Biostatistics and epidemiology

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

Biostatistical genetics and genetic epidemiology

It is well-known that when ranked set sampling (RSS) scheme is employed to estimate the mean of a population, it is more efficient than simple random sampling (SRS) with the same sample size. One can use a RSS analog of SRS regression estimator to estimate the population mean of Y using its concomitant variable X when they are linearly related. Unfortunately, the variance of this estimate cannot be evaluated unless the distribution of X is known. We investigate the use of resampling methods to establish confidence intervals for the regression estimation of the population mean. Simulation studies show that the proposed methods perform well in a variety of situations when the assumption of linearity holds, and decently well under mild non-linearity.     

Statistics in epidemiology: the case-control study

This article presents a general review of the major trends in the conceptualization, development, and success of case-control methods for the study of disease causation and prevention. "Recent work on nested case-control, case-cohort, and two-stage case control designs demonstrates the continuing impact of statistical thinking on epidemiology. The influence of R. A. Fisher's work on these developments is mentioned wherever possible. His objections to the drawing of causal conclusions from observational data on cigarette smoking and lung cancer are used to introduce the problems of measurement error and confounding bias."     

The proportional hazards model: applications in epidemiology

Cox's (1972) Proportioal hazards failure time model, already widely used in the analysis of clinical trials, also provides an elegant formalization of the epidemiologic concept of relative risk. When used to compare the disease experience of a study cohort with that of an external control population, it generalizes the notions of the standardized morbidity ratio (SMR) and the proportional morbidity ratio (PMR). For studies in which matched sets of cases and controls are sampled retrospectively from the population at risk, the model provides a flexible tool for the regression analysis of multiple risk factors.     

Real-time reliability evaluation of two-phase Wiener degradation process

Reliability modeling and evaluation for the two-phase Wiener degradation process are studied. For many devices, the degradation rates could possibly increase or decrease in a non smooth manner at some point in time due to the change of degradation mechanism. A two-phase Wiener degradation process with an unobserved change point is used to model the degradation process. And we assume that the change point varies randomly from device to device. Furthermore, we integrate historical data and up-to-date observation data to improve the degradation modeling and evaluation based on Bayesian method. The change point between the two phases was obtained based on the Akaike information criterion (AIC) and the criterion of the residual sum of squares. Finally, a real example of liquid coupling devices (LCDs) and a numeric example are discussed to demonstrate the effectiveness of the proposed method. The results show that the proposed method is effective and efficient.     

我国季度GDP实时数据预测与评价

传统实证研究中使用的当期特定数据存在滞后信息和噪音信息缺陷,导致模型估计结果存在偏误。应用宏观经济实时数据可以有效的剔除造成模型偏误的滞后信息和噪音信息,得到更为准确的估计结果。MIDAS模型可将低频的关键经济数据与高频数据同时估计,较好的解决了应用一般模型存在的高频数据信息损失问题。本文应用M-MIDAS-DL模型与季度GDP实时数据建立我国季度GDP预测模型,实证表明,应用实时数据与组合预测方法,能及时准确预测出2008年以来中国经济增长率的下滑与反弹走势,能起到较好的提前预警作用,是当前较为有效的经济预测手段之一。     

Dynamic generalized linear models with application to environmental epidemiology

Summary. We propose modelling short-term pollutant exposure effects on health by using dynamic generalized linear models. The time series of count data are modelled by a Poisson distribution having mean driven by a latent Markov process; estimation is performed by the extended Kalman filter and smoother. This modelling strategy allows us to take into account possible overdispersion and time-varying effects of the covariates. These ideas are illustrated by reanalysing data on the relationship between daily non-accidental deaths and air pollution in the city of Birmingham, Alabama.     

Empirical bayesInterval estimates: An application to geographical epidemiology

Summary Empirical Bayes estimates have been advocated as an improvement for mapping rare diseases or health events aggregated in small areas. In particular different parametric approaches have been proposed for dealing with non-normal data, assuming that disease occurrencies follow non-homogeneous Poisson law, whose parameters are treated as random variables. This paper shows how to conduct a complete Empirical Bayes analysis under an exchangeable model in the context of Geographical Epidemiology. Three different approaches for defining confidence limits obtained using a parametric bootstrap are compared: method 1 relies only on the first and second moment of the bootstrapped posterior distributions; method 2 computes the centiles of the bootstrapped posteriors; method 3 equates to α the average of the probabilities derived from the estimated bootstrapped cumulative posterior distributions. The simple Poisson-Gamma formulation was used to model mortality data on Larynx Cancer in the Local Health Units of Tuscany (1980–82 males). Two areas of significant elevated risk are identified.     

Some applications of lagrangian distributions in queueing theory and epidemiology

In this note we demonstrate that the Lagrangian distributions have applications in queueing theory and theory of epidemics. The Lagrangian distribution appears as the distribution describing the number of customers served in a busy period under certain conditions. Also, the Lagrangian distribution describes the distribution of number of persons infected by a certain infectious disease.     

Multiparameter evidence synthesis in epidemiology and medical decision-making: current approaches

Summary.  Alongside the development of meta-analysis as a tool for summarizing research literature, there is renewed interest in broader forms of quantitative synthesis that are aimed at combining evidence from different study designs or evidence on multiple parameters. These have been proposed under various headings: the confidence profile method, cross-design synthesis, hierarchical models and generalized evidence synthesis. Models that are used in health technology assessment are also referred to as representing a synthesis of evidence in a mathematical structure. Here we review alternative approaches to statistical evidence synthesis, and their implications for epidemiology and medical decision-making. The methods include hierarchical models, models informed by evidence on different functions of several parameters and models incorporating both of these features. The need to check for consistency of evidence when using these powerful methods is emphasized. We develop a rationale for evidence synthesis that is based on Bayesian decision modelling and expected value of information theory, which stresses not only the need for a lack of bias in estimates of treatment effects but also a lack of bias in assessments of uncertainty. The increasing reliance of governmental bodies like the UK National Institute for Clinical Excellence on complex evidence synthesis in decision modelling is discussed.     

Real-time monitoring of carbon monoxide using value-at-risk measure and control charting

One of the most important environmental health issues is air pollution, causing the deterioration of the population's quality of life, principally in cities where the urbanization level seems limitless. Among ambient pollutants, carbon monoxide (CO) is well known for its biological toxicity. Many studies report associations between exposure to CO and excess mortality. In this context, the present work provides an advanced modelling scheme for real-time monitoring of pollution data and especially of carbon monoxide pollution in city level. The real-time monitoring is based on an appropriately adjusted multivariate time series model that is used in finance and gives accurate one-step-ahead forecasts. On the output of the time series, we apply an empirical monitoring scheme that is used for the early detection of abnormal increases of CO levels. The proposed methodology is applied in the city of Athens and as the analysis revealed has a valuable performance.     

Understanding the epidemiology of foreign body injuries in children using a data-driven Bayesian network

Bayesian networks (BNs) are probabilistic expert systems which have emerged over the last few decades as a powerful data mining technique. Also, BNs have become especially popular in biomedical applications where they have been used for diagnosing diseases and studying complex cellular networks, among many other applications. In this study, we built a BN in a fully automated way in order to analyse data regarding injuries due to the inhalation, ingestion and aspiration of foreign bodies (FBs) in children. Then, a sensitivity analysis was carried out to characterize the uncertainty associated with the model. While other studies focused on characteristics such as shape, consistency and dimensions of the FBs which caused injuries, we propose an integrated environment which makes the relationships among the factors underlying the problem clear. The advantage of this approach is that it gives a picture of the influence of critical factors on the injury severity and allows for the comparison of the effect of different FB characteristics (volume, FB type, shape and consistency) and children's features (age and gender) on the risk of experiencing a hospitalization. The rates it consents to calculate provide a more rational basis for promoting care-givers’ education of the most influential risk factors regarding the adverse outcomes.     

Bayesian semiparametric modeling for stochastic precedence,with applications in epidemiology and survival analysis

We propose a prior probability model for two distributions that are ordered according to a stochastic precedence constraint, a weaker restriction than the more commonly utilized stochastic order constraint. The modeling approach is based on structured Dirichlet process mixtures of normal distributions. Full inference for functionals of the stochastic precedence constrained mixture distributions is obtained through a Markov chain Monte Carlo posterior simulation method. A motivating application involves study of the discriminatory ability of continuous diagnostic tests in epidemiologic research. Here, stochastic precedence provides a natural restriction for the distributions of test scores corresponding to the non-infected and infected groups. Inference under the model is illustrated with data from a diagnostic test for Johne’s disease in dairy cattle. We also apply the methodology to the comparison of survival distributions associated with two distinct conditions, and illustrate with analysis of data on survival time after bone marrow transplantation for treatment of leukemia.     

数据修订、实时估计与时变参数货币政策规则抉择

本文拓展构建了后顾、同期和前瞻三种类型的货币政策规则,并基于实时数据和最终数据实证分析数据修订和实时估计对货币政策参数的影响效应。研究结果发现,数据修订对泰勒规则的影响取决于不同模型,而且在三种模型设定中,盯住产出缺口和通胀目标的时变参数均在不同程度上受数据修订的影响。特别是,对于最终数据,采用同期性货币政策规则展开估计最为有效;而对于实时数据,则基于后顾性货币政策规则模型估计是最佳的。最后,本文在数据选择和模型匹配上提出相应的对策建议。     

A case-study in the clinical epidemiology of psoriatic arthritis: multistate models and causal arguments

In psoriatic arthritis, permanent joint damage characterizes disease progression and represents a major debilitating aspect of the disease. Understanding the process of joint damage will assist in the treatment and disease management of patients. Multistate models provide a means to examine patterns of disease, such as symmetric joint damage. Additionally, the link between damage and the dynamic course of disease activity (represented by joint swelling and stress pain) at both the individual joint level and otherwise can be represented within a correlated multistate model framework. Correlation is reflected through the use of random effects for progressive models and robust variance estimation for non-progressive models. Such analyses, undertaken with data from a large psoriatic arthritis cohort, are discussed and the extent to which they permit causal reasoning is considered. For this, emphasis is given to the use of the Bradford Hill criteria for causation in observational studies and the concept of local (in)dependence to capture the dynamic nature of the relationships.     

The zero-inflated Poisson model and the decayed, missing and filled teeth index in dental epidemiology

For frequency counts, the situation of extra zeros often arises in biomedical applications. This is demonstrated with count data from a dental epidemiological study in Belo Horizonte (the Belo Horizonte caries prevention study) which evaluated various programmes for reducing caries. Extra zeros, however, violate the variance–mean relationship of the Poisson error structure. This extra-Poisson variation can easily be explained by a special mixture model, the zero-inflated Poisson (ZIP) model. On the basis of the ZIP model, a graphical device is presented which not only summarizes the mixing distribution but also provides visual information about the overall mean. This device can be exploited to evaluate and compare various groups. Ways are discussed to include covariates and to develop an extension of the conventional Poisson regression. Finally, a method to evaluate intervention effects on the basis of the ZIP regression model is described and applied to the data of the Belo Horizonte caries prevention study.     

Generalized linear spatial models in epidemiology: A case study of zoonotic cutaneous leishmaniasis in Tunisia

Generalized linear spatial models (GLSM) are used here to study spatial characters of zoonotic cutaneous leishmaniasis (ZCL) in Tunisia. The response variable stands for the number of affected by district during the period 2001–2002. The model covariates are: climates (temperature and rainfall), humidity and surrounding vegetation status. As the environmental and weather data are not available for all the studied districts, Kriging based on linear interpolation was used to estimate the missing data. To account for unexplained spatial variation in the model, we include a stationary Gaussian process S with a powered exponential spatial correlation function. Moran coefficient, DIC criterion and residuals variograms are used to show the high goodness-of-fit of the GLSM. When compared with the statistical tools used in the previous ZCL studies, the optimal GLSM found here yields a better assessment of the impact of the risk factors, a better prediction of ZCL evolution and a better comprehension of the disease transmission. The statistical results show the progressive increase in the number of affected in zones with high temperature, low rainfall and high surrounding vegetation index. Relative humidity does not seem to affect the distribution of the disease in Tunisia. The results of the statistical analyses stress the important risk of misleading epidemiological conclusions when non-spatial models are used to analyse spatially structured data.     

Census data issues for epidemiology and health risk assessment: experiences from the Small Area Health Statistics Unit

Summary.  Census data are vital components of epidemiological studies, but the issues that are involved in using these data in such studies are often not fully appreciated. The paper describes some of the problems and uncertainties that arise, and some of the approaches that can be used to address them, based on experience in the Small Area Health Statistics Unit at Imperial College London. Issues considered include the geography of census data (zone design systems, recasting and the role of postcodes), temporal aspects of census data (especially in relation to migration and population change) and information content (especially in relation to characterization of socio-economic status). In the light of these issues, opportunities to improve the resolution and utility of census data for epidemiological studies are discussed.     

Parametric versus semi-parametric models for the analysis of correlated survival data: A case study in veterinary epidemiology

Correlated survival data arise frequently in biomedical and epidemiologic research, because each patient may experience multiple events or because there exists clustering of patients or subjects, such that failure times within the cluster are correlated. In this paper, we investigate the appropriateness of the semi-parametric Cox regression and of the generalized estimating equations as models for clustered failure time data that arise from an epidemiologic study in veterinary medicine. The semi-parametric approach is compared with a proposed fully parametric frailty model. The frailty component is assumed to follow a gamma distribution. Estimates of the fixed covariates effects were obtained by maximizing the likelihood function, while an estimate of the variance component ( frailty parameter) was obtained from a profile likelihood construction.