Key result
Survival tree analysis matches complex Cox models for predicting incident CHD using few predictors.
Why the study?
Risk patterns associated with incident coronary heart disease and the comparative performance of survival tree versus Cox proportional hazards models in an adult cohort were not well characterized.
Does survival tree analysis perform comparably to Cox proportional hazards modeling for predicting incident coronary heart disease in Iranian adults?
Cohort (n=8,279)
No
Does survival tree analysis perform comparably to Cox proportional hazards modeling for predicting incident coronary heart disease in Iranian adults?
Answers are grounded in this paper and cite their sources.
Survival tree analysis provides a parsimonious alternative to Cox proportional hazards models for predicting incident coronary heart disease, effectively identifying key risk factor interactions.
We identified risk patterns associated with incident coronary heart disease (CHD) using survival tree, and compared performance of survival tree versus Cox proportional hazards (Cox PH) in a cohort of Iranian adults. Data on 8,279 participants (3,741 men) aged ≥30 yr were used to analysis. Survival trees identified seven subgroups with different risk patterns using four [(age, non-HDL-C, fasting plasma glucose (FPG) and family history of diabetes] and five [(age, systolic blood pressure (SBP), non-HDL-C, FPG and family history of CVD] predictors in women and men, respectively. Additional risk factors were identified by Cox models which included: family history of CVD and waist circumference (in both genders); hip circumference, former smoking and using aspirin among men; diastolic blood pressure and lipid lowering drug among women. Survival trees and multivariate Cox models yielded comparable performance, as measured by integrated Brier score (IBS) and Harrell's C-index on validation datasets; however, survival trees produced more parsimonious models with a minimum number of well recognized risk factors of CHD incidence, and identified important interactions between these factors which have important implications for intervention programs and improve clinical decision making.
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Ramezankhani et al. (2017) conducted a cohort in Coronary heart disease (n=8,279). Risk factor patterns (age, fasting plasma glucose, systolic blood pressure, non-HDL-C, family history) vs. Low-risk covariate patterns was evaluated on Incident coronary heart disease (CHD). Survival tree analysis identified distinct risk patterns for incident coronary heart disease using only 4 to 5 predictors, yielding predictive performance comparable to complex Cox proportional hazards models.
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