Graphical representation of survival curves associated with a binary non-reversible time dependent covariate

1992 ◽  
Vol 11 (4) ◽  
pp. 455-474 ◽  
Author(s):  
Eric J. Feuer ◽  
Benjamin F. Hankey ◽  
Jeffrey J. Gaynor ◽  
Margaret N. Wesley ◽  
Stuart G. Baker ◽  
...  
Keyword(s):  
Graphical Representation ◽  
Time Dependent ◽  
Survival Curves ◽  
Reversible Time

Graphical representation of survival curves in the presence of time-dependent categorical covariates with application to liver transplantation

2018 ◽  
Vol 46 (9) ◽  
pp. 1702-1713
Author(s):  
Abigail R. Smith ◽  
Nathan P. Goodrich ◽  
Charlotte A. Beil ◽  
Qian Liu ◽  
Robert M. Merion ◽  
...  
Keyword(s):  
Liver Transplantation ◽  
Graphical Representation ◽  
Time Dependent ◽  
Survival Curves

scholarly journals Systematic Analysis of the Relationship Between Ovarian Cancer Prognosis and Alternative Splicing

2021 ◽  
Author(s):  
Di Zhang ◽  
Dan Zou ◽  
Yue Deng ◽  
Lihua Yang
Keyword(s):  
Regression Analysis ◽  
Operating Characteristic ◽  
Cox Regression ◽  
Risk Groups ◽  
Time Dependent ◽  
Survival Curves ◽  
Cox Regression Analysis ◽  
Prognostic Analysis ◽  
Independent Analysis ◽  
The Relationship

Abstract Background: Ovarian cancer(OC) is the gynecological tumor with the highest mortality rate, effective biomarkers are of great significance in improving its prognosis. In recent years, there have been many studies on alternative splicing (AS) events, and the role of AS events in tumor has become a focus of attention.Methods: Data were downloaded from the TCGA database and Univariate Cox regression analysis was performed to determine AS events associated with OC prognosis. Eight prognostic models of OC were constructed in R package, and the accuracy of the models were evaluated by the time-dependent receiver operating characteristic (ROC) curves. Eight types of survival curves were drawn to evaluate the differences between the high and low risk groups. Independent prognostic factors of OC were analyzed by single factor independent analysis and multi-factor independent prognostic analysis. Again, Univariate Cox regression analysis was used to analyze the relationship between splicing factors(SF) and AS events, and Gene Ontology(GO) and Kyoto Encyclopedia of Genes and Genomes(KEGG) enrichment analysis were performed on OS-related SFs to understand the pathways.Results: Univariate Cox regression analysis showed that among the 15,278 genes, there were 31,286 overall survival (OS) related AS events, among which 1524 AS events were significantly correlated with OS. The area under the time-dependent receiver operating characteristic curve (AUC) of AT and ME were the largest and the RI was the smallest ,which were 0.757 and 0.68 respectively. The constructed models have good value for the prognosis assessment of OC patients. Among the eight survival curves, AP was the most significant difference between the high and low risk groups, with a P value of 1.61e−1.The results of single factor independent analysis and multi-factor independent prognostic analysis showed that risk score calculated by the model and age could be used as independent risk factors. According to univariate COX regression analysis ,109 SFs were correlated with AS events and adjusted in two ways: positive and negative.Conclusions: SFs and AS events can directly or indirectly affect the prognosis of OC patients. It is very important to find effective prognostic markers to improve the survival rate of OC.

scholarly journals SURVIVAL MODELS IN THE CONTEXT OF MULTIPLICATIVE AND ADDITIVE HAZARDS

2019 ◽  
Vol 37 (3) ◽  
pp. 306
Author(s):  
Suely Ruiz GIOLO ◽  
Jaqueline Aparecida RAMINELLI
Keyword(s):  
Survival Analysis ◽  
Primary Biliary Cirrhosis ◽  
Goodness Of Fit ◽  
Operating Characteristic ◽  
Graphical Representation ◽  
Predictive Accuracy ◽  
Time Dependent ◽  
Survival Models ◽  
Biliary Cirrhosis ◽  
Additive Hazards

In survival analysis, multiplicative and additive hazards models provide the two principal frameworks to study the association between the hazard and covariates. When these models are considered for analyzing a given survival dataset, it becomes relevant to evaluate the overall goodness-of-fit and how well each model can predict those subjects who subsequently will or will not experience the event. In this paper, this evaluation is based on a graphical representation of the Cox-Snell residuals and also on a time-dependent version of the area under the receiver operating characteristic (ROC) curve, denoted by AUC(t). A simulation study is carried out to evaluate the performance of the AUC(t) as a tool for comparing the predictive accuracy of survival models. A dataset from the Mayo Clinic trial in primary biliary cirrhosis  (PBC) of the liver is also considered to illustrate the usefulness of these tools to compare survival models formulated under distinct hazards frameworks.

scholarly journals Effects of Variable Production Rate and Time-Dependent Holding Cost for Complementary Products in Supply Chain Model

2017 ◽  
Vol 2017 ◽  
pp. 1-13 ◽  
Author(s):  
Mitali Sarkar ◽  
Sun Hur ◽  
Biswajit Sarkar
Keyword(s):  
Supply Chain ◽  
Production Rate ◽  
Graphical Representation ◽  
Stackelberg Game ◽  
Time Dependent ◽  
Chain Model ◽  
Complementary Products ◽  
Supply Chain Model ◽  
Holding Cost ◽  
Variable Production

Recently, a major trend is going to redesign a production system by controlling or making variable the production rate within some fixed interval to maintain the optimal level. This strategy is more effective when the holding cost is time-dependent as it is interrelated with holding duration of products and rate of production. An effort is made to make a supply chain model (SCM) to show the joint effect of variable production rate and time-varying holding cost for specific type of complementary products, where those products are made by two different manufacturers and a common retailer makes them bundle and sells bundles to end customers. Demand of each product is specified by stochastic reservation prices with a known potential market size. Those players of the SCM are considered with unequal power. Stackelberg game approach is employed to obtain global optimum solution of the model. An illustrative numerical example, graphical representation, and managerial insights are given to illustrate the model. Results prove that variable production rate and time-dependent holding cost save more than existing literature.

scholarly journals Multi objective fuzzy inventory model with deterioration, price and time dependent demand and time dependent holding cost

2020 ◽  
Vol 11 (3) ◽  
pp. 928
Author(s):  
Satya Kumar Das ◽  
Sahidul Islam
Keyword(s):  
Graphical Representation ◽  
Fuzzy Programming ◽  
Inventory Model ◽  
Time Dependent ◽  
Selling Price ◽  
Multi Objective ◽  
Holding Cost ◽  
Fuzzy Inventory ◽  
Cost Parameters ◽  
Dependent Demand

In this paper, we have formulated an inventory model with time dependent holding cost, selling price as well as time dependent demand. Multi-item inventory model has been considered under limitation on storage space. Due to uncertainty all the require cost parameters are taken as generalized trapezoidal fuzzy number. Our proposed multi-objective inventory model has been solved by using fuzzy programming techniques which are FNLP, FAGP, WFNLP and WFAGP methods. A numerical example is provided to demonstrate the application of the model. Finally to illustrate the model and sensitivity analysis and graphical representation have been shown. 

Application of Adjusted Survival Curves to Renal Transplant Data

1992 ◽  
Vol 31 (03) ◽  
pp. 210-214 ◽  
Author(s):  
R. Bender ◽  
A. Schultz ◽  
R. Pichlmayr ◽  
U. Grouven
Keyword(s):  
Renal Transplant ◽  
Graphical Representation ◽  
Survival Curve ◽  
Time Data ◽  
Survival Curves ◽  
Data Set ◽  
Important Means ◽  
Multifactorial Approach ◽  
Parametric Survival ◽  
Survival Time Data

Abstract:An important means in the analysis of survival time data is the estimation and graphical representation of survival probabilities. In this paper unifactorial parametric and non-parametric survival curve estimators and two types of adjusted survival curves based on a parametric multifactorial approach are applied to renal transplant data. It is shown that the resulting survival curves can differ substantially. The unifactorial survival curves yield biased results in case of serious disequilibrium in the data. This drawback of the unifactorial methods has been overcome by the use of adjusted survival curves which take possible distortions in the data set into account. The benefits of adjusted survival curves in assessing potentially prognostic factors are elucidated by the application to data from renal transplantation.

scholarly journals Approximate Analytical Solution of Advection-Dispersion Equation By Means of OHAM.

2018 ◽  
Vol 7 (1) ◽  
pp. 15-20
Author(s):  
D J Prajapati ◽  
N B Desai
Keyword(s):  
Analytical Solution ◽  
Dispersion Equation ◽  
Graphical Representation ◽  
Approximate Analytical Solution ◽  
Time Dependent ◽  
Analysis Method ◽  
Optimal Homotopy Analysis Method ◽  
Advection Dispersion ◽  
Homotopy Analysis ◽  
Input Source

This work deals with the analytical solution of advection dispersion equation arising in solute transport along unsteady groundwater flow in finite aquifer. A time dependent input source concentration is considered at the origin of the aquifer and it is assumed that the concentration gradient is zero at the other end of the aquifer. The optimal homotopy analysis method (OHAM) is used to obtain numerical and graphical representation.

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