Risk models in genetic epidemiology

Advances in the identification and treatment of genetically transmitted diseases have lead to an increased need for reliable estimates of genetic susceptibility risk. These estimates are used in clinic settings to identify individuals at increased risk of being a carrier of a disease susceptibility allele as well as to define the probability of developing a particular disease given one is a carrier. Accurate assessment of these probabilities is extremely important given the implications for medical decision making including the identification of patients who might benefit from genetic counselling or from entry into clinical trials. A wide range of risk models has been proposed including those that utilize logistic regression, Cox proportional hazards regression, log-incidence models, and Bayesian modelling. The specific data used to create the various risk models varies by disease and may include molecular, epidemiologic, and clinical information although, in general, family history remains the primary variable of interest, particularly for those diseases for which a susceptibility allele(s) has yet to be identified. When permitted by sample size, researchers also attempt to measure the effect of any gene-environment interaction. In this paper we give an overview of the various definitions of risk as well as several of the more frequently used methods of risk estimation in genetic epidemiology at present. In addition, the means by which different methods are able to provide a measure of error or uncertainty associated with a given risk estimate will be discussed. Applications to risk modelling for breast cancer are given the disease for which risk assessment has probably been most extensively defined.     

Statistical methods in public health and epidemiology: a look at the recent past and projections for the next decade

This article attempts to prognosticate from past patterns, the type of statistical methods that will be used in published public health and epidemiological studies in the decade that follows the millennium. With this in mind, we conducted a study that would characterize trends in use of statistical methods in two major public health journals: the American Journal of Public Health, and the American Journal of Epidemiology. We took a probability sample of 348 articles published in these journals between 1970 and 1998. For each article sampled, we abstracted information on the design of the study and the types of statistical methods used in the article. Our major findings are that the proportion of articles using statistical methods as well as the mean number of statistical methods used per article has increased dramatically over the three decades surveyed. Also, the proportion of published articles using study designs that we classified as analytic has increased over the years. We also examined patterns of use in these journals of three statistical methodologies: logistic regression, proportional hazards regression, and methods for analysis of data from complex sample surveys. These methods were selected because they had been introduced initially in the late 1960s or early 1970s and had made considerable impact on data analysis in the biomedical sciences in the 1970s-90s. Estimated usage of each of these techniques remained relatively low until user-friendly software became available. Our overall conclusions are that new statistical methods are developed on the basis of need, disseminated to potential users over a course of many years, and often do not reach maximum use until tools for their comfortable use are made readily available to potential users. Based on these conclusions, we identify certain needs that are not now being met and which are likely to generate new statistical methodologies that we will see in the next decade.     

Statistical methods in model discrimination

Particularly in the chemical kinetic field but also in others in chemistry and engineering there has been much recent attention on the use of statistics in discriminating between rival models. There are two related basic problems. The first is to design experiments which will be most informative in determining which of several possible mathematical models is the “correct” one. Simple illustrations are given of the solution of this problem by different methods, such as the criteria of Roth, Box and Hill and of others, including some work done by R. S. Hawkins and the author on expected entropy change. The second problem is to analyze the data when received. A sampling of likelihood and Bayesian approaches to this problem is described with examples.     

Statistical approaches for farm and parasitic risk profiling in geographical veterinary epidemiology

We address the problem of farm and parasitic risk profiling in the context of Veterinary Epidemiology. We take advantage of a cross-sectional study carried out in the Campania Region in order to study the spatial distribution of 16 parasites in 121 ovine farms. We propose a tri-level hierarchical Bayesian model, which account for multivariate spatially structured overdispersion, to obtain estimate of posterior classification probabilities, that is for each parasite and farm the probability to belong to the set of the null hypothesis. We explore four decision rules based on either posterior probabilities or posterior means and compare the results in terms of the number of false discoveries/non-discoveries or the rate of false discovery/non-discovery. Our approach proved useful for parasitological risk profiling and we show that decision rules can be easily handled.     

An overview of robust methods in medical research

Robust statistics is an extension of classical parametric statistics that specifically takes into account the fact that the assumed parametric models used by the researchers are only approximate. In this article, we review and outline how robust inferential procedures may routinely be applied in practice in the biomedical research. Numerical illustrations are given for the t-test, regression models, logistic regression, survival analysis and ROC curves, showing that robust methods are often more appropriate than standard procedures.     

Statistical methods for comparing GPC curves

The possibilities of comparing the GPC curves of two or more polymers were studied, and the method of “distinguished points” (DPs) is suggested for this purpose. The features of these DP values as random variables were investigated. According to the experimental results they meet the requirements of the statistical tests applied in this text. In order to indicate the significant deviation or the agreement of the DP values of GPC curves of two polymers, the sequential U and t tests are suggested, because with these methods the number of the necessary parallel measurements is considerably decreased, and one can also decide on the magnitude of the variation on must be able to detect, while in the case of more than two polymers the methods of the analysis of variance can be utilized. The molecular weight ranges in which significant differences occur can also be determined. The described methods were tested by materials of known molecular weight distributions.     

Two-mode clustering methods: a structured overview

In this paper we present a structured overview of methods for two-mode clustering, that is, methods that provide a simultaneous clustering of the rows and columns of a rectangular data matrix. Key structuring principles include the nature of row, column and data clusters and the type of model structure or associated loss function. We illustrate with analyses of symptom data on archetypal psychiatric patients.     

Statistical control methods in glass batch preparation process

Statistic methods for quality control of raw materials and glass batches are considered. An algorithm for statistic control of the batch preparation process is proposed. __________ Translated from Steklo i Keramika, No. 5, pp. 3–5, May, 2006.     

常用生物素测定方法的简介

生物素作为B族类维生素,在机体代谢、医药、化学等工业已经广泛应用,生物素测定方法的研究也取得了迅速发展,总结了6种生物素的测定方法.随着检测技术的提高以及对检测结果精细度要求的提高,这些较为传统的方法也在适应现代检测技术的发展中有了新的提高与改进.对这几种方法的应用实例、适用范围、优缺点等做了简单介绍,提出了适用条件的选择,使得人们能够根据其实验要求,更好地选择理想的检测方法.对生物素检测新方法进行了展望.     

An overview of group sequential methods in longitudinal clinical trials

During the last decade, several papers have been published on group sequential methods in general and on sequential longitudinal clinical trials in particular. This paper gives an overview of the proposed methods, emphasizing longitudinal clinical trials. Furthermore, it tries to answer some practical questions that may arise during the conduct of interim analyses in longitudinal trials. Simulations have been carried out to obtain insight in these practical considerations.