The purpose of this study is reanalyzing the Childhood Cancer Survivors Study (CCSS) and the Cancer and Leukemia Group B (CALGB) study 9082 data using an innovative Bayesian method for quantile regression (QR) and censored QR model. The CCSS and the CALGB study 9082 are concerned with two important questions of cancer epidemiology, respectively, identifying and assessing risk factors for modifiable disease and weighing more aggressive cancer treatment against less aggressive one. The primary analyses of the studies were constrained by the study design or the choice of statistical analysis method and fell short of adequately answering their respective research questions. Reanalyzing these studies is well warranted by appropriately addressing such constraints. We will use an innovative Bayesian (censored) QR method. It replaces the likelihood with empirical likelihood within the Bayesian analytic framework and conducts (censored) QR analysis. It will be a method, the first and only regarding the censored QR and one among a few regarding the uncensored QR, that accommodate all the features that are essential to adequately reanalyze the CCSS and the CALGB study 9082 data. It will also provide an alternative analytic tool for many other cancer epidemiology studies where standard regression or survival data analysis methods fail or are deemed insufficient. This study will improve our understanding of, respectively, obesity related future health risks in the childhood acute lymphoblastic leukemia (ALL) survivors and the role of cranial radiotherapy, and of the relative benefit of the high dose chemotherapy (with autologous bone marrow transplantation) in women with high risk breast cancer. This knowledge will consequently lead to better designed treatment protocols and intervention strategies that will increase survival and minimize harmful health effects in pediatric cancer and high risk breast cancer patient populations.
The purpose of this study is reanalyzing the Childhood Cancer Survivors Study (CCSS) and the Cancer and Leukemia Group B (CALGB) study 9082 by using an innovative Bayesian method for quantile regression and censored quantile regression model. The proposed analyses will improve our understanding of, respectively, obesity related future health risks in the childhood acute lymphoblastic leukemia (ALL) survivors and the role of cranial radiotherapy, and of the relative benefit of high dose chemotherapy with autologous bone marrow transplantation in women with high risk breast cancer.
