A Bayesian model for estimating the malaria transition probabilities considering individuals lost to follow-up

It is known that patients may cease participating in a longitudinal study and become lost to follow-up. The objective of this article is to present a Bayesian model to estimate the malaria transition probabilities considering individuals lost to follow-up. We consider a homogeneous population, and it is assumed that the considered period of time is small enough to avoid two or more transitions from one state of health to another. The proposed model is based on a Gibbs sampling algorithm that uses information of lost to follow-up at the end of the longitudinal study. To simulate the unknown number of individuals with positive and negative states of malaria at the end of the study and lost to follow-up, two latent variables were introduced in the model. We used a real data set and a simulated data to illustrate the application of the methodology. The proposed model showed a good fit to these data sets, and the algorithm did not show problems of convergence or lack of identifiability. We conclude that the proposed model is a good alternative to estimate probabilities of transitions from one state of health to the other in studies with low adherence to follow-up.     

Buehler confidence limits and nesting

The Buehler 1 –α upper confidence limit is as small as possible, subject to the constraints that its coverage probability is at least 1 –α and that it is a non‐decreasing function of a pre‐specified statistic T. This confidence limit has important biostatistical and reliability applications. Previous research has examined the way the choice of T affects the efficiency of the Buehler 1 –α upper confidence limit for a given value of α. This paper considers how T should be chosen when the Buehler limit is to be computed for a range of values of α. If T is allowed to depend on α then the Buehler limit is not necessarily a non‐increasing function of α, i.e. the limit is ‘non‐nesting’. Furthermore, non‐nesting occurs in standard and practical examples. Therefore, if the limit is to be computed for a range [α_L, α_U]of values of α, this paper suggests that T should be a carefully chosen approximate 1 –α_L upper limit for θ. The choice leads to Buehler limits that have high statistical efficiency and are nesting.     

Computation of exact confidence intervals from discrete data using studentized test statistics

A new area of research interest is the computation of exact confidence limits or intervals for a scalar parameter of interest from discrete data by inverting a hypothesis test based on a studentized test statistic. See, for example, Chan and Zhang (1999), Agresti and Min (2001) and Agresti (2003) who deal with a difference of binomial probabilities and Agresti and Min (2002) who deal with an odds ratio. However, neither (1) a detailed analysis of the computational issues involved nor (2) a reliable method of computation that deals effectively with these issues is currently available. In this paper we solve these two problems for a very broad class of discrete data models. We suppose that the distribution of the data is determined by (,) where is a nuisance parameter vector. We also consider six different studentized test statistics. Our contributions to (1) are as follows. We show that the P-value resulting from the hypothesis test, considered as a function of the null-hypothesized value of , has both jump and drop discontinuities. Numerical examples are used to demonstrate that these discontinuities lead to the failure of simple-minded approaches to the computation of the confidence limit or interval. We also provide a new method for efficiently computing the set of all possible locations of these discontinuities. Our contribution to (2) is to provide a new and reliable method of computing the confidence limit or interval, based on the knowledge of this set.     

Strategies for Success: Early-Stage Collaborating Biostatistics Faculty in an Academic Health Center

Collaborative biostatistics faculty (CBF) are increasingly valued by academic health centers (AHCs) for their role in increasing success rates of grants and publications, and educating medical students and clinical researchers. Some AHCs have a biostatistics department that consists of only biostatisticians focused on methodological research, collaborative research, and education. Others may have a biostatistics unit within an interdisciplinary department, or statisticians recruited into clinical departments. Within each model, there is also variability in environment, influenced by the chair's background, research focus of colleagues, type of students taught, funding sources, and whether the department is in a medical school or school of public health. CBF appointments may be tenure track or nontenure, and expectations for promotion may vary greatly depending on the type of department, track, and the AHC. In this article, the authors identify strategies for developing early-stage CBFs in four domains: (1) Influence of department/environment, (2) Skills to develop, (3) Ways to increase productivity, and (4) Ways to document accomplishments. Graduating students and postdoctoral fellows should consider the first domain when choosing a faculty position. Early-stage CBFs will benefit by understanding the requirements of their environment early in their appointment and by modifying the provided progression grid with their chair and mentoring team as needed. Following this personalized grid will increase the chances of a satisfying career with appropriate recognition for academic accomplishments.     

Biostatistics and epidemiology

The fields of biostatistics and epidemiology are described. Their relationship to each other and recent advances are emphasized.     

美国统计高等教育的现状及其发展趋势

统计学专业已经成为当今美国最热门的专业之一。美国统计学会的最新报告详细介绍了统计学和生物统计学专业的科研经费、学生就业及薪金水平等情况。美国国家卫生研究院于2001年和2003年先后两次召开专题会议,研讨生物统计学专业人才的培养、美国学界呼吁：加强下一代统计和生物统计专业人才的培养,特别要重视多重背景高层次统计学专业人才的培养。     

《生物统计学》教学改革与实践

《生物统计学》是一门农业院校种、养殖专业学生必学的专业基础课,作者通过多年的教学实践,提出了在生物统计学的教学中突出应用性的观点,并对课程教学内容的选择、教学方法的使用等提出了自己的见解。     

A survival analysis of the last great European plagues: The case of Nonantola (Northern Italy) in 1630

This paper develops the first survival analysis of a large-scale mortality crisis caused by plague. For the time-to-event analyses we used the Cox proportional hazards regression model. Our case study is the town of Nonantola during the 1630 plague, which was probably the worst to affect Italy since the Black Death. Individual risk of death did not depend on sex, grew with age (peaking at ages 40–60 and then declining), was not affected by socio-economic status, and was positively associated with household size. We discuss these findings in light of the historical–demographic and palaeo-demographic literature on medieval and early modern plagues. Our results are compatible with the debated idea that ancient plague was able to spread directly from human to human. Our methods could be replicated in other studies of European plagues to nuance and integrate the findings of recent palaeo-biological and palaeo-demographic research on plague.     

Biostatistics:a difficult road ahead

We are now well into the nineteen-eighties and looking back over the last ten years one of the most striking, perhaps the most striking, aspect of biostatistics and statistical epidemiology is sheer volume of literature that has emerged. Any historian of our subject is bound to ask two major and not unrelated questions! how much of this was necessary and how much of it was of direct use to clinical science in the sense of giving real aid to progress in medicine?.     

医学院校生物统计学专业生物信息学教学探索

生物信息学是一门新兴的前沿交叉学科.多种专业选择其作为必修课或选修课.由于专业性质及对学生培养目标的不同,生物信息学课程在知识体系和教学方法等方面都有所区别.文章就医学院校生物统计学专业如何优化生物信息学教学内容及方法等进行探索,并对如何提高教师的专业水平进行讨论.