Combined survival analysis of cardiac patients by a Cox PH model and a Markov chain

The control and treatment of dyslipidemia is a major public health challenge, particularly for patients with coronary heart diseases. In this paper we propose a framework for survival analysis of patients who had a major cardiac event, focusing on assessment of the effect of changing LDL-cholesterol level and statins consumption on survival. This framework includes a Cox PH model and a Markov chain, and combines their results into reinforced conclusions regarding the factors that affect survival time. We prospectively studied 2,277 cardiac patients, and the results show high congruence between the Markov model and the PH model; both evidence that diabetes, history of stroke, peripheral vascular disease and smoking significantly increase hazard rate and reduce survival time. On the other hand, statin consumption is correlated with a lower hazard rate and longer survival time in both models. The role of such a framework in understanding the therapeutic behavior of patients and implementing effective secondary and primary prevention of heart diseases is discussed here.     

Semi-parametric hybrid empirical likelihood inference for two-sample comparison with censored data

Two-sample comparison problems are often encountered in practical projects and have widely been studied in literature. Owing to practical demands, the research for this topic under special settings such as a semiparametric framework have also attracted great attentions. Zhou and Liang (Biometrika 92:271–282, 2005) proposed an empirical likelihood-based semi-parametric inference for the comparison of treatment effects in a two-sample problem with censored data. However, their approach is actually a pseudo-empirical likelihood and the method may not be fully efficient. In this study, we develop a new empirical likelihood-based inference under more general framework by using the hazard formulation of censored data for two sample semi-parametric hybrid models. We demonstrate that our empirical likelihood statistic converges to a standard chi-squared distribution under the null hypothesis. We further illustrate the use of the proposed test by testing the ROC curve with censored data, among others. Numerical performance of the proposed method is also examined.     

Statistical approaches for conducting network meta-analysis in drug development

We introduce health technology assessment and evidence synthesis briefly, and then concentrate on the statistical approaches used for conducting network meta-analysis (NMA) in the development and approval of new health technologies. NMA is an extension of standard meta-analysis where indirect as well as direct information is combined and can be seen as similar to the analysis of incomplete-block designs. We illustrate it with an example involving three treatments, using fixed-effects and random-effects models, and using frequentist and Bayesian approaches. As most statisticians in the pharmaceutical industry are familiar with SAS? software for analyzing clinical trials, we provide example code for each of the methods we illustrate. One issue that has been overlooked in the literature is the choice of constraints applied to random effects, and we show how this affects the estimates and standard errors and propose a symmetric set of constraints that is equivalent to most current practice. Finally, we discuss the role of statisticians in planning and carrying out NMAs and the strategy for dealing with important issues such as heterogeneity.     

Replication of Low-Risk Gambling Limits Using Canadian Provincial Gambling Prevalence Data

A set of low-risk gambling limits were recently produced using Canadian epidemiological data on the intensity of gambling behavior and related consequences (Currie et al. Addiction 101:570–580, 2006). The empirically derived limits (gambling no more than two to three times per month, spending no more than $501–$100°CAN per year or no more than 1% of gross income spent on gambling) accurately predicted risk of gambling-related harm after controlling for other risk factors. The present study sought to replicate these limits on data collected in three independently conducted Canadian provincial gambling surveys. Dose–response curves and logistic regression analyses were applied to gambling prevalence data collected in surveys conducted in 2001–2002 within the provinces of Alberta, British Columbia, and Ontario (combined sample N = 7,675). A comparable dose–response relationship between gambling intensity and risk of harm was found in each province. The optimal thresholds for defining an upper limit of low-risk gambling were similar across the three provinces despite variations in the availability and organization of legalized gambling opportunities within each region. These results provide additional evidence supporting the validity of the low-risk gambling limits. Quantitative limits could be used to augment existing responsible gambling guidelines.

Technology and social interaction: the emergence of 'workplace studies'

Despite the substantial body of literature concerned with the ways in which digital media are transforming contemporary society and institutional life, we have relatively little understanding of the ways in which new technologies feature in day to day organizational conduct and interaction. There is however a growing corpus of empirical research which places the situated and contingent character of new technologies at the heart of the analytic agenda, but as yet, these studies are relatively little known within sociology. They include ethnographies of command and control centres, financial institutions, the news media, and the construction industry. They address the ways in which tools and technologies, ranging from paper documents through to complex multimedia systems, feature in work and collaboration. In this paper, we discuss these so-called 'workplace studies' and consider their implications for our understanding of organizational conduct, social interaction and new technology.     

Bayesian Experimental Design for Long-Term Longitudinal HIV Dynamic Studies

The study of HIV dynamics is one of the most important developments in recent AIDS research for understanding the pathogenesis of HIV-1 infection and antiviral treatment strategies. Currently a large number of AIDS clinical trials on HIV dynamics are in development worldwide. However, many design issues that arise from AIDS clinical trials have not been addressed. In this paper, we use a simulation-based approach to deal with design problems in Bayesian hierarchical nonlinear (mixed-effects) models. The underlying model characterizes the long-term viral dynamics with antiretroviral treatment where we directly incorporate drug susceptibility and exposure into a function of treatment efficacy. The Bayesian design method is investigated under the framework of hierarchical Bayesian (mixed-effects) models. We compare a finite number of feasible candidate designs numerically, which are currently used in AIDS clinical trials from different perspectives, and provide guidance on how a design might be chosen in practice.     

A quasi-Newton acceleration for high-dimensional optimization algorithms

In many statistical problems, maximum likelihood estimation by an EM or MM algorithm suffers from excruciatingly slow convergence. This tendency limits the application of these algorithms to modern high-dimensional problems in data mining, genomics, and imaging. Unfortunately, most existing acceleration techniques are ill-suited to complicated models involving large numbers of parameters. The squared iterative methods (SQUAREM) recently proposed by Varadhan and Roland constitute one notable exception. This paper presents a new quasi-Newton acceleration scheme that requires only modest increments in computation per iteration and overall storage and rivals or surpasses the performance of SQUAREM on several representative test problems.