Schymura et al. BMC Cancer 2010, 10:152 http://www.biomedcentral.com/1471-2407/10/152 ness. Codes for the 17 conditions that make up the Charl- son index were consolidated into a summary Charlson measure [9]. Insurance status was also determined from medical records. Private insurance included those with either private insurance or Medicare with a supplement. Public insurance included those with Medicare, Medicaid or welfare, or other federally funded health insurance. For a small proportion of patients, insurance type was indi- cated as "other". Each patient's address was geocoded at the participat- ing registry to the census tract level. The records were subsequently linked by census tract to the following socio-demographic indicators, derived from the 2000 census data: poverty (<20% versus 20%+ of residents below the 2000 poverty level); education (<25% versus 25%+ of residents age twenty-five and over with less than a high school education); working class status (<66% ver- sus 66%+ working class occupations); and urban-rural residence (totally urban, totally rural, urban-rural mix, or unknown). Indicators were selected based on the work of Nancy Krieger [10]. Vital status as of December 31, 2002, was obtained from medical records, physician offices, and by linking to state vital statistics files and to the National Death Index (NDI) for 1997 through 2002. At the time funding for the study ended, 2002 was the last year for which data from NDI were available. Initial treatment was defined as treatment received within the first six months following diagnosis. A hierar- chical variable was created to categorize treatment, rang- ing from the most aggressive to the least aggressive [11]. Men who received radical prostatectomy were assigned to radical prostatectomy, whether or not they received any other therapy. Men who received radiation therapy (external beam or interstitial) were assigned to radiation therapy, whether or not they also received hormone ther- apy. The hormone therapy category was comprised of men who only received hormone therapy (medical or sur- gical). Men who had no record of any therapy within the first six months following diagnosis were assigned to the watchful waiting category. Whether or not cases with missing treatment data were excluded from analysis depended on what treatment data were missing relative to the treatment hierarchy. For example, patients with missing surgery information were excluded. However, patients with missing hormone information were still eli- gible for inclusion in the radical prostatectomy or radia- tion therapy categories. Patients who died within six months of diagnosis were deleted from all analyses because determination of treatment mode was based on the six month post-diagnosis timeframe. Data Analyses We examined the proportion of patients receiving each type of therapy by sociodemographic and clinical factors. Page 3 of 15 Bivariate analysis was used to examine the association between the four major treatment options and patient and tumor characteristics. The analytic variables included age at diagnosis, race/ethnicity, marital status, insurance, education, poverty, working class status, urban-rural residence, registry, whether the tumor was screen detected, digital rectal exam (DRE) results, PSA value, Gleason score, and Charlson comorbidity score. Two-sided chi-square tests were used to compare the dis- tribution of treatments across the predictor variables. Variables found to be significantly associated in the bivar- iate analysis were included in the multivariable logistic regression models. For the multivariable analyses, marital status was dichotomized into married and not married (single, separated, divorced, and widowed). "Other" insurance was combined with private insurance. Separate logistic regression analyses were conducted to identify factors associated with receipt of conservative manage- ment and factors associated with receipt of radiation therapy among those receiving definitive treatment. A backward selection approach was utilized. That is, non- significant and non-relevant variables were deleted from the model one at a time. At each step, the change in parameter estimates for the remaining variables was examined to determine whether non-significant variables should be retained to control for confounding. The Hos- mer-Lemeshow test was used to assess the goodness of fit of the model. We examined all cause survival from diagnosis through December 31, 2002, conditional upon having survived six months following diagnosis (i.e., patients had to have sur- vived six months to be included in this study). The pro- portion of patients surviving five years following diagnosis was estimated using the life table method. Cox proportional hazards models were used to estimate crude and adjusted hazard ratios. A backward selection approach was utilized to determine which factors to include in the final model. Time-varying covariates were used to test the proportional hazards assumption, which was violated for comorbidity. Therefore, time-varying covariates for comorbidity were retained in the final model. SAS statistical software (version 9.1, SAS Insti- tute, Cary, NC) was used for all analyses. Results A total of 3,504 patients with localized primary prostate cancer were identified. Of these, 176 were excluded from analysis for the following reasons: diagnosis year was not 1997 (n = 3), tumor was not microscopically confirmed (n = 23), histology other than adenocarcinoma (n = 3), month of diagnosis was unknown (n = 51), information on treatment or treatment dates was missing (n = 64), or died within six months of diagnosis (n = 53). Some patients were excluded for multiple reasons. A total of