Related Experiment Video
Updated: Nov 30, 2025

Establishing a Competing Risk Regression Nomogram Model for Survival Data
Published on: October 23, 2020
Derivation and validation of a simple nomogram prediction model for all-cause mortality among middle-aged and elderly
Lin Liu1, Kenneth Lo1, Cheng Huang1
1Department of Cardiology, Guangdong Provincial Key Laboratory of Coronary Heart Disease Prevention, Guangdong Cardiovascular Institute, Guangdong Provincial People's Hospital, Guangdong Academy of Medical Sciences, Guangzhou, China.
Background:
A simple clinical model that can predict all-cause mortality in the middle-aged and older adults in general population based on demographics and physical measurement indicators. The aim of this study was to develop a simple nomogram prediction model for all-cause mortality in middle-aged and elderly general population based on demographics and physical measurement indicators.
Methods:
This was a prospective cohort study. We used data from the 1999-2006 National Health and Nutrition Examination Survey (NHANES), which included adults aged ≥40 years with mortality status updated through 31 December 2015. Cox proportional hazards regression, nomogram and least absolute shrinkage and selection operator (LASSO) binomial regression model were performed to evaluate the prediction model in the derivation and validation cohort.
Results:
A total of 13,026 participants (6,414 men, mean age was 61.59±13.80 years) were included, of which 6,671 (3,263 men) and 6,355 (3,151 men) were included in the derivation cohort and validation cohort, respectively. During an average follow-up period of 129.23±9.62 months, 4,321 died. We developed a 9-item nomogram mode included age, gender, smoking, alcohol intake, diabetes, hypertension, marriage status, education and poverty to income ratio (PIR). The area under the curve (AUC) was 0.842 and had good calibration. Internal validation showed good discrimination of the nomogram model with AUC of 0.849 and good calibration. Application of the LASSO regression model in the validation cohort also revealed good discrimination (AUC =0.854) and good calibration. A time-dependent and optimism-corrected AUC value for the model showed no significant relationship with the change of follow-up time.
Conclusions:
A simple nomogram model, including age, gender, smoking, alcohol intake, diabetes, hypertension, marriage, education and PIR, could predict all-cause mortality well in middle-aged and elderly general population.
Related Concept Videos
Drug Dosing in Renal Diseases: Estimation of Glomerular Filtration Rate Based on Serum Creatinine Concentration
Dosage Regimen Designs: Nomograms and Tabulations
Pharmacokinetics in Geriatric Patients: Effect of Age on Drug Excretion
Actuarial Approach
Consider the example of a high-risk surgical procedure with significant early-stage mortality. A two-year clinical study is conducted,...
Life Tables
Drug Dosing: Geriatric Patients

