scholarly journals Development and Validation of a Novel Prognostic Model for Endometrial Cancer Based on Clinical Characteristics

2021 ◽  
Vol Volume 13 ◽  
pp. 8879-8886
Author(s):  
Zhicheng Yu ◽  
Sitian Wei ◽  
Jun Zhang ◽  
Rui Shi ◽  
Lanfen An ◽  
...  
Keyword(s):  
Endometrial Cancer ◽  
Clinical Characteristics ◽  
Prognostic Model ◽  
Development And Validation

scholarly journals The development and validation of prognostic models for overall survival in the presence of missing data in the training dataset: a strategy with a detailed example

2021 ◽  
Vol 5 (1) ◽  
Author(s):  
Kara-Louise Royle ◽  
David A. Cairns
Keyword(s):  
Overall Survival ◽  
Missing Data ◽  
Prognostic Model ◽  
Prognostic Index ◽  
Risk Groups ◽  
Prognostic Models ◽  
Training Dataset ◽  
Validation Process ◽  
Test Dataset ◽  
Development And Validation

Abstract Background The United Kingdom Myeloma Research Alliance (UK-MRA) Myeloma Risk Profile is a prognostic model for overall survival. It was trained and tested on clinical trial data, aiming to improve the stratification of transplant ineligible (TNE) patients with newly diagnosed multiple myeloma. Missing data is a common problem which affects the development and validation of prognostic models, where decisions on how to address missingness have implications on the choice of methodology. Methods Model building The training and test datasets were the TNE pathways from two large randomised multicentre, phase III clinical trials. Potential prognostic factors were identified by expert opinion. Missing data in the training dataset was imputed using multiple imputation by chained equations. Univariate analysis fitted Cox proportional hazards models in each imputed dataset with the estimates combined by Rubin’s rules. Multivariable analysis applied penalised Cox regression models, with a fixed penalty term across the imputed datasets. The estimates from each imputed dataset and bootstrap standard errors were combined by Rubin’s rules to define the prognostic model. Model assessment Calibration was assessed by visualising the observed and predicted probabilities across the imputed datasets. Discrimination was assessed by combining the prognostic separation D-statistic from each imputed dataset by Rubin’s rules. Model validation The D-statistic was applied in a bootstrap internal validation process in the training dataset and an external validation process in the test dataset, where acceptable performance was pre-specified. Development of risk groups Risk groups were defined using the tertiles of the combined prognostic index, obtained by combining the prognostic index from each imputed dataset by Rubin’s rules. Results The training dataset included 1852 patients, 1268 (68.47%) with complete case data. Ten imputed datasets were generated. Five hundred twenty patients were included in the test dataset. The D-statistic for the prognostic model was 0.840 (95% CI 0.716–0.964) in the training dataset and 0.654 (95% CI 0.497–0.811) in the test dataset and the corrected D-Statistic was 0.801. Conclusion The decision to impute missing covariate data in the training dataset influenced the methods implemented to train and test the model. To extend current literature and aid future researchers, we have presented a detailed example of one approach. Whilst our example is not without limitations, a benefit is that all of the patient information available in the training dataset was utilised to develop the model. Trial registration Both trials were registered; Myeloma IX-ISRCTN68454111, registered 21 September 2000. Myeloma XI-ISRCTN49407852, registered 24 June 2009.

scholarly journals Development and Validation of a Prognostic Model for Patients with Advanced Lung Cancer Treated with the Immune Checkpoint Inhibitor Atezolizumab

2020 ◽  
Vol 26 (13) ◽  
pp. 3280-3286 ◽  
Author(s):  
Ashley M. Hopkins ◽  
Ganessan Kichenadasse ◽  
Elizabeth Garrett-Mayer ◽  
Christos S. Karapetis ◽  
Andrew Rowland ◽  
...  
Keyword(s):  
Lung Cancer ◽  
Immune Checkpoint ◽  
Prognostic Model ◽  
Immune Checkpoint Inhibitor ◽  
Checkpoint Inhibitor ◽  
Advanced Lung Cancer ◽  
Development And Validation

scholarly journals Development and Validation of a Nomogram Prognostic Model for SCLC Patients

2018 ◽  
Vol 13 (9) ◽  
pp. 1338-1348 ◽  
Author(s):  
Shidan Wang ◽  
Lin Yang ◽  
Bo Ci ◽  
Matthew Maclean ◽  
David E. Gerber ◽  
...  
Keyword(s):  
Prognostic Model ◽  
Development And Validation

Development and validation of a prognostic model for survival time data: application to prognosis of HIV positive patients treated with antiretroviral therapy

2004 ◽  
Vol 23 (15) ◽  
pp. 2375-2398 ◽  
Author(s):  
Margaret May ◽  
Patrick Royston ◽  
Matthias Egger ◽  
Amy C. Justice ◽  
Jonathan AC Sterne ◽  
...  
Keyword(s):  
Antiretroviral Therapy ◽  
Survival Time ◽  
Prognostic Model ◽  
Hiv Positive ◽  
Time Data ◽  
Data Application ◽  
Development And Validation ◽  
Survival Time Data

Clinical characteristics and outcomes of women with stage IV endometrial cancer

2009 ◽  
Vol 27 (4) ◽  
pp. 1371-1377 ◽  
Author(s):  
M. Tanioka ◽  
N. Katsumata ◽  
Y. Sasajima ◽  
S. Ikeda ◽  
T. Kato ◽  
...  
Keyword(s):  
Endometrial Cancer ◽  
Clinical Characteristics ◽  
Stage Iv

scholarly journals Development and validation of a prognostic model to predict clinical deterioration in hospitalized patients with COVID-19

2021 ◽  
Author(s):  
Javid Azadbakht ◽  
Sina Rashedi ◽  
Soheil Kooraki ◽  
Hamed Kowsari ◽  
Elnaz Tabibian
Keyword(s):  
Roc Curve ◽  
Scoring System ◽  
Prognostic Model ◽  
Operating Characteristic ◽  
Clinical Laboratory ◽  
External Validation ◽  
Clinical Deterioration ◽  
Roc Curve Analysis ◽  
Prospective Multicenter Study ◽  
Development And Validation

Abstract Objectives We aimed to develop and validate a prognostic model to predict clinical deterioration defined as either death or intensive care unit admission of hospitalized COVID-19 patients.Methods This prospective, multicenter study investigated 172 consecutive hospitalized COVID-19 patients who underwent a chest computed tomography (CT) scan between March 20 and April 30, 2020 (development cohort), as well as an independent sample of 40 consecutive patients for external validation (validation cohort). The clinical, laboratory, and radiologic data were gathered, and logistic regression along with receiver operating characteristic (ROC) curve analysis was performed.Results The overall clinical deterioration rates of the development and validation cohorts were 28.4% (49 of 172) and 30% (12 of 40), respectively. Seven predictors were included in the scoring system with a total score of 15: CT severity score\(\ge\)15 (Odds Ratio (OR)=6.34, 4 points), pleural effusion (OR = 6.80, 2 points), symptom onset to admission ≤ 6 days (OR = 2.44, 2 points), age\(\ge\)70 years (OR = 2.44, 2 points), diabetes mellitus (OR = 2.24, 2 points), dyspnea (OR = 2.17, 1.5 points), and abnormal leukocyte count (OR = 1.89, 1.5 points). The area under the ROC curve for the scoring system in the development and validation cohorts was 0.823 (CI [0.751–0.895]) and 0.558 (CI [0.340–0.775]), respectively.Conclusion This study provided a new easy-to-calculate scoring system with external validation for hospitalized COVID-19 patients to predict clinical deterioration based on a combination of seven clinical, laboratory, and radiologic parameters.

scholarly journals Development and validation of a TP53-associated immune prognostic model for hepatocellular carcinoma

EBioMedicine ◽  
2019 ◽  
Vol 42 ◽  
pp. 363-374 ◽  
Author(s):  
Junyu Long ◽  
Anqiang Wang ◽  
Yi Bai ◽  
Jianzhen Lin ◽  
Xu Yang ◽  
...  
Keyword(s):  
Hepatocellular Carcinoma ◽  
Prognostic Model ◽  
Development And Validation
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