Related Experiment Video
Updated: Jun 1, 2026

Cutoff Value of Phase Angle by Bioelectrical Impedance Analysis at Admission as a Prognostic Factor in Patients with Acute Heart Failure
Published on: June 10, 2025
Simulation of quality-adjusted survival in chronic diseases: an application in type 2 diabetes
Alison J Hayes1, Philip M Clarke1, Merryn Voysey2
1School of Public Health, University of Sydney, NSW, Australia (AJH, PMC)
Background:
Recent studies have demonstrated that measures of health-related quality of life can predict complications and mortality in patients with diabetes, even after adjustment for clinical risk factors.
Methods:
The authors developed a simulation model of disease progression in type 2 diabetes to investigate the impact of patient quality of life on lifetime outcomes and its potential response to therapy. Changes in health utility over time are captured as a result of complications and aging. All risk equations, model parameter estimates, and input data were derived from patient-level data from the Fenofibrate Intervention and Event Lowering in Diabetes (FIELD) trial.
Results:
Healthier patients with type 2 diabetes enjoy more life years, quality-adjusted life years (QALYs), and more life years free of complications. A 65-year-old patient at full health (utility = 1) can expect to live approximately 2 years longer and achieve 6 more QALYs than a patient at average health (utility = 0.8), given similar clinical risk factors. For patients with higher EQ-5D utility, the additional years lived without complications contribute more to longer life expectancy than years lived with complications.
Conclusions:
The authors have developed a model for progression of disease in diabetes that has a number of novel features; it captures the observed relationships between measures of quality of life and future outcomes, the number of states have been minimized, and it can be parameterized with just 4 risk equations. Underlying the simple model structure is important patient-level heterogeneity in health and outcomes. The simulations suggest that differences in patients' EQ-5D utility can account for large differences in QALYs, which could be relevant in cost-utility analyses.
Related Concept Videos
Cancer Survival Analysis
Comparing the Survival Analysis of Two or More Groups
Actuarial Approach
Consider the example of a high-risk surgical procedure with significant early-stage mortality. A two-year clinical study is conducted,...
Assumptions of Survival Analysis
Introduction To Survival Analysis
The primary goal of survival analysis is to estimate survival time—the time until a...
Kaplan-Meier Approach