Application Of Medical Statistics Research Articles

Seemingly Unrelated Multi-State Processes: A Bayesian Semiparametric Approach

Many applications in medical statistics and other fields can be described by transitions between multiple states (e.g. from health to disease) experienced by individuals over time. In this context, multi-state models are a popular statistical technique, in particular when the exact transition times are not observed. The key quantities of interest are the transition rates, capturing the instantaneous risk of moving from one state to another. The main contribution of this work is to propose a joint semiparametric model for several possibly related multi-state processes (Seemingly Unrelated Multi-State, SUMS, processes), assuming a Markov structure for the transitions over time. The dependence between different processes is captured by specifying a joint prior distribution on the transition rates of each process. In this case, we assume a flexible distribution, which allows for clustering of the individuals, overdispersion and outliers. Moreover, we employ a graph structure to describe the dependence among processes, exploiting tools from the Gaussian Graphical model literature. It is also possible to include covariate effects. We use our approach to model disease progression in mental health. Posterior inference is performed through a specially devised MCMC algorithm.

Cognition of and Demand for Education and Teaching in Medical Statistics in China: A Systematic Review and Meta-Analysis.

BackgroundAlthough a substantial number of studies focus on the teaching and application of medical statistics in China, few studies comprehensively evaluate the recognition of and demand for medical statistics. In addition, the results of these various studies differ and are insufficiently comprehensive and systematic.ObjectivesThis investigation aimed to evaluate the general cognition of and demand for medical statistics by undergraduates, graduates, and medical staff in China.MethodsWe performed a comprehensive database search related to the cognition of and demand for medical statistics from January 2007 to July 2014 and conducted a meta-analysis of non-controlled studies with sub-group analysis for undergraduates, graduates, and medical staff.ResultsThere are substantial differences with respect to the cognition of theory in medical statistics among undergraduates (73.5%), graduates (60.7%), and medical staff (39.6%). The demand for theory in medical statistics is high among graduates (94.6%), undergraduates (86.1%), and medical staff (88.3%). Regarding specific statistical methods, the cognition of basic statistical methods is higher than of advanced statistical methods. The demand for certain advanced statistical methods, including (but not limited to) multiple analysis of variance (ANOVA), multiple linear regression, and logistic regression, is higher than that for basic statistical methods. The use rates of the Statistical Package for the Social Sciences (SPSS) software and statistical analysis software (SAS) are only 55% and 15%, respectively.ConclusionThe overall statistical competence of undergraduates, graduates, and medical staff is insufficient, and their ability to practically apply their statistical knowledge is limited, which constitutes an unsatisfactory state of affairs for medical statistics education. Because the demand for skills in this area is increasing, the need to reform medical statistics education in China has become urgent.

Attitudes of Medical Graduate and Undergraduate Students toward the Learning and Application of Medical Statistics

It is clear that the teaching of medical statistics needs to be improved, yet areas for priority are unclear as medical students’ learning and application of statistics at different levels is not well known. Our goal is to assess the attitudes of medical students toward the learning and application of medical statistics, and discover their learning needs. We conducted a cross-sectional survey of learning and application of medical statistics among graduates and undergraduates at a medical school in Chongqing. More than 70% of the medical students thought that the medical statistics course was as important as other medical courses. Among them, 62.58% of graduate students thought that it was very hard to learn medical statistics, whereas only 27.72% undergraduate students held the same opinion. Both graduates and undergraduates stated that the main difficulties involved in learning medical statistics were its abstract principles and the large quantity of concepts and formulas. In all, 70% of both graduates and undergraduates could conduct a basic statistical analysis of general medical data independently, although undergraduates demonstrated greater proficiency in applying basic statistical methods than did graduates (p<0.05). The most common mistake students made in solving statistical problems was the misuse of statistical methods. In conclusion, medical students do not perform well in learning and applying medical statistical methods despite a clear recognition of the importance of the course, and we should start to make the necessary reforms in the teaching of medical statistics.

Relation between kidney function, proteinuria, and adverse outcomes--a critical look at the application of medical statistics in the Nephrology literature

In a recent publication in the Journal of the American Medical Association , Hemmelgarn et al. 1 studied an impressively large cohort, over a million participants, and concluded that the risk of mortality, myocardial infarction and progression to kidney failure associated with a given level of estimated glomerular filtration rate (eGFR) are independently increased in patients with higher levels of proteinuria. Their use of extensive and elaborate statistical analyses is highly commendable. Limitations noted include its observational nature, the cross-sectional nature of the data and the limited number of individual participant data as compared to the group-level patient data.1 While agreeing with the general trend of the article, other limitations must be acknowledged. First and foremost, …

Flexible random‐effects models using Bayesian semi‐parametric models: applications to institutional comparisons

Random effects models are used in many applications in medical statistics, including meta-analysis, cluster randomized trials and comparisons of health care providers. This paper provides a tutorial on the practical implementation of a flexible random effects model based on methodology developed in Bayesian non-parametrics literature, and implemented in freely available software. The approach is applied to the problem of hospital comparisons using routine performance data, and among other benefits provides a diagnostic to detect clusters of providers with unusual results, thus avoiding problems caused by masking in traditional parametric approaches. By providing code for Winbugs we hope that the model can be used by applied statisticians working in a wide variety of applications.

Sparse Bayesian Kernel Survival Analysis for Modeling the Growth Domain of Microbial Pathogens

Survival analysis is a branch of statistics concerned with the time elapsing before "failure," with diverse applications in medical statistics and the analysis of the reliability of electrical or mechanical components. We introduce a parametric accelerated life survival analysis model based on kernel learning methods that, at least in principal, is able to learn arbitrary dependencies between a vector of explanatory variables and the scale of the distribution of survival times. The proposed kernel survival analysis method is then used to model the growth domain of Clostridium botulinum, the food processing and storage conditions permitting the growth of this foodborne microbial pathogen, leading to the production of the neurotoxin responsible for botulism. A Bayesian training procedure, based on the evidence framework, is used for model selection and to provide a credible interval on model predictions. The kernel survival analysis models are found to be more accurate than models based on more traditional survival analysis techniques but also suggest a risk assessment of the foodborne botulism hazard would benefit from the collection of additional data.