scholarly journals Parametric regression model for response time in clinical trials – a bayesian approach

2017 ◽  
Vol 7 (1) ◽  
pp. 1-7 ◽  
Author(s):  
SUNDARAM N
Keyword(s):  
Clinical Trials ◽  
Monte Carlo ◽  
Regression Model ◽  
Survival Data ◽  
Regression Models ◽  
Bayesian Regression ◽  
The Other ◽  
Mcmc Methods ◽  
Censored Survival Data ◽  
Parametric Regression

In this paper an attempt has been made to model the censored survival data using Bayesian regressions with Markov Chain Monte Carlo (MCMC) methods. Bayesian LogNormal (LN) regression model are found to be providing better fit than the other Bayesian regression models namely Exponential (E), Generalized Exponential (GE), Webull (W), LogLogistic (LL) and Gamma (G).

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

2018 ◽  
Vol 2 (2) ◽  
pp. 28-35
Author(s):  
Gatri Eka Kusumawardhani ◽  
Vera Maya Santi ◽  
Suyono Suyono
Keyword(s):  
Logistic Regression ◽  
Regression Model ◽  
Survival Data ◽  
Regression Models ◽  
Logistic Regression Model ◽  
Likelihood Method ◽  
Censored Survival Data ◽  
Logistic Regression Models ◽  
Lung Cancer Patients ◽  
Time Required

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.

scholarly journals New Flexible Regression Models Generated by Gamma Random Variables with Censored Data

2016 ◽  
Vol 5 (3) ◽  
pp. 9 ◽  
Author(s):  
Elizabeth M. Hashimoto ◽  
Gauss M. Cordeiro ◽  
Edwin M.M. Ortega ◽  
G.G. Hamedani
Keyword(s):  
Regression Model ◽  
Censored Data ◽  
Survival Data ◽  
Regression Models ◽  
Empirical Distribution ◽  
Real Data ◽  
Maximum Likelihood Estimates ◽  
Standard Normal Distribution ◽  
Model Parameters ◽  
Linear Regression Models

We propose and study a new log-gamma Weibull regression model. We obtain explicit expressions for the raw and incomplete moments, quantile and generating functions and mean deviations of the log-gamma Weibull distribution. We demonstrate that the new regression model can be applied to censored data since it represents a parametric family of models which includes as sub-models several widely-known regression models and therefore can be used more effectively in the analysis of survival data. We obtain the maximum likelihood estimates of the model parameters by considering censored data and evaluate local influence on the estimates of the parameters by taking different perturbation schemes. Some global-influence measurements are also investigated. Further, for different parameter settings, sample sizes and censoring percentages, various simulations are performed. In addition, the empirical distribution of some modified residuals are displayed and compared with the standard normal distribution. These studies suggest that the residual analysis usually performed in normal linear regression models can be extended to a modified deviance residual in the proposed regression model applied to censored data. We demonstrate that our extended regression model is very useful to the analysis of real data and may give more realistic fits than other special regression models. 

Fitting regression models to censored survival data

1983 ◽  
Vol 2 (2) ◽  
pp. 287-293 ◽  
Author(s):  
M. Hudec ◽  
H. Platz
Keyword(s):  
Survival Data ◽  
Regression Models ◽  
Censored Survival Data

Bayesian survival analysis in clinical trials: What methods are used in practice?

2016 ◽  
Vol 14 (1) ◽  
pp. 78-87 ◽  
Author(s):  
Caroline Brard ◽  
Gwénaël Le Teuff ◽  
Marie-Cécile Le Deley ◽  
Lisa V Hampson
Keyword(s):  
Clinical Trials ◽  
Survival Analysis ◽  
Bayesian Methods ◽  
Regression Models ◽  
Final Analysis ◽  
Bayesian Regression ◽  
Phase Iii ◽  
Informative Priors ◽  
Primary Analysis ◽  
Survival Analyses

Background Bayesian statistics are an appealing alternative to the traditional frequentist approach to designing, analysing, and reporting of clinical trials, especially in rare diseases. Time-to-event endpoints are widely used in many medical fields. There are additional complexities to designing Bayesian survival trials which arise from the need to specify a model for the survival distribution. The objective of this article was to critically review the use and reporting of Bayesian methods in survival trials. Methods A systematic review of clinical trials using Bayesian survival analyses was performed through PubMed and Web of Science databases. This was complemented by a full text search of the online repositories of pre-selected journals. Cost-effectiveness, dose-finding studies, meta-analyses, and methodological papers using clinical trials were excluded. Results In total, 28 articles met the inclusion criteria, 25 were original reports of clinical trials and 3 were re-analyses of a clinical trial. Most trials were in oncology (n = 25), were randomised controlled (n = 21) phase III trials (n = 13), and half considered a rare disease (n = 13). Bayesian approaches were used for monitoring in 14 trials and for the final analysis only in 14 trials. In the latter case, Bayesian survival analyses were used for the primary analysis in four cases, for the secondary analysis in seven cases, and for the trial re-analysis in three cases. Overall, 12 articles reported fitting Bayesian regression models (semi-parametric, n = 3; parametric, n = 9). Prior distributions were often incompletely reported: 20 articles did not define the prior distribution used for the parameter of interest. Over half of the trials used only non-informative priors for monitoring and the final analysis (n = 12) when it was specified. Indeed, no articles fitting Bayesian regression models placed informative priors on the parameter of interest. The prior for the treatment effect was based on historical data in only four trials. Decision rules were pre-defined in eight cases when trials used Bayesian monitoring, and in only one case when trials adopted a Bayesian approach to the final analysis. Conclusion Few trials implemented a Bayesian survival analysis and few incorporated external data into priors. There is scope to improve the quality of reporting of Bayesian methods in survival trials. Extension of the Consolidated Standards of Reporting Trials statement for reporting Bayesian clinical trials is recommended.

scholarly journals Homoscedasticity: an overlooked critical assumption for linear regression

2019 ◽  
Vol 32 (5) ◽  
pp. e100148
Author(s):  
Kun Yang ◽  
Justin Tu ◽  
Tian Chen
Keyword(s):  
Monte Carlo Simulation ◽  
Monte Carlo ◽  
Linear Regression ◽  
Regression Models ◽  
Data Distribution ◽  
The Other ◽  
Simulation Studies ◽  
Linear Regression Models ◽  
Popular Belief

Linear regression is widely used in biomedical and psychosocial research. A critical assumption that is often overlooked is homoscedasticity. Unlike normality, the other assumption on data distribution, homoscedasticity is often taken for granted when fitting linear regression models. However, contrary to popular belief, this assumption actually has a bigger impact on validity of linear regression results than normality. In this report, we use Monte Carlo simulation studies to investigate and compare their effects on validity of inference.

Parametric Survival Modelling of Risk Factor of Tuberculosis Patients under DOTS Program at Hawassa Town, Ethiopia

2015 ◽  
Vol 4 (1) ◽  
pp. 1-17 ◽  
Author(s):  
Fikadu Zawdie Chere ◽  
Yohannes Yebabe Tesfay ◽  
Fikre Enquoselassie
Keyword(s):  
Regression Model ◽  
Survival Data ◽  
Information Criterion ◽  
Major Health Problem ◽  
Weibull Regression ◽  
Tuberculosis Patients ◽  
Parametric Regression ◽  
Time To Death ◽  
Baseline Weight ◽  
Weibull Regression Model

Tuberculosis (TB) is a chronic infectious disease that has a major health problem over the centuries. This study assessed the risk factors associated with time to death among TB patients treated under directly observed short course treatment program in Health facilities in Hawassa city, Ethiopia. The authors analysed data from a cohort of 1604 TB patients recruited between September 2008 to September 2011. They apply the parametric regression model of survival data analysis. The best fitted parametric regression model is selected by using the Akaike information criterion (AIC). The AIC confirms that the Weibull regression model is found to be the best fit of the survival of tuberculosis patients under the DOTS program at Hawassa town, Ethiopia. The fit of the Weibull regression model result revealed that sex, age, baseline weight, HIV status, category of patients and year of enrolment are the significant factor for the survival of TB patients.

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