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. ^

The normal range of the leukocyte count: Implications for surveillance of occupational and environmental exposure and the practice of preventive medicine

Leukopenia, the leukocyte count, and prognosis of disease are interrelated; a systematic search of the literature was undertaken to ascertain the strength of the evidence. One hundred seventy-one studies were found from 1953 onward pertaining to the predictive capabilities of the leukocyte count. Of those studies, 42 met inclusion criteria. An estimated range of 2,200cells/μL to 7,000cells/μL was determined as that which indicates good prognosis in disease and indicates the least amount of risk to an individual overall. Tables of the evidence are included indicating the disparate populations examined and the possible degree of association. ^

Joint effects of pulmonary function and aerobic power on survival in a cohort of healthy adults

Objective measurements of physical fitness and pulmonary function are related individually to long-term survival, both in healthy people and in those who are ill. These factors are furthermore known to be related to one another physiologically in people with pulmonary disease, because advanced pulmonary disease causes ventilatory limitation to exercise. Healthy people do not have ventilatory limitation to exercise, but rather have ventilatory reserve. The relationship between pulmonary function and exercise performance in healthy people is minimal. Exercise performance has been shown to modify the effect of pulmonary function on mortality in people with chronic obstructive pulmonary disease, but the relationship between these factors in healthy people has not been studied and is not known. The purpose of this study is to quantify the joint effects of pulmonary function and exercise performance as these bear on mortality in a cohort of healthy adults. This investigation is an historical cohort study over 20 years of follow-up of 29,624 adults who had complete preventive medicine, spirometry and treadmill stress examinations at the Cooper Clinic in Dallas, Texas.^ In 20 years of follow-up, there were 738 evaluable deaths. Forced expiratory volume in one second (FEV$\sb1$) percent of predicted, treadmill time in minutes percent of predicted, age, gender, body mass index, baseline smoking status, serum glucose and serum total cholesterol were all significant, independent predictors of mortality risk. There were no frank interactions, although age had an important increasing effect on the risk associated with smoking when other covariates were controlled for in a proportional-hazards model. There was no confounding effect of exercise performance on pulmonary function. In agreement with the pertinent literature on independent effects, each unit increase in FEV$\sb1$ percent predicted was associated with about eight tenths of a percent reduction in adjusted mortality rate. The concept of physiologic reserve is useful in interpretation of the findings. Since pulmonary function does not limit exercise tolerance in healthy adults, it is reasonable to expect that exercise tolerance would not modify the effect of pulmonary function on mortality. Epidemiologic techniques are useful for elucidating physiological correlates of mortality risk. ^

Mixture modeling for joint analysis of survival, discrete, and continuous data

Mixture modeling is commonly used to model categorical latent variables that represent subpopulations in which population membership is unknown but can be inferred from the data. In relatively recent years, the potential of finite mixture models has been applied in time-to-event data. However, the commonly used survival mixture model assumes that the effects of the covariates involved in failure times differ across latent classes, but the covariate distribution is homogeneous. The aim of this dissertation is to develop a method to examine time-to-event data in the presence of unobserved heterogeneity under a framework of mixture modeling. A joint model is developed to incorporate the latent survival trajectory along with the observed information for the joint analysis of a time-to-event variable, its discrete and continuous covariates, and a latent class variable. It is assumed that the effects of covariates on survival times and the distribution of covariates vary across different latent classes. The unobservable survival trajectories are identified through estimating the probability that a subject belongs to a particular class based on observed information. We applied this method to a Hodgkin lymphoma study with long-term follow-up and observed four distinct latent classes in terms of long-term survival and distributions of prognostic factors. Our results from simulation studies and from the Hodgkin lymphoma study demonstrated the superiority of our joint model compared with the conventional survival model. This flexible inference method provides more accurate estimation and accommodates unobservable heterogeneity among individuals while taking involved interactions between covariates into consideration.^

Interim analysis of clinical trials: Simulation studies of fractional Brownian motion

Interim clinical trial monitoring procedures were motivated by ethical and economic considerations. Classical Brownian motion (Bm) techniques for statistical monitoring of clinical trials were widely used. Conditional power argument and α-spending function based boundary crossing probabilities are popular statistical hypothesis testing procedures under the assumption of Brownian motion. However, it is not rare that the assumptions of Brownian motion are only partially met for trial data. Therefore, I used a more generalized form of stochastic process, called fractional Brownian motion (fBm), to model the test statistics. Fractional Brownian motion does not hold Markov property and future observations depend not only on the present observations but also on the past ones. In this dissertation, we simulated a wide range of fBm data, e.g., H = 0.5 (that is, classical Bm) vs. 0.5< H <1, with treatment effects vs. without treatment effects. Then the performance of conditional power and boundary-crossing based interim analyses were compared by assuming that the data follow Bm or fBm. Our simulation study suggested that the conditional power or boundaries under fBm assumptions are generally higher than those under Bm assumptions when H > 0.5 and also matches better with the empirical results. ^