We compared respondents and non-respondents according to key demographic characteristics from the 2006 American Medical Association (AMA) Masterfile in addition to specialty and hospital affiliation data provided by the physicians’ organization whose affiliates were surveyed. We defined physicians as primary care physicians (PCPs) if their primary specialty was internal medicine (with no additional subspecialty), family medicine, general practice, preventive medicine, geriatrics, or general osteopathy. All other physicians were classified as medical or surgical specialists, or “other” (e.g. psychiatry). To assess differences between the characteristics of respondents and non-respondents, we used χ2 and t-tests, as appropriate. We compared the names given by respondents in the name generator section of the survey to patient-sharing relationships in Medicare data by first matching the names with the 2006 Medicare Provider Identification File (MPIER) to obtain unique UPIN identifiers for the named physicians. The pairs of respondents and named physicians were then compared with all patient-sharing relationships identified in the Medicare claims database based on claims for 100% of Medicare patients residing in the Boston hospital referral region. We assessed differences in proportions of relationships recognized by respondents using the two-proportion z-test with Yates’ continuity correction (Pagano and Gauvreau 2000 ).
The “number of patients shared” based on administrative data can be thought of as a diagnostic test for the existence of a reported relationship between two physicians. Given this, we calculated a receiver-operating characteristic (ROC) curve for predicting physician reported relationships based on the number of patients shared. To assess overall predictive accuracy, we computed the area under the ROC curve and its standard error by adapting Harrell’s c statistic as calculated using the rcorr.cens() function in the Hmisc package (version 3.8-2) implemented in the R statistical programming language (version 2.11) (Hanley and McNeil 1982 (link); Harrell Jr. 2010 ; Newson 2006 ) To visualize the network of physicians based on the relationships measured using administrative data and reported in the survey sample, we used the Kamada-Kawai algorithm as implemented in the igraph package in R (Csardi and Nepusz 2006 ; Fruchterman and Reingold 1991 ; Kamada and Kawai 1989 ). All tests of statistical significance were two-sided. All analyses were conducted using R statistical software, version 2.11.1 (R Development Core Team 2009 ). The study protocol was approved by the Harvard Medical School Committee on Human Studies.
The “number of patients shared” based on administrative data can be thought of as a diagnostic test for the existence of a reported relationship between two physicians. Given this, we calculated a receiver-operating characteristic (ROC) curve for predicting physician reported relationships based on the number of patients shared. To assess overall predictive accuracy, we computed the area under the ROC curve and its standard error by adapting Harrell’s c statistic as calculated using the rcorr.cens() function in the Hmisc package (version 3.8-2) implemented in the R statistical programming language (version 2.11) (Hanley and McNeil 1982 (link); Harrell Jr. 2010 ; Newson 2006 ) To visualize the network of physicians based on the relationships measured using administrative data and reported in the survey sample, we used the Kamada-Kawai algorithm as implemented in the igraph package in R (Csardi and Nepusz 2006 ; Fruchterman and Reingold 1991 ; Kamada and Kawai 1989 ). All tests of statistical significance were two-sided. All analyses were conducted using R statistical software, version 2.11.1 (R Development Core Team 2009 ). The study protocol was approved by the Harvard Medical School Committee on Human Studies.