Bayesian joint modeling of longitudinal and survival data

The joint modeling of longitudinal and survival data is a new approach to many applications such as HIV, cancer vaccine trials and quality of life studies. There are recent developments of the methodologies with respect to each of the components of the joint model as well as statistical processes that link them together. Among these, second order polynomial random effect models and linear mixed effects models are the most commonly used for the longitudinal trajectory function. In this study, we first relax the parametric constraints for polynomial random effect models by using Dirichlet process priors, then three longitudinal markers rather than only one marker are considered in one joint model. Second, we use a linear mixed effect model for the longitudinal process in a joint model analyzing the three markers. In this research these methods were applied to the Primary Biliary Cirrhosis sequential data, which were collected from a clinical trial of primary biliary cirrhosis (PBC) of the liver. This trial was conducted between 1974 and 1984 at the Mayo Clinic. The effects of three longitudinal markers (1) Total Serum Bilirubin, (2) Serum Albumin and (3) Serum Glutamic-Oxaloacetic transaminase (SGOT) on patients' survival were investigated. Proportion of treatment effect will also be studied using the proposed joint modeling approaches. ^ Based on the results, we conclude that the proposed modeling approaches yield better fit to the data and give less biased parameter estimates for these trajectory functions than previous methods. Model fit is also improved after considering three longitudinal markers instead of one marker only. The results from analysis of proportion of treatment effects from these joint models indicate same conclusion as that from the final model of Fleming and Harrington (1991), which is Bilirubin and Albumin together has stronger impact in predicting patients' survival and as a surrogate endpoints for treatment. ^

Association of periodontal disease with sex hormones levels among men; an analysis of NHANES III data

Background. The purpose of this study was to investigate the association of periodontal disease with sex hormones. If periodontal disease is associated with abnormal levels of sex hormones this may indicate a link between periodontal disease and prostate cancer. ^ Methods. All participants were derived from the third National Health and Nutrition Examination survey (NHANES III) data. For the purpose of our study, serum samples for hormones measurements such as testosterone, free testosterone, estradiol, free estradiol and sex hormone binding globulin (SHBG) and periodontal examination data were available for 1,101 of these men. ^ Results. After adjusting for known risk factors, periodontal disease was significantly associated with sex hormones as testosterone, free testosterone, estradiol and free estradiol. The association of periodontal disease and sex hormone levels were not significantly different between ethnicity groups. ^ Discussion. The results indicate the need for further study of periodontal disease and serum levels of testosterone, free testosterone, estradiol and free estradiol in men.^