Ranking Weibull Survival Model: Boosting the Concordance Index of the Weibull Time-to-Event Prediction Model with Ranking Losses

2021 ◽  
pp. 43-56
Author(s):  
Kseniia Cheloshkina
Keyword(s):  
Prediction Model ◽  
Survival Model ◽  
Concordance Index ◽  
Time To Event ◽  
Event Prediction

scholarly journals Development of a Survival Model Based on Autophagy-Associated Genes for Predicting Prognosis of Gastric Cancer

2020 ◽  
Author(s):  
Wanli Yang ◽  
Lili Duan ◽  
Xinhui Zhao ◽  
Liaoran Niu ◽  
Yiding Li ◽  
...  
Keyword(s):  
Gastric Cancer ◽  
Regression Analysis ◽  
Prediction Model ◽  
Roc Curve ◽  
Survival Model ◽  
Differentially Expressed ◽  
Lasso Regression ◽  
Prognosis Prediction ◽  
Cellular Processes ◽  
Model Based

Abstract Background: Gastric cancer (GC) is one of lethal diseases worldwide. Autophagy-associated genes play a crucial role in the cellular processes of GC. Our study aimed to investigate and identify the prognostic potential of autophagy-associated genes signature in GC. Methods: RNA-seq and clinical information of GC and normal controls were downloaded from The Cancer Genome Atlas (TCGA) database. Then, the Wilcoxon signed-rank test was used to pick out the differentially expressed autophagy-associated genes. Gene Ontology (GO) and Kyoto Encyclopedia of Genes and Genomes (KEGG) analyses were performed to investigate the potential roles and mechanisms of autophagy-associated genes in GC. Cox proportional hazard regression analysis and Lasso regression analysis were carried out to identify the overall survival (OS) related autophagy-associated genes, which were then collected to construct a predictive model. Kaplan-Meier method and receiver operating characteristic (ROC) curve were utilized to validate the accuracy of this model. Finally, a clinical nomogram was established by combining the clinical factors and autophagy-associated genes signature. Results: A total of 28 differentially expressed autophagy-associated genes were identified. GO and KEGG analyses revealed that several important cellular processes and signaling pathways were correlated with these genes. Through Cox regression and Lasso regression analyses, we identified 4 OS-related autophagy-associated genes (GRID2, ATG4D, GABARAPL2, and CXCR4) and constructed a prognosis prediction model. GC Patients with high-risk had a worse OS than those in low-risk group (5-year OS, 27.7% vs 38.3%; P=9.524e-07). The area under the ROC curve (AUC) of the prediction model was 0.67. The nomogram was demonstrated to perform better for predicting 3-year and 5-year survival possibility for GC patients with a concordance index (C-index) of 0.70 (95% CI: 0.65-0.72). The calibration curves also presented good concordance between nomogram-predicted survival and actual survival. Conclusions: We constructed and evaluated a survival model based on the autophagy-associated genes for GC patients, which may improve the prognosis prediction in GC.

scholarly journals A practical perspective on the concordance index for the evaluation and selection of prognostic time-to-event models

2020 ◽  
Vol 108 ◽  
pp. 103496
Author(s):  
Enrico Longato ◽  
Martina Vettoretti ◽  
Barbara Di Camillo
Keyword(s):  
Concordance Index ◽  
Time To Event ◽  
Event Models ◽  
Selection Of

scholarly journals Systematic review on event prediction models

2020 ◽  
Vol 19 (3) ◽  
pp. 1490
Author(s):  
Fakhruzy Shakirin Jamaludin ◽  
R Kanesaraj Ramasamy
Keyword(s):  
Systematic Review ◽  
Prediction Model ◽  
Hybrid Algorithm ◽  
Prediction Models ◽  
Hybrid Algorithms ◽  
Event Prediction ◽  
Different Types ◽  
Prediction Systems ◽  
A Company

Predicting an event can comes as a huge advantage for a lot of people, especially if it involves profits that a company can make. In this paper, matters that were discussed were the algorithms used for the event prediction systems, the type of algorithms and the comparison between different types of algorithm. Two main categories that were compared was the single algorithm for the prediction model and hybrid algorithms which is a combination of two or more algorithms in the model. Hybrid algorithm shows signs of better efficiency when data used were complex and large but a smaller dataset is more suitable with a single algorithm as the base as it is easier to apply with satisfactory results.

scholarly journals A Prediction Model of Extubation Failure Risk in Preterm Infants

2021 ◽  
Vol 9 ◽  
Author(s):  
Zimei Cheng ◽  
Ziwei Dong ◽  
Qian Zhao ◽  
Jingling Zhang ◽  
Su Han ◽  
...  
Keyword(s):  
Acute Respiratory Distress Syndrome ◽  
Respiratory Distress Syndrome ◽  
Respiratory Distress ◽  
Prediction Model ◽  
Preterm Infants ◽  
Gestational Age ◽  
Distress Syndrome ◽  
Extubation Failure ◽  
Concordance Index ◽  
Training Cohort

Objectives: This study aimed to identify variables and develop a prediction model that could estimate extubation failure (EF) in preterm infants.Study Design: We enrolled 128 neonates as a training cohort and 58 neonates as a validation cohort. They were born between 2015 and 2020, had a gestational age between 250/7 and 296/7 weeks, and had been treated with mechanical ventilation through endotracheal intubation (MVEI) because of acute respiratory distress syndrome. In the training cohort, we performed univariate logistic regression analysis along with stepwise discriminant analysis to identify EF predictors. A monogram based on five predictors was built. The concordance index and calibration plot were used to assess the efficiency of the nomogram in the training and validation cohorts.Results: The results of this study identified a 5-min Apgar score, early-onset sepsis, hemoglobin before extubation, pH before extubation, and caffeine administration as independent risk factors that could be combined for accurate prediction of EF. The EF nomogram was created using these five predictors. The area under the receiver operator characteristic curve was 0.824 (95% confidence interval 0.748–0.900). The concordance index in the training and validation cohorts was 0.824 and 0.797, respectively. The calibration plots showed high coherence between the predicted probability of EF and actual observation.Conclusions: This EF nomogram was a useful model for the precise prediction of EF risk in preterm infants who were between 250/7 and 296/7 weeks' gestational age and treated with MVEI because of acute respiratory distress syndrome.

Demonstrating the Performance of Accelerated Failure Time Model over Cox-PH Model of Survival Data Analysis with Application to HIV-Infected Patients under HAART

2019 ◽  
Author(s):  
Getnet Begashaw
Keyword(s):  
Marital Status ◽  
Functional Status ◽  
Survival Data ◽  
Logistic Model ◽  
Clinical Stage ◽  
Survival Model ◽  
Cd4 Count ◽  
Cd4 Cells ◽  
Time To Event ◽  
Significant Difference

Abstract Background: Human Immunodeficiency Virus (HIV) is a virus that kills CD4 cells. These CD4 cells are white blood cells that fight infection. CD4 count is like a snapshot of how well our immune system is functioning. Studying the way of CD4+ count over time provides an insight to the disease evolution. Methods: This study was considering the data of HIV/AIDS patients who were undergoing Antiretroviral Therapy in the ART clinic of Menellik II Referral Hospital, Addis Ababa, Ethiopia, during the period 1st January 2014 to 31st December 2017. The data was analyzed in separate survival models i.e non parametric, semi parametric (Cox PH) and parametric survival model (AFT models). For the purpose of model diagnosis cox-snail residual analysis were incorporated. Results: For separate survival model log-logistic model is more appropriate for the survival data than other parametric models. Therefore; functional status and regimen class are significant covariates in determining the hazard function patients. . In the Log-logistic model, among the covariates we have included in the survival model: functional status (working subgroup) and regimen class (all subgroup) were significant at 5% level of significance. But, sex, age, baseCD4, marital status and WHO-clinical stage are not significance at 5% significance level. Using cox-snail residual shows proportionality not satisfied for these WHO stage, regimen class and marital status. Conclusions: Log rank and Wilcoxon tests showed that the significant difference in survival situation among the categorical variables selected for this study sex, marital status, functional status, WHO-clinical stages and regimen class subgroups. But, there was no significant difference in the time-to-event between subgroups of sex, Marital Status and WHO clinical Stage, while, Regimen Class and Functional Status there was a significant difference in the time-to-event between subgroups.

scholarly journals A framework for meta-analysis of prediction model studies with binary and time-to-event outcomes

2018 ◽  
Vol 28 (9) ◽  
pp. 2768-2786 ◽  
Author(s):  
Thomas PA Debray ◽  
Johanna AAG Damen ◽  
Richard D Riley ◽  
Kym Snell ◽  
Johannes B Reitsma ◽  
...  
Keyword(s):  
Prediction Model ◽  
Prediction Models ◽  
Meta Analysis ◽  
Predictive Performance ◽  
R Package ◽  
Small Samples ◽  
Time To Event ◽  
Model Studies ◽  
Framingham Risk ◽  
Study Heterogeneity

It is widely recommended that any developed—diagnostic or prognostic—prediction model is externally validated in terms of its predictive performance measured by calibration and discrimination. When multiple validations have been performed, a systematic review followed by a formal meta-analysis helps to summarize overall performance across multiple settings, and reveals under which circumstances the model performs suboptimal (alternative poorer) and may need adjustment. We discuss how to undertake meta-analysis of the performance of prediction models with either a binary or a time-to-event outcome. We address how to deal with incomplete availability of study-specific results (performance estimates and their precision), and how to produce summary estimates of the c-statistic, the observed:expected ratio and the calibration slope. Furthermore, we discuss the implementation of frequentist and Bayesian meta-analysis methods, and propose novel empirically-based prior distributions to improve estimation of between-study heterogeneity in small samples. Finally, we illustrate all methods using two examples: meta-analysis of the predictive performance of EuroSCORE II and of the Framingham Risk Score. All examples and meta-analysis models have been implemented in our newly developed R package “metamisc”.

scholarly journals Nomogram based on immune scores for predicting the survival of patients with esophageal squamous cell carcinoma

2021 ◽  
Vol 49 (4) ◽  
pp. 030006052110096
Author(s):  
Wentao Wu ◽  
Wen Ma ◽  
Daning Li ◽  
Shuai Zheng ◽  
Fanfan Zhao ◽  
...  
Keyword(s):  
Overall Survival ◽  
Squamous Cell Carcinoma ◽  
Esophageal Squamous Cell Carcinoma ◽  
Prediction Model ◽  
Cell Carcinoma ◽  
Calibration Curve ◽  
Concordance Index ◽  
Clinical Prediction ◽  
Clinical Prediction Model ◽  
Immune Score

Objective To explore the relationship between immune scores and prognosis of patients with esophageal squamous cell carcinoma (ESCC) and construct a corresponding clinical prediction model. Methods The present research was a retrospective cohort study. We obtained the clinical information and immune scores of 137 patients with ESCC from The Cancer Genome Atlas database, and a Cox proportional risk model was used to construct the clinical prediction model. The concordance index, receiver operating characteristic curve, calibration curve, net reclassification improvement (NRI), and integrated discrimination improvement (IDI) were used to evaluate model performance and prediction accuracy. Results Patients with a high immune score (> −121.4) showed a worse prognosis than those with a low immune score (< −645.8; hazard ratio=3.743, 95% confidence interval [CI]=1.385–10.115, P=0.009). The concordance index of the predictive model was 0.733 (95% CI=0.655–0.812). The calibration curve showed that the 3- and 5-year overall survival rates predicted by the model were highly consistent with the observed values. The NRI and IDI for the 3-year overall survival indicated that the model with the immune scores was superior for classifying the risk probability and distinguishing cases. Conclusion Immune scores may be an independent predictor of prognosis in patients with ESCC.

scholarly journals A novel nomogram to predict the risk of anastomotic leakage in patients after oesophagectomy

2020 ◽  
Author(s):  
Chengya Huang ◽  
Haixia Yao ◽  
Qi Huang ◽  
Huijie Lu ◽  
Meiying Xu ◽  
...  
Keyword(s):  
Prediction Model ◽  
Anastomotic Leakage ◽  
Cardiac Arrhythmia ◽  
Discrimination Performance ◽  
Smoking History ◽  
Multivariable Logistic Regression Analysis ◽  
Consecutive Series ◽  
Concordance Index ◽  
Internal Validation ◽  
Type Reconstruction

Abstract Background: Anastomotic leakage is a dangerous postoperative complication of oesophageal surgery. The present study aimed to develop a simple and practical scoring system to predict the risk of anastomotic leakage after oesophageal resection. Methods: A consecutive series of 330 patients who underwent oesophageal cancer surgery from January 2016 to January 2018 at the Shanghai Chest Hospital were included to develop a prediction model. Anastomotic leakage was evaluated using oesophagography, computed tomography, or flexible endoscopy. Least absolute shrinkage and selection operator regression based on a generalized linear model was used to select variables for the anastomotic leakage risk model while avoiding overfitting. Multivariable logistic regression analysis was applied to build forest plots and a prediction model. The concordance index or the area under the curve was used to judge the discrimination, and calibration plots verified the consistency. Internal validation of the model was conducted, and the clinical usefulness and threshold screening of the model were evaluated by decision curve analysis. Results: The factors included in the predictive nomogram included Sex, diabetes history, anastomotic type, reconstruction route, smoking history, CRP level and presence of cardiac arrhythmia. The model displayed a discrimination performance with a concordance index of 0.690 (95% confidence interval: 0.620-0.760) and good calibration. A concordance index value of 0.664 was maintained during the internal validation. The calibration curve showed good agreement between the actual observations and the predicted results. Conclusion: The present prediction model, which requires only seven variables and includes Sex, diabetes history, anastomotic type, reconstruction route, smoking history, CRP level and presence of cardiac arrhythmia, may be useful for predicting anastomotic leakage in patients after oesophagectomy.

Development and validation of a Super learner-based model for predicting survival in Chinese Han patients with resected colorectal cancer

2020 ◽  
Vol 50 (10) ◽  
pp. 1133-1140
Author(s):  
Jiqing Li ◽  
Jianhua Gu ◽  
Yuan Lu ◽  
Xiaoqing Wang ◽  
Shucheng Si ◽  
...  
Keyword(s):  
Colorectal Cancer ◽  
Overall Survival ◽  
Confidence Interval ◽  
Prediction Model ◽  
Prediction Models ◽  
External Validation ◽  
Concordance Index ◽  
Cox Regression Analysis ◽  
Prognosis Prediction ◽  
Super Learner

Abstract Objective Improved prognostic prediction for patients with colorectal cancer stays an important challenge. This study aimed to develop an effective prognostic model for predicting survival in resected colorectal cancer patients through the implementation of the Super learner. Methods A total of 2333 patients who met the inclusion criteria were enrolled in the cohort. We used multivariate Cox regression analysis to identify significant prognostic factors and Super learner to construct prognostic models. Prediction models were internally validated by 10-fold cross-validation and externally validated with a dataset from The Cancer Genome Atlas. Discrimination and calibration were evaluated by Harrell concordence index (C-index) and calibration plots, respectively. Results Age, T stage, N stage, histological type, tumor location, lymph-vascular invasion, preoperative carcinoembryonic antigen and sample lymph nodes were integrated into prediction models. The concordance index of Super learner-based prediction model (SLM) was 0.792 (95% confidence interval: 0.767–0.818), which is higher than that of the seventh edition American Joint Committee on Cancer TNM staging system 0.689 (95% confidence interval: 0.672–0.703) for predicting overall survival (P &lt; 0.05). In the external validation, the concordance index of the SLM for predicting overall survival was also higher than that of tumor-node-metastasis (TNM) stage system (0.764 vs. 0.682, respectively; P &lt; 0.001). In addition, the SLM showed good calibration properties. Conclusions We developed and externally validated an effective prognosis prediction model based on Super learner, which offered more reliable and accurate prognosis prediction and may be used to more accurately identify high-risk patients who need more active surveillance in patients with resected colorectal cancer.

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