Research Article Logic Regression for Provider Effects on Kidney Cancer Treatment Delivery Mousumi Banerjee, 1,2 Christopher Filson, 3 Rong Xia, 1 and David C. Miller 2,3 1 Department of Biostatistics, School of Public Health, University of Michigan, 1415 Washington Heights, Ann Arbor, MI 48109, USA 2 Center for Healthcare Outcomes & Policy, University of Michigan, 2800 Plymouth Road, Ann Arbor, MI 48105, USA 3 Department of Urology, University of Michigan, 1500 E Medical Center Drive, Ann Arbor, MI 48109, USA Correspondence should be addressed to Mousumi Banerjee; mousumib@umich.edu Received 9 January 2014; Accepted 28 February 2014; Published 27 March 2014 Academic Editor: Zhenyu Jia Copyright © 2014 Mousumi Banerjee et al. his is an open access article distributed under the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited. In the delivery of medical and surgical care, oten times complex interactions between patient, physician, and hospital factors inluence practice patterns. his paper presents a novel application of logic regression in the context of kidney cancer treatment delivery. Using linked data from the National Cancer Institute’s (NCI) Surveillance, Epidemiology, and End Results (SEER) program and Medicare we identiied patients diagnosed with kidney cancer from 1995 to 2005. he primary endpoints in the study were use of innovative treatment modalities, namely, partial nephrectomy and laparoscopy. Logic regression allowed us to uncover the interplay between patient, provider, and practice environment variables, which would not be possible using standard regression approaches. We found that surgeons who graduated in or prior to 1980 despite having some academic ailiation, low volume surgeons in a non-NCI hospital, or surgeons in rural environment were signiicantly less likely to use laparoscopy. Surgeons with major academic ailiation and practising in HMO, hospital, or medical school based setting were signiicantly more likely to use partial nephrectomy. Results from our study can show eforts towards dismantling the barriers to adoption of innovative treatment modalities, ultimately improving the quality of care provided to patients with kidney cancer. 1. Introduction Open radical nephrectomy has long been the standard treat- ment for patients with early-stage kidney cancer [1]. In recent years, however, easier convalescence and equivalent cancer control established laparoscopy as an alternative standard of care for most patients treated with radical nephrectomy [1–3]. Studies have also demonstrated that, for patients with small renal masses, partial instead of radical nephrectomy achieves identical cancer control while better preserving long-term renal function and reducing overtreatment of benign or clinically indolent tumors [4–7]. However, despite their potential advantages, the adoption of laparoscopy and partial nephrectomy have been relatively slow and asymmetric in the population [3, 8]. Earlier studies have shown that individual surgeon char- acteristics and their practice environments largely inluence the use of laparoscopy and partial nephrectomy [9]. hese studies are based on logistic regression models, a member of the generalized linear model family suitable for data with a binary outcome (e.g., use versus nonuse of laparoscopy). Logistic regression focuses on identiication of main efects. While interactions can be assessed using logistic regression, these interactions need to be known a priori and speciied as input variables in the model. Discovery of interactions is therefore diicult using logistic regression. We hypothesize that surgeon characteristics may not have uniform efect on the adoption of laparoscopy and partial nephrectomy across practice environments. For example, use of advanced tech- niques may vary among recently trained surgeons depending on the surgeon’s ailiation with an academic hospital or NCI-designated cancer center, suggesting a potential interac- tion between year of medical school graduation and practice setting. Logic regression is an adaptive classiication and regres- sion procedure [10], initially developed to uncover and Hindawi Publishing Corporation Computational and Mathematical Methods in Medicine Volume 2014, Article ID 316935, 9 pages http://dx.doi.org/10.1155/2014/316935