Abstract
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.
Highlights
According to the World Health Organization (WHO), in 2005, 30% of the total death was due to cardiovascular diseases (CVD), mainly heart disease and stroke[1]
The aim of the present study was to use this type of survival trees to identify relative importance of factors contributing to the incidence of CHD, and detecting the subgroups with different survival functions based on related covariates
Results of multivariate Cox proportional hazards (Cox PH) model in women showed that, age, WC, fasting plasma glucose (FPG), non-high density lipoprotein cholesterol (HDL-C), DBP, family history (FH) of premature CVD in male relatives and using lipid lowering drugs were positively related to the incidence of CHD in women (Table 5)
Summary
According to the World Health Organization (WHO), in 2005, 30% of the total death was due to cardiovascular diseases (CVD), mainly heart disease and stroke[1]. Recursive partitioning or ‘decision trees’, the relatively recently developed methodology, are another class of nonparametric regressions which have been widely used in many fields[12, 13, 17,18,19] These methods provide a very flexible framework without pre-specifying the interactions. Survival trees are popular nonparametric alternatives to the Cox PH regression models which have been extended to survival analysis[12] They can naturally group subjects according to their length of survival based on their covariates patterns[12, 14]. We used the prospective cohort database from Tehran Lipid and Glucose Study (TLGS) for our analysis
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