Redaniel et al. BMC Cancer 2010, 10:100 http://www.biomedcentral.com/1471-2407/10/100 datasets for the remaining 1,635 colorectal cancer patients (81.8%) were prepared and used in the analysis. Of these, 83.6% have complete follow-up information. Of the patients who are alive, 68.6% have complete fol- low-up information. For survival analyses, patients with incomplete follow-up information were censored at the last date known alive. From the patients who have complete follow-up infor- mation, 53.9% were identified to be deceased, of whom 98.2% have cancer as the cause of death. For 82.3% of patients who are dead, information on the cause of death was obtained from medical records or death certi- ficates, while data for another 12.6% were acquired through interviews of family members. Survival informa- tion for only a few (5.1%) came from other sources. The project proposal was approved by the Ethics Review Board of the National Institutes of Health of the University of the Philippines Manila. Data analysis Estimation of Survival using Period Analysis Conventionally, cohort-based analyses, using the life- table (actuarial) method or the Kaplan-Meier method [21,22], have been used to derive cancer survival esti- mates. In this study, however, we employed period ana- lysis, introduced by Brenner and Gefeller in 1996, to derive more up-to-date survival estimates [23]. With this approach, only the survival experience of patients during the most recent calendar period for which data are available are included. Various empirical studies have shown that period analysis provides more up-to- date estimates of survival that closely predict survival later observed for patients diagnosed in the respective period [24-28]. Estimation of Relative Survival As commonly practiced in population-based survival analysis, estimates of relative survival (calculated as the ratio of observed and expected survival) are reported, in addition to estimates of absolute survival. Expected sur- vival for the general population of analogous age distri- bution was derived using the so-called Ederer II method [29] and life tables for the year 2000. Due to the absence of life tables for other races, the life table for whites from the US National Center for Health Statistics [30] was used for both SEER populations. The life table for the Philippine resident population was derived from the projected population estimate and the actual mortality data for this area, which were obtained from the Philip- pine National Statistics Office. To compare overall CRC survival estimates between the different cancer popula- tions, age adjustment was done using weights from the World Standard Cancer Patient Population (WSCPP) [12]. Age groups used were less than 50, 50-59, 60-69, 70-79 and 80 and above. Page 3 of 9 Tests for survival differences between cancer populations To test for differences in survival between the three cancer populations, a novel modelling approach for period analysis [31] was used. First, age-specific num- bers of patients at risk and of deaths by year of follow- up were calculated separately for each population group. Then, Poisson regression models were fitted, in which the numbers of deaths were modelled as a func- tion of the population group (Philippine residents or Filipino-Americans or Caucasians), year of follow-up (1, 2, 3, 4, 5 - entered as a categorical variable) and age-group (15-49, 50-59, 60-69, 70-79 and 80+ - entered as a categorical variable), using the logarithm of the person-years at risk as offset, and accounting for late entries and withdrawals as half persons, as described in detail elsewhere [31]. This approach allowed testing for significance of differences in survi- val, after adjustment for age, based on p-values for the population parameter estimate. A significance level of alpha = 0.05 (two-sided testing) was used. Multivariate analysis To explain possible survival differences and to deter- mine factors affecting survival, both within and between the three cancer patient populations, the Cox Propor- tional Hazards model was used. Initially, separate Cox models by population group were used to determine bivariate associations of age, sex, stage, histology, sur- gery and radiotherapy with survival. A multivariate model was then built jointly for all three groups to com- pare survival probabilities between populations. Relative risks were calculated using Filipino-Americans as the reference group, while controlling for the effects of age, sex, stage, histology, surgery and radiotherapy, first indi- vidually and then simultaneously. Those with missing information were excluded in the multivariate analysis. For each variable included in the Cox models, the pro- portional hazards assumption was checked by using log (-log) graphs. The plotted lines were roughly parallel over time and no violations of the proportional hazards assumption were found. Age at diagnosis was categorized into the 5 age adjust- ment groupings mentioned earlier. Stage categories were localized, regional and distant, while histology was clas- sified based on the WHO Classification of Tumours [32] (adenocarcinoma and other types). Binary variables (with/without) were used for the receipt of surgical treatment and of radiotherapy. Chemotherapy and hor- mone therapy were not included in the Cox models as these were not available from the SEER public use data- base. Nevertheless, a sub-analysis for the receipt of che- motherapy was done for the Philippine residents. To account for missing information in the databases, multiple imputation [33] using a Markov Chain Monte Carlo method was done. Parameter estimates were