joint model according to Akaike and Bayesian information criterions, the best available model was compared with standard joint model and of separate analysis of survival data and longitudinal data. As a results, in the studies where longitudinal and survival data are obtained together, it is seen that the parametric joint model gives more better results than the standard joint model when the proportional hazard assumption is not provided.

Properties of non-parametric Stute estimators

In this paper, we use the linear regression model for survival data, explaining that it corresponds to an accelerated time model of lifetime see as described in Kalbfleisch and Prentice [12] and Koul et al. [15]. In this context, we adapt the jumps of the KM estimator as defined in Lopez [16] to the accelerated lifetime model. The introduction of a more restrictive hypothesis allows us to establish a strong consistency property of the Stute [24] estimator obtained by minimizing the sum of the least squares. Using the asymptotic normality of the bivariate distribution estimator proposed by Stute [26] and the Slutsky theorem, we succeed in establishing the asymptotic distribution of the Stute [24]estimator.

Ten Years’ Survival in Patients with Cervical Cancer and Related Factors in West Azerbaijan Province: Using of Cox Proportion Hazard Model

Objective:There are two basic ways to analyze survival data including nonparametric and parametric methods. Considering that cervical cancer is the fourth most common cancer among women, this study was conducted using a suitable regression model for survival data for patients with cervical cancer in Urmia.Material and Methods:In this historical retrospective study, all patients with cervical cancer who referred to Motahhari Hospital of Urmia during 2004 and 2015 were included in the study and consisted of 109 women with cervical cancer. The data were collected using checklist which filled with records of patients. The data were described with percent, mean and standard deviation. The survival function was computed with the Kaplan-Meier method. The adjusted Hazard Ratio for variables were estimated after fitting the Cox proportion hazard model using Forward Stepwise Likelihood Ratio method with PE=0.1, PR=0.15.Results:Of the 109 patients, the mean (SD) time of diagnosis was 50.1% (11.7) years. The mean (SD) and median of follow-up time was 38.23 (32.50) and 27.1 months (Mim: 2 months and Max: 132 months). Ninty-Three of patients (88.7%) were in urban area. Using Log-Rank test, the mean score of survival in patients with recurrence of 38.8 months was significantly lower than those without recurrence (102.5 months) (P <0.001). The results of the COX model showed that the adjusted relative risk of mortality with a diagnostic age of 50 years or more compared to those under 50 years of age was 1.978. The risk for marriage under age 20 was 3.189 compared to the marriage age of 20 years or more.Conclusion:According to results, the low age of marriage increases the occurrence of advanced stages of cancer in older ages and as a social and cultural factor has a significant effect on the survival of patients with cervical cancer.

Analisis Survival dengan Model Regresi pada Data Tersensor Berdistribusi Log-Logistik

Survival analysis is an analysis used to determine the length of time required by an object in order to survive. That time is sometimes influenced by several factors called independent variables. One way to know relationship is through a regression model. The dependent variable in this regression model is a survival time which is log-logistic distributed. The data used in this study were right censored survival data. Log-logistic regression models for survival data can be expressed by transformation Y=lnT= θ0+θ1xi1+...+θixij+σԑ. The parameter of the log-logistic regression models for right censored survival data are estimated with the maximum likelihood method. In this study, the application of log-logistic regression model for survival data is in data of lung cancer patients. Based on the data already performed, best log-logistic regression model is obtained yi=1.92458+0.0242393 xi1+0.639037ԑi.

EM algorithm-based likelihood estimation for a generalized Gompertz regression model in presence of survival data with long-term survivors: an application to uterine cervical cancer data

ABSTRACTIn this paper we develop a regression model for survival data in the presence of long-term survivors based on the generalized Gompertz distribution introduced by El-Gohary et al. [The generalized Gompertz distribution. Appl Math Model. 2013;37:13–24] in a defective version. This model includes as special case the Gompertz cure rate model proposed by Gieser et al. [Modelling cure rates using the Gompertz model with covariate information. Stat Med. 1998;17:831–839]. Next, an expectation maximization algorithm is then developed for determining the maximum likelihood estimates (MLEs) of the parameters of the model. In addition, we discuss the construction of confidence intervals for the parameters using the asymptotic distributions of the MLEs and the parametric bootstrap method, and assess their performance through a Monte Carlo simulation study. Finally, the proposed methodology was applied to a database on uterine cervical cancer.

Comparison of Cox Regression and Parametric Models: Application for Assessment of Survival of Pediatric Cases of Acute Leukemia in Southern Iran

Background:Finding the most appropriate regression model for survival data in cancer casesin order to determine prognosis is an important issue in medical research. Here we compare Cox and parametric regression models regarding survival of children with acute leukemia in southern Iran.Methods:In a retrospective cohort study, information for 197 children with acute leukemia over 6 years was collected through observation and interviews. In order to identify factors affecting their survival, the Cox and parametric (exponential, Weibull, log-logistic, log-normal, Gompertz and generalized gamma) models were fitted to the data. To find the best predictor model, the Akaike’s information criterion (AIC) and the Coxsnell residual were employed.Results:Out of 197 children, 164 (83.3%) had ALL and 33 (16.7%) AML; the mean (± standard deviation) survival time was 52.1±8.10 months. According to both the AIC and the Coxsnell residual, the Cox regression model was the weakest and the log-normal and Weibull models were the best for fitting to data. Based on the log-normal model, age (HR=1.01, p=0.004), residence area (HR=1.60, p=0.038) and WBC (White Blood Cell) (HR=1.57, p=0.014) had significant effects on patient survival.Conclusion:Parametric regression models demonstrate better performance as compared to the Cox model for identifying risk factors for prognosis with acute leukemia data. Just because the assumption of PH (Proportional Hazards) is held for the Cox regression model, we should not ignore parameter models.

Quantifying the effect of elevation and aspect on fire return intervals in the Canadian Rocky Mountains

The effect of topography on wildfire distribution in the Canadian Rockies has been the subject of debate. We suspect the size of the study area, and the assumption fire return intervals are distributed as a Weibull distribution used in many previous studies may have obscured the real effect of topography on these fire-regulated ecosystems. The objective of this study was to quantify the effects of elevation, aspect, slope and dominant species on probabilities of burning. The study area covered three natural subregions: Subalpine, Montane, and Upper Foothills of the Rocky Mountains of southern Alberta, Canada. Fire return interval (FRI) data from 870 fire history sampling sites were stratified by subregion and analyzed with the non-parametric Kaplan-Meier survival model and Cox Proportional Hazards (PH) regression model for survival data. The natural subregions were found to have distinct FRI distributions. The Kaplan-Meier probability median historical (pre-1948) FRI values ranged from 26 to 35 years for the Montane, was 39 years for the Upper Foothills and ranged between 65 and 85 years for the Subalpine. Within each subregion elevation was the lead predictor variable influencing probabilities of burning, and its significant PH ratio indicated the effect of elevation was maintained through time. For every 100m of increased elevation, the probability of burning decreased by 12%, 21% and 33% for the Montane, Upper Foothills and Subalpine, respectively. Aspect was only significant in the Subalpine with a PH ratio indicating warm aspects are twice as likely (105%) to burn than cool aspects. The combined topographic elements of high elevation and cool aspect produce the longest FRIs and are where fire refugia (old growth) tend to form. A salient finding of our study was the multiplicative effect that cool aspect has on burning probabilities of forests located at low or high elevation, as well as the interchangeable effect of some topographic combinations in the Subalpine. Our findings do not support the established assumptions from previous studies that the Little Ice Age was responsible for a change in the burning rate, nor that probabilities of burning are spatially homogeneous in mountain landscapes. The combined effect of aspect and elevation on FRIs need to be integrated when writing fire prescriptions for ecological restoration, and be part of adaptive forest management strategies working towards maintaining fire refugia patches. Timber managers aiming to emulate natural disturbances will also need to consider the effect of topography when planning seral stage distributions in mountainous landscapes.

Latent cure rate model under repair system and threshold effect

In this paper, we formulate a simple latent cure rate model with repair mechanism for a cell exposed to radiation. This latent approach is a flexible alternative to the models proposed by Klebanov et al. [A stochastic model of radiation carcinogenesis: latent time distributions and their properties. Math Biosci. 1993;18:51–75], Kim et al. [A new threshold regression model for survival data with a cure fraction. Lifetime Data Anal. 2011;17:101–122], and is along the lines of the destructive cure rate model formulated recently by Rodrigues et al. [Destructive weighted Poisson cure rate model. Lifetime Data Anal. 2011b;17:333–346]. A new version of the modified Gompertz model and the promotion cure rate model that takes into account the first passage time of reaching the critical point are discussed, and the estimation of tumor size at detection is then addressed from the Bayesian viewpoint. In addition, a simulation study and an application to real data set illustrate the usefulness of the proposed cure rate model.

Yaşam Çözümlemesinde Hızlandırılmış Başarısızlık Süresi Modelleri ve Bir Uygulama

The Cox regression model is the most commonly used regression model for survival data. The Cox regression model has an important assumption that hazard rates are proportional over time. The accelerated failure time model is an alternative method for the analysis of survival data when hazards are not proportional. The accelerated failure time models should lead to more efficient parameter estimates than Cox model under certain circumferences. In this study, The accelerated failure time models are described. The data of stomach cancer patients is used to illustrate these models and the results are discussed

Review of Case-Mix Corrected Survival Curves

Survival is an end point of immense interest in cardiothoracic research. In observational studies, the comparison of survival between groups of patients is usually accomplished using a toolbox that includes Kaplan-Meier survival curves and the Cox model. The Cox model yields comparisons between groups adjusted for case-mix differences, whereas the Kaplan-Meier is a plot of survival over time without adjustment. During the past decade, new methods have emerged for case-mix adjustment of survival curves and are increasingly being used in cardiothoracic research. The purpose of this report is to describe, illustrate, and review several approaches to case-mix adjusted survival (or event-free) curves.

Evaluation of the Salmonella surveillance program in Belgian pig farms

Since July 2007, Belgium has implemented a national surveillance and control program for Salmonella in pigs. Pig farms are designated as Salmonella high risk farms based on serological profiles and are obliged to subsequently take part in a Salmonella specific action plan (SSAP). The SSAP was evaluated and potential risk factors for the persistence of Salmonella on the farm were investigated. First, the Kaplan–Meier method was used in order to study the period during which a farm maintains a Salmonella high risk status. Farms recovered more slowly from their high risk status before the SSAP was implemented compared to after the program was implemented (p<0.001). However, results showed that 29% of the farms were withdrawn from the program possibly only because of sampling error. The program should thus be adapted to accommodate for this. Secondly, the influence of several risk factors (type of farm, season of entrance into the SSAP, size of farm and farm density of the municipality) on the time to withdrawal from the high risk status was evaluated using univariable methods and a Cox multiple regression model for survival data. A statistically significant association was identified between the type of farm and the time to withdrawal from the high risk status. At any point in time after the onset of the SSAP, withdrawals from the high risk status occurred at a 39% and 28% higher rate in mixed (p=0.01) and fattening farms (p=0.05), respectively, compared to closed farms. The risk attributed to closed pig farms is related to the presence of sows in this particular type of structure, which underlines the importance of implementing control measures in this category of animals.

A new threshold regression model for survival data with a cure fraction

Due to the fact that certain fraction of the population suffering a particular type of disease get cured because of advanced medical treatment and health care system, we develop a general class of models to incorporate a cure fraction by introducing the latent number N of metastatic-competent tumor cells or infected cells caused by bacteria or viral infection and the latent antibody level R of immune system. Various properties of the proposed models are carefully examined and a Markov chain Monte Carlo sampling algorithm is developed for carrying out Bayesian computation for model fitting and comparison. A real data set from a prostate cancer clinical trial is analyzed in detail to demonstrate the proposed methodology.

Bayesian Semiparametric Proportional Odds Models

Methodology for implementing the proportional odds regression model for survival data assuming a mixture of finite Polya trees (MPT) prior on baseline survival is presented. Extensions to frailties and generalized odds rates are discussed. Although all manner of censoring and truncation can be accommodated, we discuss model implementation, regression diagnostics, and model comparison for right-censored data. An advantage of the MPT model is the relative ease with which predictive densities, survival, and hazard curves are generated. Much discussion is devoted to practical implementation of the proposed models, and a novel MCMC algorithm based on an approximating parametric normal model is developed. A modest simulation study comparing the small sample behavior of the MPT model to a rank-based estimator and a real data example is presented.

Group-Sequential Analysis Incorporating Covariate Information

In this article we survey existing results concerning the joint distribution of the sequence of estimates of the parameter vector when a model is fitted to accumulating data and provide a unified theory that explains the “independent increments” structure commonly seen in group-sequential test statistics. Our theory covers normal linear models, including the case of correlated observations, and asymptotic results extend to generalized linear models and the proportional hazards regression model for survival data. The asymptotic results are derived using standard methods for the nonsequential case, and they hold as long as these nonsequential techniques are applicable at each individual analysis. In all cases, the joint distribution of the sequence of parameter estimates has the same form, exactly or asymptotically, as that of the sequence of means of an increasing number of independent, identically distributed normal variables. Thus our results provide the formal basis for extending the scope of standard group-sequential methods to a wide range of problems.

Survival modeling through recursive stratification

This paper presents a strategy for checking the goodness-of-fit of a regression model for survival data, based on analysis of residuals. In the event that a lack of fit is found, a method of stratification of the data is proposed that permits separate regression models to be fitted to each stratum. Recursive stratification yields a tree structure that provides information about the predictive power of the covariates as well as any interactions among them.

Semiparametric Marshall-Olkin Models Applied to the Occurrence of Metastases at Multiple Sites after Breast Cancer

It is noted that the bivariate exponential distribution introduced by Marshall and Olkin (1967, Journal of the American Statistical Association 62, 30-40) allows semiparametric generalizations along the lines of the Cox regression model for survival data. Partial likelihoods for the regression parameters may be derived (here illustrated by the use of the profile likelihood construction), and in most cases standard Cox regression model software may be applied for the analysis with minor modification of the input files. The study was initiated by data on occurrence of metastases from breast cancer. Metastases may occur at various sites, here grouped into ten categories, and simultaneous as well as consecutive occurrence at several sites in common. It is desired to identify and compare risk factors for development of metastases at each site, and we illustrate on some of these data that the above models may be useful for this purpose.