Estimates of absolute treatment benefit for individual patients required careful modeling of statistical interactions
Journal of Clinical Epidemiology , Volume 68 - Issue 11 p. 1366- 1374
Objectives We aimed to compare modeling approaches to estimate the individual survival benefit of treatment with either coronary artery bypass graft surgery (CABG) or percutaneous coronary intervention (PCI) for patients with complex coronary artery disease. Study Design and Setting We estimated survival with Cox regression models that included the treatment variable (CABG/PCI) interacting with either an internally developed overall prognostic index (PI) or with individual prognostic factors. We analyzed data of patients who were randomized in the Synergy between Percutaneous Coronary Intervention with Taxus and Cardiac Surgery trial (1,800 patients, 178 deaths). Results A negligible interaction with the PI (P = 0.51) led to 4-year survival estimates in favor of CABG for all patients. In contrast, individual interactions indicated substantial relative treatment effect heterogeneity (overall interaction P = 0.004), and estimates of 4-year survival were numerically in favor of CABG for 1,275 of 1,800 patients (71%; 519 with 95% confidence). To test the more complex model with individual interactions, we first used penalized regression, resulting in smaller but largely consistent individual estimates of the survival difference between CABG and PCI. Second, strong treatment interactions were confirmed at external validation in 2,891 patients from a multinational registry. Conclusion Modeling strategies that omit interactions may result in misleading estimates of absolute treatment benefit for individual patients with the potential hazard of suboptimal decision making.
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|Journal of Clinical Epidemiology|
|Organisation||Department of Cardio-Thoracic Surgery|
van Klaveren, D, Vergouwe, Y, Farooq, V, Serruys, P.W.J.C, & Steyerberg, E.W. (2015). Estimates of absolute treatment benefit for individual patients required careful modeling of statistical interactions. Journal of Clinical Epidemiology, 68(11), 1366–1374. doi:10.1016/j.jclinepi.2015.02.012