死因监测整群抽样与不等概率抽样设计方案的比较

为比较不同整群抽样设计方法 的抽样误差及设计效应,评价不等概率抽样在死因监测中的应用效果.以陕西省107个县(市、区)作为抽样框架,采用等概率整群抽样和不等概率整群抽样等设计方案抽取样本,用复杂抽样方法 计算不同方案样本的抽样误差和设计效应.不同的抽样方案得到不同的抽样误差估计,分层整群抽样的标准误小于完全随机整群抽样;不等概率抽样(πPS抽样)的设计效率虽略逊于等概率的完全随机整群抽样,但扩大了监测范围.结论 :对于抽样框架明确的整群抽样调查数据,在统计分析时不应脱离预先设定的抽样设计方案和设计参数.死因监测采用不等概率抽样设计,能增加样本的权重,提高死亡率的地区代表性.     

考虑多阶段抽样设计的误差估计

多阶段随机抽样是公共卫生开展人群抽样调查的常用设计。多阶段抽样设计下获得的样本具有复杂样本的特征,存在群效应或数据不独立,若不考虑抽样设计,通常会低估抽样误差或增加统计推断Ⅰ类错误的风险。由于复杂样本误差估计形式较复杂,目前常用统计软件均默认采用极群方差估计策略来简化样本结构,即假设样本来自于一阶段整群抽样,忽略除第一阶段抽样外的所有抽样设计,从而实现对误差的近似估计。然而,在初级抽样单元入样比较高时,后继抽样阶段对误差的贡献不可忽略,极群方差估计策略可能导致无效的误差估计。本文旨在介绍考虑多阶段抽样设计下的误差估计方法,并通过对现实数据进行多阶段模拟抽样,探讨在不同抽样设计下,极群方差估计策略和考虑多阶段抽样设计下的误差估计差异。模拟结果显示,随初级抽样单元入样比的增加,极群方差估计策略估计的误差出现不同程度的偏倚,且随入样比增加偏倚加重;而考虑多阶段抽样设计下的误差估计则较准确反映误差水平,可得到准确的统计推断结果。     

复杂抽样调查数据实例分析

提出复杂抽样调查数据的分析思路和方法以及忽视权重和抽样设计时会出现的问题.文中以2002年中国居民营养与健康状况调查数据中高血压患病率的估算为例,分加权和不加权、考虑和不考虑整群设计特征的四种组合情况对数据进行分析.表明忽视权重的设置会影响点估计和标准误的估计,忽视对整群设计特征的考虑不仅会高估结果的精确度,还会得到地区间患病率有差异的假阳性结论.因此使用合理的统计方法分析复杂抽样调查数据非常有必要.     

整群抽样现场调查中样本大小的估计方法

整群抽样是常用的流行病学、卫生现场调查的抽样方法。它的抽样单位不是个体,而是包含有多个个体的整群。整群抽样的优点是方便、实用。进行整群抽样时,力求群间差异小(群内差异稍大些无妨),以提高统计效率。整群抽样误差大于单纯随机抽样,所以样本大小     

分层按构成比系统整群随机抽样法

在医学研究和防治工作中经常采用抽样调查，用样本去推论总体。样本对总体的代表性强弱关系到推论结果的可信程度。而抽样方法是否正确严密直接影响样本代表性。笔者在工作实践中设计了“分层按构成比系统整群随机抽样法”，曾用于陕西省世行“卫Ⅵ”贷款项目永寿试点县基线调查这一大型复杂的综合性调查抽样，后经省级有关专家审查，报卫生部统计信息中心批准，在咸阳市部分项目县推广应用。现将这一抽样方法以永寿县“卫Ⅵ”项目基线调查抽样为例介绍如下，仅供实际工作中参考。     

随机抽样方法的SAS实现

在实际调查过程中，我们往往采用抽样调查的方法获得有代表性的样本。目前流行病学调查中常用的随机抽样方法有单纯随机抽样、系统抽样、分层抽样等。在实际应用中，面对大量的调查数据，如何使用统计软件快速有效地进行抽样，这是实际中经常碰到的问题，现介绍这些抽样方法如何在SAS 8.2中实现。     

快速流行病学评价法抽样的应用评价

本文利用一个基于全人群的大肠癌危险因素病例对照组研究资料，来评价快速流行病学评价法抽样方法的实用价值；并与对同一总体的整群及完全单纯随机抽样方法作了比较。结果表明整群、快速流行病学评价法和单纯随机抽样的样本暴露率在ａ＝０．０５水平上对总体的错估率分别为２１．９％、９．４％和０。３２项因素的样本率与相应总体率之间的平衡分别为：０．０４３、０．０２８和０．０２１，且快速流行病学议价法抽样误差与单纯随机     

多阶段整群随机抽样方法在流行病学研究中的运用

抽样调查是医学科研工作尤其是流行病学研究中常用的调查方法,所抽取的样本对总体的代表性的好坏直接关系到结果的可信程度.基本的抽样方法如单纯随机抽样、系统抽样、分层抽样、整群抽样等各有优缺点[1],在大规模流行病学抽样调查中经常几种方法联合使用[2,3],以使得抽样结果能够较好地代表实际总体的情况.     

三阶段抽样样本大小的研究及应用

目的 为调查设计中常用的三阶段随机抽样方法寻求其样本大小估计公式。方法 利用高等数学中的哥西不等式原理及求极小值方法。结果 当采用三阶段随机抽样作参数估计时，在限定抽样误差使调查花费最小及限定调查花费使抽样误差最小两种情况下，推导出其最优样本大小的计算公式。结论 本文首次推导出三阶段随机抽样本大小的估计公式，在中国铁路职工医疗费用的抽样调查中取得了成功的应用效果。     

整群抽样所需样本大小的简便估计

整群抽样所需样本大小的简便估计合肥联合大学黄体乾安徽医科大学施仲赋合肥农经学院韩嵘抽样调查是从调查总体中抽取一定数量的单位组成样本，然后对样本中的单位进行调查。在大规模抽样调查中，整群抽样是经常采用的方法。整群抽样是先将总体分成Ｎ个群，然后从Ｎ个群中...     

Using threshold regression to analyze survival data from complex surveys: With application to mortality linked NHANES III Phase II genetic data

The Cox proportional hazards (PH) model is a common statistical technique used for analyzing time‐to‐event data. The assumption of PH, however, is not always appropriate in real applications. In cases where the assumption is not tenable, threshold regression (TR) and other survival methods, which do not require the PH assumption, are available and widely used. These alternative methods generally assume that the study data constitute simple random samples. In particular, TR has not been studied in the setting of complex surveys that involve (1) differential selection probabilities of study subjects and (2) intracluster correlations induced by multistage cluster sampling. In this paper, we extend TR procedures to account for complex sampling designs. The pseudo‐maximum likelihood estimation technique is applied to estimate the TR model parameters. Computationally efficient Taylor linearization variance estimators that consider both the intracluster correlation and the differential selection probabilities are developed. The proposed methods are evaluated by using simulation experiments with various complex designs and illustrated empirically by using mortality‐linked Third National Health and Nutrition Examination Survey Phase II genetic data.     

Inappropriate Survey Design Analysis of the Korean National Health and Nutrition Examination Survey May Produce Biased Results

Objectives

The inherent nature of the Korean National Health and Nutrition Examination Survey (KNHANES) design requires special analysis by incorporating sample weights, stratification, and clustering not used in ordinary statistical procedures.

Methods

This study investigated the proportion of research papers that have used an appropriate statistical methodology out of the research papers analyzing the KNHANES cited in the PubMed online system from 2007 to 2012. We also compared differences in mean and regression estimates between the ordinary statistical data analyses without sampling weight and design-based data analyses using the KNHANES 2008 to 2010.

Results

Of the 247 research articles cited in PubMed, only 19.8% of all articles used survey design analysis, compared with 80.2% of articles that used ordinary statistical analysis, treating KNHANES data as if it were collected using a simple random sampling method. Means and standard errors differed between the ordinary statistical data analyses and design-based analyses, and the standard errors in the design-based analyses tended to be larger than those in the ordinary statistical data analyses.

Conclusions

Ignoring complex survey design can result in biased estimates and overstated significance levels. Sample weights, stratification, and clustering of the design must be incorporated into analyses to ensure the development of appropriate estimates and standard errors of these estimates.     

Grouping methods for estimating the prevalences of rare traits from complex survey data that preserve confidentiality of respondents

Originally, 2‐stage group testing was developed for efficiently screening individuals for a disease. In response to the HIV/AIDS epidemic, 1‐stage group testing was adopted for estimating prevalences of a single or multiple traits from testing groups of size q, so individuals were not tested. This paper extends the methodology of 1‐stage group testing to surveys with sample weighted complex multistage‐cluster designs. Sample weighted‐generalized estimating equations are used to estimate the prevalences of categorical traits while accounting for the error rates inherent in the tests. Two difficulties arise when using group testing in complex samples: (1) How does one weight the results of the test on each group as the sample weights will differ among observations in the same group. Furthermore, if the sample weights are related to positivity of the diagnostic test, then group‐level weighting is needed to reduce bias in the prevalence estimation; (2) How does one form groups that will allow accurate estimation of the standard errors of prevalence estimates under multistage‐cluster sampling allowing for intracluster correlation of the test results. We study 5 different grouping methods to address the weighting and cluster sampling aspects of complex designed samples. Finite sample properties of the estimators of prevalences, variances, and confidence interval coverage for these grouping methods are studied using simulations. National Health and Nutrition Examination Survey data are used to illustrate the methods.     

多阶段抽样调查资料的加权估计法

多阶段抽样技术广泛应用于流行病学现况调查中,但针对其所得资料的统计分析方法往往选择不当.文中介绍一种用于多阶段抽样调查资料的统计分析方法--加权估计法,以推广针对此类资料的恰当的分析方法.在介绍加权估计法基本原理的基础上通过两个二阶段分层整群抽样的实际调查资料实现这种算法.加权估计法可以校正由多阶段抽样导致的三种效应:群效应、分层效应、不等概率性,给出无偏点估计和比较客观的误差估计,并作出正确的统计推断.     

A simplified general method for cluster-sample surveys of health in developing countries

General guidelines are presented for the use of cluster-sample surveys for health surveys in developing countries. The emphasis is on methods which can be used by practitioners with little statistical expertise and no background in sampling. A simple self-weighting design is used, based on that used by the World Health Organization's Expanded Programme on Immunization (EPI). Topics covered include sample design, methods of random selection of areas and households, sample-size calculation and the estimation of proportions, ratios and means with standard errors appropriate to the design. Extensions are discussed, including stratification and multiple stages of selection. Particular attention is paid to allowing for the structure of the survey in estimating sample size, using the design effect and the rate of homogeneity. Guidance is given on possible values for these parameters. A spreadsheet is included for the calculation of standard errors.     

Use of Design Effects and Sample Weights in Complex Health Survey Data: A Review of Published Articles Using Data From 3 Commonly Used Adolescent Health Surveys

Objectives. We assessed how frequently researchers reported the use of statistical techniques that take into account the complex sampling structure of survey data and sample weights in published peer-reviewed articles using data from 3 commonly used adolescent health surveys.Methods. We performed a systematic review of 1003 published empirical research articles from 1995 to 2010 that used data from the National Longitudinal Study of Adolescent Health (n = 765), Monitoring the Future (n = 146), or Youth Risk Behavior Surveillance System (n = 92) indexed in ERIC, PsycINFO, PubMed, and Web of Science.Results. Across the data sources, 60% of articles reported accounting for design effects and 61% reported using sample weights. However, the frequency and clarity of reporting varied across databases, publication year, author affiliation with the data, and journal.Conclusions. Given the statistical bias that occurs when design effects of complex data are not incorporated or sample weights are omitted, this study calls for improvement in the dissemination of research findings based on complex sample data. Authors, editors, and reviewers need to work together to improve the transparency of published findings using complex sample data.Secondary data analysis of nationally representative health surveys is commonly conducted by health science researchers and can be extremely useful when they are investigating risk and protective factors associated with health-related outcomes. By providing access to a vast array of variables on large numbers of individuals, large-scale health survey data are enticing to many researchers. Many researchers, however, lack the methodological skills needed for effective access to and use of such data. Traditional statistical methods and software analysis programs assume that data were generated through simple random sampling, with each individual having equal probability of being selected. With large, nationally representative health surveys, however, this is often not the case. Instead, from the perspective of statistical analysis, data from these complex sample surveys differ from those obtained via simple random sampling in 4 respects.First, the probabilities of selection of the observations are not equal; oversampling of certain subgroups in the population is often employed in survey sample design to allow reasonable precision in the estimation of parameters. Second, multistage sampling results in clustered observations in which the variance among units within each cluster is less than the variance among units in general. Third, stratification in sampling ensures appropriate sample representation on the stratification variable(s), but yields negatively biased estimates of the population variance. Fourth, unit nonresponse and other poststratification adjustments are usually applied to the sample to allow unbiased estimates of population characteristics.1 If these aspects of complex survey data are ignored, standard errors and point estimates are biased, thereby potentially leading to incorrect inferences being made by the researcher.     

Efficiency of EPI cluster sampling for assessing diarrhoea and dysentery prevalence.

This study examines the efficiency of EPI cluster sampling in assessing the prevalence of diarrhoea and dysentery. A computer was used to simulate fieldwork carried out by a survey taker. The bias and variance of prevalence estimates obtained using EPI cluster sampling were compared with those obtained using simple random sampling and cluster (stratified random) sampling. Efficiency ratios, calculated as the mean square error divided by total distance travelled, were used to compare EPI cluster sampling to simple random sampling and standard cluster sampling. EPI cluster sampling may be an appropriate low-cost tool for monitoring trends in the prevalence of diarrhoea and dysentery over time. However, it should be used with caution when estimating the prevalence of diarrhoea at a single point in time because of the bias associated with this cluster sampling method.