Journal of Data Science

Approximately 15% of adults in the United States (U.S.) are afflicted with chronic kidney disease (CKD). For CKD patients, the progressive decline of kidney function is intricately related to hospitalizations due to cardiovascular disease and eventual “terminal” events, such as kidney failure and mortality. To unravel the mechanisms underlying the disease dynamics of these interdependent processes, including identifying influential risk factors, as well as tailoring decision-making to individual patient needs, we develop a novel Bayesian multivariate joint model for the intercorrelated outcomes of kidney function (as measured by longitudinal estimated glomerular filtration rate), recurrent cardiovascular events, and competing-risk terminal events of kidney failure and death. The proposed joint modeling approach not only facilitates the exploration of risk factors associated with each outcome, but also allows dynamic updates of cumulative incidence probabilities for each competing risk for future subjects based on their basic characteristics and a combined history of longitudinal measurements and recurrent events. We propose efficient and flexible estimation and prediction procedures within a Bayesian framework employing Markov Chain Monte Carlo methods. The predictive performance of our model is assessed through dynamic area under the receiver operating characteristic curves and the expected Brier score. We demonstrate the efficacy of the proposed methodology through extensive simulations. Proposed methodology is applied to data from the Chronic Renal Insufficiency Cohort study established by the National Institute of Diabetes and Digestive and Kidney Diseases to address the rising epidemic of CKD in the U.S.

Abstract: Simulation studies are important statistical tools used to inves-tigate the performance, properties and adequacy of statistical models. The simulation of right censored time-to-event data involves the generation of two independent survival distributions, where the rst distribution repre-sents the uncensored survival times and the second distribution represents the censoring mechanism. In this brief report we discuss how we can make it so that the percentage of censored data is previously de ned. The described method was used to generate data from a Weibull distribution, but it can be adapted to any other lifetime distribution. We further presented an R code function for generating random samples, considering the proposed approach.

Abstract: Cancer is a complex disease where various types of molecular aber rations drive the development and progression of malignancies. Among the diverse molecular aberrations, inherited and somatic mutations on DNA se quences are considered as major drivers for oncogenesis. The complexity of somatic alterations is revealed from large-scale investigations of cancer genomes and robust methods for interring the function of genes. In this review, we will describe sequence mutations of several cancer-related genes and discuss their functional implications in cancer. In addition, we will in troduce the on-line resources for accessing and analyzing sequence mutations in cancer. We will also provide an overview of the statistical and computa tional approaches and future prospects to conduct comprehensive analyses of the somatic alterations in cancer genomes.

Semi-parametric Cox regression and parametric methods have been used to analyze survival data of cancer; however, no study has focused on the comparison of survival models in genetic association analysis of age at onset (AAO) of cancer. The Hepatocyte nuclear factor-1- beta (HNF1B) gene has been associated with risk of endometrial and prostate cancers; however, no study has focused on the effect of HNF1B gene on the AAO of cancer. This study examined 23 single nucleotide polymorphisms (SNPs) within the HNF1B gene in the Marshfield sample with 716 cancer cases and 2,848 non-cancer controls. Cox proportional hazards models in PROC PHREG and parametric survival models (including exponential, Weibull, log-normal, log-logistic, and gamma models) in PROC LIFEREG in SAS 9.4 were used to detect the genetic association of HNF1B gene with the AAO. The Akaike information criterion (AIC) and Bayesian information criterion (BIC) were used to compare the Cox models and parametric survival models. Both AIC and BIC values showed that the Weibull distribution is the best model for all the 23 SNPs and the Gamma distribution is the second best. The top two SNPs are rs4239217 and rs7501939 with time ratio (TR) =1.08 (p<0.0001 for the AA and AG genotypes, respectively) and 1.07 (p=0.0004 and 0.0002 for CC and CT genotypes, respectively) based on the Weibull model, respectively. This study shows that the parametric Weibull distribution is the best model for the genetic association of AAO of cancer and provides the first evidence of several genetic variants within the HNF1B gene associated with AAO of cancer.

Survival analysis is the widely used statistical tool for new intervention comparison in presence of hazards of follow up studies. However, it is difficult to obtain suitable survival rate in presence of high level of hazard within few days of surgery. The group of patients can be directly stratified into cured and non-cured strata. The mixture models are natural choice for estimation of cure and non-cure rate estimation. The estimation of cure rate is an important parameter of success of any new intervention. The cure rate model is illustrated to compare the surgery of liver cirrhosis patients with consenting for participation HFLPC (Human Fatal Liver Progenitor Cells) Infusion vs. consenting for participation alone group in South Indian popula-tion. The surgery is best available technique for liver cirrhosis treatment. The success of the surgery is observed through follow up study. In this study, MELD (Model for End-Stage Liver Disease) score is considered as response of interest for cured and non-cured group. The primary efficacy of surgery is considered as covariates of interest. Distributional assumptions of the cure rate are solved with Markov Chain Monte Carlo (MCMC) techniques. It is found that cured model with parametric approach allows more consistent estimates in comparison to standard procedures. The risk of death due to liver transplantation in liver cirrhosis patients including time dependent effect terms has also been explored. The approach assists to model with different age and sex in both the treatment groups.