Trends colliding: aging comprehensive family physicians and the growing complexity of their patients
Premji K, Glazier RH, Green ME, Khan S, Schultz S, Mathews M, Nastos S, Frymire E, Ryan BL. Can Fam Physician. 2025 Jun 16.
Objective — There is a growing interest in using classification and regression trees in biomedical research. R and S-PLUS are two statistical programming languages that share a similar syntax and functionality. Both R and S-PLUS allow users to fit classification and regression trees. The objective was to compare classification trees grown using R with those grown using S-PLUS.
Study Design and Setting — Using data on 9,484 patients hospitalized with an acute myocardial infarction, we compared the classification trees for predicting mortality that were grown using R and S-PLUS. We also used repeated split-sample derivation to determine the predictive accuracy of classification trees grown using R and S-PLUS.
Results — The classification tree grown using R was substantially more parsimonious than the one grown using S-PLUS. The pruned classification tree grown using R was equal to a classification tree that was obtained by removing six subtrees from the pruned classification tree grown using S-PLUS. Repeated split-sample validation was then used to demonstrate that classification trees constructed using S-PLUS had greater discrimination and accuracy compared to classification trees grown using R.
Conclusions — R can produce different classification trees than S-PLUS using the same data.
Austin PC. J Clin Epidemiol. 2008; 61(12):1222-6. Epub 2008 Jul 10.
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