病例对照研究中相对危险度的估计方法及其应用

本文介绍了在没有发病率很低之假设的条件下,病例对照研究中相对危险度的估计方法,包括已知总体中某些参数条件下的估计以及利用 Bayes 定理和多变量模型推算暴露别发病率条件下的估计。利用两个例子说明了这些方法的应用。     

Bayes线性混合效应模型多中心临床试验应用

目的 为解决多中心间的异质性问题。方法 系统介绍Bayes线性混合效应模型原理与方法。结果 通过WinBUGS软件和SAS软件实例分析，认为二者固定效应结果相同，WinBUGS软件Bayes分析混合效应结果可提供更多效应变异的信息。结论 Bayes线性混合效应模型分析结果解释更合理，推论范围更广。     

Bayes判别分析在尘肺诊断中的应用

判别分析在医学研究中应用广泛^[1]，就准则而言，大致上分为两大类，一类是以距离为判别准则（Fisher判别分析）。另一类是以概率为判别准则（Bayes判别分析），其中Bayes判别分析的判别效能优于其他方法。本文用黄石市2005年尘肺病人的检验指标与诊断结论进行Bayes判别分析应用研究，现将结果报告如下。     

应用Bayes判别分析进行输卵管妊娠的早期诊断

目的应用妊娠患者的临床、生理、生化、形态学指标对输卵管妊娠进行早期鉴别诊断。方法通过Bayes逐步判别分析的方法筛选有意义的指标,建立输卵管妊娠的判别诊断模型与计量诊断表。结果年龄、体重、超声诊断、包块等指标对输卵管妊娠进行早期鉴别诊断有统计学意义,判别诊断模型交互验证考核正确率达91.9%。结论用Bayes判别分析方法筛选妊娠患者的早期征象和指标并建立模型,对输卵管妊娠早期诊断具有积极意义。     

泊松分布参数的最短置信区间

目的根据抽样结果估计泊松分布参数λ的最短置信区间。方法证明了参数λ最短置信区间的存在和唯一性,利用搜索法用Matlab编程求得最短置信区间,将最短置信区间的长度与用一般的方法求得的置信区间的长度进行了比较。结果对置信度0.90、0.95、0.99和样本总计数1～50给出了最短置信区间表,可作为统计用表使用。结论最短置信区间估计精度比用一般的方法的精度有显著提高,用最短置信区间作为参数λ的区间估计更精确。     

卫生统计

021043类别归因比例的Bayes估计与极大似然估计比较/余小金…//中国卫生统计.一2000,17(6).一322～324 比较类别归因比例的Bayes图模型估计与极大似然估计方法。应用Gibbs抽样和迭代蒙特卡罗方法得到参数后验分布,得到类别归因比例的模型法估计,应用婴儿低出生体重资料分析孕妇     

流行病学研究中低危险度的评价方法

在流行病学研究中,常用测量危险因素和疾病效应相关程度的指标是危险度和比值比。危险度主要分为相对危险度(RR),归因危险度(AR)和人群归因危险度(PAR)。在病例对照研究中用比值比(OR)对相对危险度进行估计。比值比与相对危险度之间的差异取决于患病率和比值比大小。对于高危险     

多因素人群归因危险度估计在胃癌病因研究中的应用

多因素人群归因危险度估计在胃癌病因研究中的应用缪永东（庐江县卫生防疫站２３１５００）人群归因危险度（ｐｏｐｕｌａｔｉｏｎａｔｒｉｂｕｔａｂｌｅｒｉａｋＰＡＲ）是定量描述危险因素对人群致病大小的统计指标，对于在慢性病防治研究中评价因素在人群中危害程度和...     

列线图法预测人群归因分值

人群归因分值（人群归因危险度百分比，PAF）是广大流行病学工作者熟悉的公共卫生学指标。PAF的计算主要根据某个危险因素对某病的相对危险度（RR）和人群中该危险因素的暴露比例（R）。文中介绍由RR和R估计PAF列线图的制作方法，以便快速简捷地估算PAF。     

常见结局事件的前瞻性研究中修正Poisson回归模型的应用

目的 介绍应用修正poisson回归模型计算常见结局事件的前瞻性研究中暴露因素的调整相对危险度的精确区间估计值.方法 应用稳健误差方差估计法（sandwich variance estimator）来校正相对危险度（RR）的估计方差,并通过SAS程序中GENMOD过程的REPEATED语句实现修正poisson回归.此外,采用不同的统计方法对5个虚拟的研究数据进行了分析比较.结果 以分层的Mantel-Haenszel法为标准参照,修正poisson回归对aRR点和区间估计均较为理想,普通poisson回归的aRR区间估计偏于保守.而logistic回归得到的aOR值明显偏离真实的RR值.结论 修正poisson回归模型适合于处理常见结局事件的前瞻性研究资料.     

Bayesian multivariate disease mapping and ecological regression with errors in covariates: Bayesian estimation of DALYs and ‘preventable’ DALYs

This paper presents Bayesian multivariate disease mapping and ecological regression models that take into account errors in covariates. Bayesian hierarchical formulations of multivariate disease models and covariate measurement models, with related methods of estimation and inference, are developed as an integral part of a Bayesian disability adjusted life years (DALYs) methodology for the analysis of multivariate disease or injury data and associated ecological risk factors and for small area DALYs estimation, inference, and mapping. The methodology facilitates the estimation of multivariate small area disease and injury rates and associated risk effects, evaluation of DALYs and ‘preventable’ DALYs, and identification of regions to which disease or injury prevention resources may be directed to reduce DALYs. The methodology interfaces and intersects the Bayesian disease mapping methodology and the global burden of disease framework such that the impact of disease, injury, and risk factors on population health may be evaluated to inform community health, health needs, and priority considerations for disease and injury prevention. A burden of injury study on road traffic accidents in local health areas in British Columbia, Canada, is presented as an illustrative example. Copyright © 2009 John Wiley & Sons, Ltd.     

Mapping disability-adjusted life years: a Bayesian hierarchical model framework for burden of disease and injury assessment

This paper presents a Bayesian disability-adjusted life year (DALY) methodology for spatial and spatiotemporal analyses of disease and/or injury burden. A Bayesian disease mapping model framework, which blends together spatial modelling, shared-component modelling (SCM), temporal modelling, ecological modelling, and non-linear modelling, is developed for small-area DALY estimation and inference. In particular, we develop a model framework that enables SCM as well as multivariate CAR modelling of non-fatal and fatal disease or injury rates and facilitates spline smoothing for non-linear modelling of temporal rate and risk trends. Using British Columbia (Canada) hospital admission-separation data and vital statistics mortality data on non-fatal and fatal road traffic injuries to male population age 20-39 for year 1991-2000 and for 84 local health areas and 16 health service delivery areas, spatial and spatiotemporal estimation and inference on years of life lost due to premature death, years lived with disability, and DALYs are presented. Fully Bayesian estimation and inference, with Markov chain Monte Carlo implementation, are illustrated. We present a methodological framework within which the DALY and the Bayesian disease mapping methodologies interface and intersect. Its development brings the relative importance of premature mortality and disability into the assessment of community health and health needs in order to provide reliable information and evidence for community-based public health surveillance and evaluation, disease and injury prevention, and resource provision.     

Large sample Bayesian inference on the parameters of the proportional hazard models

This paper considers large sample Bayesian analysis of the proportional hazards model when interest is in inference on the parameters and estimation of the log relative risk for specified covariate vectors rather than on prediction of the survival function. We use a normal prior distribution for the parameters and make inferences based on the derived posterior distribution. The suggested approach is much simpler than alternative Bayesian analyses previously suggested for the proportional hazards models. Using simulated data we compare estimates obtained from the Bayesian analysis with those obtained from the full proportional hazards model and the reduced model after backwards elimination. We show that under a wider range of assumptions, the Bayesian analysis provides reduced estimation errors and improved rejection of noise variables. Finally, we illustrate the methodology using data from a large study of prognostic markers in breast cancer. © 1997 by John Wiley & Sons, Ltd.     

A practical introduction to Bayesian estimation of causal effects: Parametric and nonparametric approaches

Substantial advances in Bayesian methods for causal inference have been made in recent years. We provide an introduction to Bayesian inference for causal effects for practicing statisticians who have some familiarity with Bayesian models and would like an overview of what it can add to causal estimation in practical settings. In the paper, we demonstrate how priors can induce shrinkage and sparsity in parametric models and be used to perform probabilistic sensitivity analyses around causal assumptions. We provide an overview of nonparametric Bayesian estimation and survey their applications in the causal inference literature. Inference in the point‐treatment and time‐varying treatment settings are considered. For the latter, we explore both static and dynamic treatment regimes. Throughout, we illustrate implementation using off‐the‐shelf open source software. We hope to leave the reader with implementation‐level knowledge of Bayesian causal inference using both parametric and nonparametric models. All synthetic examples and code used in the paper are publicly available on a companion GitHub repository.     

Latent transition analysis: inference and estimation

Parameters for latent transition analysis (LTA) are easily estimated by maximum likelihood (ML) or Bayesian method via Markov chain Monte Carlo (MCMC). However, unusual features in the likelihood can cause difficulties in ML and Bayesian inference and estimation, especially with small samples. In this study we explore several problems in drawing inference for LTA in the context of a simulation study and a substance use example. We argue that when conventional ML and Bayesian estimates behave erratically, problems often may be alleviated with a small amount of prior input for LTA with small samples. This paper proposes a dynamic data-dependent prior for LTA with small samples and compares the performance of the estimation methods with the proposed prior in drawing inference.     

Meta-analysis: formulating, evaluating, combining, and reporting

Meta-analysis involves combining summary information from related but independent studies. The objectives of a meta-analysis include increasing power to detect an overall treatment effect, estimation of the degree of benefit associated with a particular study treatment, assessment of the amount of variability between studies, or identification of study characteristics associated with particularly effective treatments. This article presents a tutorial on meta-analysis intended for anyone with a mathematical statistics background. Search strategies and review methods of the literature are discussed. Emphasis is focused on analytic methods for estimation of the parameters of interest. Three modes of inference are discussed: maximum likelihood; restricted maximum likelihood, and Bayesian. Finally, software for performing inference using restricted maximum likelihood and fully Bayesian methods are demonstrated. Methods are illustrated using two examples: an evaluation of mortality from prophylactic use of lidocaine after a heart attack, and a comparison of length of hospital stay for stroke patients under two different management protocols.     

BAYESIAN ANALYSIS FOR A SINGLE 2×2 TABLE

The simple comparison of two binomial populations is frequently of interest in epidemiology when the domains are large. For small domains, however, there are no exact methods except Fisher's exact test. A basic problem, therefore, is to compare two populations by assessing the difference between the proportions of individuals who possess a characteristic in the first and second populations. When there is prior information, we take the proportions to have independent conjugate beta distributions with known parameters, thereby facilitating a Bayesian analysis. We consider Bayesian inference on functions of the proportions, and the three most common scalar measures used in epidemiology and health services research, namely relative risk, odds ratio and attributable risk. We develop the highest density regions (both exact and approximate) for relative risk, odds ratio and attributable risk. In addition, we consider the Bayes factor for testing whether the model with a common proportion holds rather than one with distinct proportions. Using data from the population-based Worcester Heart Attack Study, we apply our methodology to study gender differences in the therapeutic management of patients with acute myocardial infarction (AMI) by selected demographic and clinical characteristics. The Bayes factor, the approximate and exact intervals generally suggest that there are no substantial differences in the pharmacologic management of males and females hospitalized with AMI. © 1997 by John Wiley & Sons, Ltd.     

Semiparametric Bayesian modeling of random genetic effects in family-based association studies

We consider the inference problem of estimating covariate and genetic effects in a family-based case-control study where families are ascertained on the basis of the number of cases within the family. However, our interest lies not only in estimating the fixed covariate effects but also in estimating the random effects parameters that account for varying correlations among family members. These random effects parameters, though weakly identifiable in a strict theoretical sense, are often hard to estimate due to the small number of observations per family. A hierarchical Bayesian paradigm is a very natural route in this context with multiple advantages compared with a classical mixed effects estimation strategy based on the integrated likelihood. We propose a fully flexible Bayesian approach allowing nonparametric modeling of the random effects distribution using a Dirichlet process prior and provide estimation of both fixed effect and random effects parameters using a Markov chain Monte Carlo numerical integration scheme. The nonparametric Bayesian approach not only provides inference that is less sensitive to parametric specification of the random effects distribution but also allows possible uncertainty around a specific genetic correlation structure. The Bayesian approach has certain computational advantages over its mixed-model counterparts. Data from the Prostate Cancer Genetics Project, a family-based study at the University of Michigan Comprehensive Cancer Center including families having one or more members with prostate cancer, are used to illustrate the proposed methods. A small-scale simulation study is carried out to compare the proposed nonparametric Bayes methodology with a parametric Bayesian alternative.     

Functional interaction–based nonlinear models with application to multiplatform genomics data

Functional regression allows for a scalar response to be dependent on a functional predictor; however, not much work has been done when a scalar exposure that interacts with the functional covariate is introduced. In this paper, we present 2 functional regression models that account for this interaction and propose 2 novel estimation procedures for the parameters in these models. These estimation methods allow for a noisy and/or sparsely observed functional covariate and are easily extended to generalized exponential family responses. We compute standard errors of our estimators, which allows for further statistical inference and hypothesis testing. We compare the performance of the proposed estimators to each other and to one found in the literature via simulation and demonstrate our methods using a real data example.     

Improving estimation and prediction in linear regression incorporating external information from an established reduced model

We consider a situation where there is rich historical data available for the coefficients and their standard errors in a linear regression model describing the association between a continuous outcome variable Y and a set of predicting factors X , from a large study. We would like to use this summary information for improving inference in an expanded model of interest, Y given X , B . The additional variable B is a new biomarker, measured on a small number of subjects in a new dataset. We formulate the problem in an inferential framework where the historical information is translated in terms of nonlinear constraints on the parameter space and propose both frequentist and Bayes solutions to this problem. We show that a Bayesian transformation approach proposed by Gunn and Dunson is a simple and effective computational method to conduct approximate Bayesian inference for this constrained parameter problem. The simulation results comparing these methods indicate that historical information on E( Y | X ) can improve the efficiency of estimation and enhance the predictive power in the regression model of interest E( Y | X , B ). We illustrate our methodology by enhancing a published prediction model for bone lead levels in terms of blood lead and other covariates, with a new biomarker defined through a genetic risk score.