分层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分层三阶段随机抽样方法寻求其样本大小估计公式。方法：利用微积分学中求极小值的方法。结果：当采用分层三阶段随机抽样作参数估计时,在限定抽样误差的大小使调查花费达到最小及限定调查花费的大小使抽样误差达到最小两种情况下,推导出其最优样本大小的计算公式。结论：推导出分层三阶段随机抽样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。     

分层二阶段抽样样本大小的估计方法

抽样调查是卫生工作及医学科研中常用的调查研究方法。本文对分层二阶段抽样，在限定抽样误差大小使调查花费达到最小及限定调查花费的大小使抽样误差达到最小两种情况下，从数学上推导出其最优样本大小估计公式，并结合作者１９９８年主持的铁道部课题——中国铁路系统医...     

二阶段抽样样本大小的估计方法

抽样调查在医疗卫生事业中有着广泛的用途。调查设计的关键之一,就是要确定抽样的样本大小。Cochran WG对常用的二阶段抽样,在各群大小(各群包含的个体数)相等的特殊情况下,求出了限定抽样调查的花费,使抽样误差达到最小的样本大小计算公式。但在实际的抽样调查中,各群的大小一般并不相等,且更常见的是要求限定抽样误差,使调查花费最省。本文对二阶段抽样,在各群大小不相等的一般情况下,求出了限定抽样误差,使     

整群随机化试验设计样本例数估计

目的探讨两阶段整群随机化设计两组比较样本例数估计方法。方法通过估计组内相关系数，根据设计效应及群的大小估计样本例数。结果本文给出了计量和计数两种情况下的样本含量估计公式。结论在整群随机化设计中，数据由于隶属关系而存在聚集性，表现为至少两个水平的层次结构及组内相关性，因此在设计阶段需对其设计效应进行估计，进而估计其样本例数。     

整群抽样调查数据分析中应正确计算抽样误差

为了澄清整群抽样调查数据分析中正确计算抽样误差的必要性，以在某市15岁及以上人群中开展的一次两阶段整群抽样调查为例，分别采用适用于单纯随机抽样数据的方法和考虑了复杂抽样设计的方法对数据进行分析。结果显示，忽略对复杂抽样设计的考虑，不恰当的采用适用于单纯随机抽样数据的方法进行数据分析，不仅有可能大大低估样本统计量的抽样误差，在进行假设检验时，甚至会得到错误的结果，故正确分析和报告整群抽样调查数据的抽样误差是非常必要的。     

样本轮换下两阶段抽样连续调查的统计方法及应用

目的为实际需要的样本轮换下两阶段抽样连续调查提供科学的调查方法与统计公式,为制定某核电站职工辐射防护措施提供依据。方法采用数理统计学的理论方法推导统计量的计算公式;采用本文研究的样本轮换下两阶段抽样连续调查的统计方法,对某核电站职工的白细胞数进行了连续三年的调查分析;采用SAS编程模拟调查分析100个样本,对本文研究的调查方法及其统计公式作信度与效度评价。结果对样本轮换下两阶段抽样连续调查,推导出总体均值的估计量及其方差与估计方差的计算公式;该核电站职工两阶段抽样均轮换样本的2010年、2011年白细胞数总体均值的估计量分别为5.88、5.84(103/mm3),其标准误分别为0.247、0.255,与一般成人白细胞均数的差异具有统计学意义;100个总体均数的95%可信区间均包含模拟总体均数。结论本文研究的样本轮换下两阶段抽样连续调查的统计方法具有较好的理论与实际意义以及较高的效度与信度;该核电站职工的白细胞数偏低,应引起相关部门的高度重视。     

多分类敏感问题RRT模型下分层三阶段抽样的统计方法及应用

目的为实际需要的多分类敏感性问题的复杂抽样提供信度高效度高的调查方法及其统计公式;为制订艾滋病预防控制措施提供科学依据。方法根据抽样理论、RRT模型、全概率公式等理论方法推导统计公式;对北京市MSM人群进行实例调查;用SAS编程分别对6个调查指标各类别,各模拟多分类敏感问题RRT模型下分层三阶段抽样调查100个样本,按本文给出的统计公式计算100个总体比例的可信区间。结果推导出多分类敏感问题RRT模型下分层三阶段抽样各类别总体比例估计量及其方差的计算公式;调查得到北京市MSM人群6项敏感问题指标各类别样本比例及其标准误。各指标各类别各模拟得到的100个总体比例95%可信区间几乎均包含其模拟总体比例。结论本文研究的敏感问题调查方法及其公式具有良好的信度和效度,值得推广应用;MSM人群具有艾滋病的高危性行为,应加强对其的预防控制。     

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     

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。     

分层随机抽样方法估计湖南省洪江区吸毒人群规模研究

目的 估计怀化市洪江区吸毒人群中HIV感染情况，为今后预测该地与吸毒相关情况服务。方法 应用Poison分布期望值可信限表，确定样本大小。应用最优分配分层随机抽样方法确定调查点。采用访谈方式获得数据，估计吸毒总人数。采用当年在所监测的感染率推算感染人数。结果 最优分配随机分层抽样调查共调查1388人，发现吸毒人员24人，占1．73％；洪江区总人口为72709人，估计总吸毒人数1258人。结论 该方法可以满足对吸毒人数的估计。     

Sample size calculation in cost‐effectiveness cluster randomized trials: optimal and maximin approaches

In this paper, the optimal sample sizes at the cluster and person levels for each of two treatment arms are obtained for cluster randomized trials where the cost‐effectiveness of treatments on a continuous scale is studied. The optimal sample sizes maximize the efficiency or power for a given budget or minimize the budget for a given efficiency or power. Optimal sample sizes require information on the intra‐cluster correlations (ICCs) for effects and costs, the correlations between costs and effects at individual and cluster levels, the ratio of the variance of effects translated into costs to the variance of the costs (the variance ratio), sampling and measuring costs, and the budget. When planning, a study information on the model parameters usually is not available. To overcome this local optimality problem, the current paper also presents maximin sample sizes. The maximin sample sizes turn out to be rather robust against misspecifying the correlation between costs and effects at the cluster and individual levels but may lose much efficiency when misspecifying the variance ratio. The robustness of the maximin sample sizes against misspecifying the ICCs depends on the variance ratio. The maximin sample sizes are robust under misspecification of the ICC for costs for realistic values of the variance ratio greater than one but not robust under misspecification of the ICC for effects. Finally, we show how to calculate optimal or maximin sample sizes that yield sufficient power for a test on the cost‐effectiveness of an intervention. Copyright © 2014 John Wiley & Sons, Ltd.     

The effect of clustering on lot quality assurance sampling: a probabilistic model to calculate sample sizes for quality assessments

Background

Traditional Lot Quality Assurance Sampling (LQAS) designs assume observations are collected using simple random sampling. Alternatively, randomly sampling clusters of observations and then individuals within clusters reduces costs but decreases the precision of the classifications. In this paper, we develop a general framework for designing the cluster(C)-LQAS system and illustrate the method with the design of data quality assessments for the community health worker program in Rwanda.

Results

To determine sample size and decision rules for C-LQAS, we use the beta-binomial distribution to account for inflated risk of errors introduced by sampling clusters at the first stage. We present general theory and code for sample size calculations.The C-LQAS sample sizes provided in this paper constrain misclassification risks below user-specified limits. Multiple C-LQAS systems meet the specified risk requirements, but numerous considerations, including per-cluster versus per-individual sampling costs, help identify optimal systems for distinct applications.

Conclusions

We show the utility of C-LQAS for data quality assessments, but the method generalizes to numerous applications. This paper provides the necessary technical detail and supplemental code to support the design of C-LQAS for specific programs.

     

Sample size and costs estimate in epidemiological survey of dental caries

This study aimed to analyze how the prevalence and the distribution of dental caries influence the sample size in epidemiological surveys, and how much are the costs. Secondary data of oral health surveys in 12-year-old schoolchildren from Bauru in 1976, 1984, 1990, 1994, and 2001, and from Piracicaba in 2001 and 2005 were studied. Sample sizes were estimated taking into account the mean DMFT and standard deviation of each survey, establishing sampling errors of 1%, 2%, 5%, and 10%. Costs were estimated considering permanent material, consumption material and human resources. The sample size in both towns needed to be increased, ranging from 119 in 1976 to 1,118 in 2001 in Bauru, and from 954 in 2001 to 1,252 in 2005 in Piracicaba, when a sampling error of 10% was considered. The cost of dental caries surveys was verified considering different sampling errors. This cost depends on how acceptable is the margin of difference between the true mean and the one found in the survey. In conclusion, the reduction in the prevalence of dental caries has determined the need for increase in sample size and in costs for conducting the surveys.     

SOME OPTIMAL AND NON-OPTIMAL TWO-STAGE DESIGNS USING AN α-SPENDING FUNCTION

While designing a group sequential clinical trial in the pharmaceutical industry setting, we often face a problem of determining the time for an interim analysis. For a two-stage trial we compute the sample sizes n and N per treatment group for the interim and final analyses. respectively, that minimize the average trial size for a specified overall power. We consider this optimization when we monitor the trial using a Lan–DeMets α-spending function. Two additional problems considered in this context are (i) finding the sample sizes that maximize the overall power for a specified average trial size, and (ii) finding the sample sizes that achieve specified powers at the interim and final analyses.     

Optimum sample size determination in stratified case-control studies with cost considerations.

We investigate sample size determination for Cochran's test for stratified case-control studies when samples of cases and controls are allocated to maximize the asymptotic efficiency of Cochran's test subject to fixed total cost with cost per control varying by strata. We consider two situations typical of strata-matched case-control studies: when one samples both cases and controls and when cases are given and one samples controls. In each situation we develop and study an asymptotic method for finding the sample size required for a specific power under the optimum allocation proposed by Nam and Fears. Also, for the second situation, we investigate an asymptotic method for determining the common ratio, k, in one-to-k strata-matched case-control studies without cost consideration for a given power. When cases are given, neither the optimum nor the standard control sample sizes appear in a closed form; we present numerical methods for calculating these sample sizes and illustrate them with examples. We find the reduction in total cost obtained under the optimum allocation compared to standard allocation more pronounced as the differences in stratum-specific costs of sampling controls increase.     

Adaptive clinical trial designs with pre‐specified rules for modifying the sample size: understanding efficient types of adaptation

Adaptive clinical trial design has been proposed as a promising new approach that may improve the drug discovery process. Proponents of adaptive sample size re‐estimation promote its ability to avoid ‘up‐front’ commitment of resources, better address the complicated decisions faced by data monitoring committees, and minimize accrual to studies having delayed ascertainment of outcomes. We investigate aspects of adaptation rules, such as timing of the adaptation analysis and magnitude of sample size adjustment, that lead to greater or lesser statistical efficiency. Owing in part to the recent Food and Drug Administration guidance that promotes the use of pre‐specified sampling plans, we evaluate alternative approaches in the context of well‐defined, pre‐specified adaptation. We quantify the relative costs and benefits of fixed sample, group sequential, and pre‐specified adaptive designs with respect to standard operating characteristics such as type I error, maximal sample size, power, and expected sample size under a range of alternatives. Our results build on others’ prior research by demonstrating in realistic settings that simple and easily implemented pre‐specified adaptive designs provide only very small efficiency gains over group sequential designs with the same number of analyses. In addition, we describe optimal rules for modifying the sample size, providing efficient adaptation boundaries on a variety of scales for the interim test statistic for adaptation analyses occurring at several different stages of the trial. We thus provide insight into what are good and bad choices of adaptive sampling plans when the added flexibility of adaptive designs is desired. Copyright © 2012 John Wiley & Sons, Ltd.     

Linked surveys of health services utilization

The linked population/establishment survey (LS) of health services utilization is a two-phase sample survey that links the sample designs of the population sample survey (PS) and the health-care provider establishment sample survey (ES) of health services utilization. In Phase I, household respondents in the PS identify their health-care providers during a specified calendar period. In Phase II, health-care providers identified in Phase I report the variables of interest for all or a sample of their transactions with all households during the same calendar period. The LS has been proposed as a potential design alternative to the PS whenever the health-care transactions of interest are hard to find or enumerate in household surveys and as a potential design alternative to the ES whenever it is infeasible or expensive to construct or maintain complete sampling provider frames that list all health-care providers with good measures of provider size. Suppose that the non-sampling errors are ignorable, how do the LS, PS and ES sampling errors compare? This paper addresses that question by summarizing and extending recent research findings that compare expressions of the sampling variance of (1) the LS and PS of equivalent household sample size and (2) the LS and the ES of equivalent expected health-care provider and transaction sample sizes. The paper identifies the parameters contributing to the precision differences and assesses the conditions that favour the LS or one or the other surveys. Published in 2007 by John Wiley & Sons, Ltd.     

Single-stage cluster sampling with a telescopic respondent rule: a variation motivated by a survey of dementia in elderly residents of Shanghai

In this report, we consider the situation in which one wishes to identify a cohort of a specified number of individuals within each of several domains for future follow-up studies based on a single-stage cluster sampling design. We develop sample size formulae relevant to this situation and introduce a variation of single-stage cluster sampling that seems more suitable in this situation than is ordinary single-stage cluster sampling. The basis for this variation is the concept that the definition of eligible respondents is not the same for all clusters. The use of this modified respondent rule (which we call telescopic) enables one to meet specified sample sizes in all domains of interest without the need to sample extra individuals in some domains. We used a version of this sampling design successfully in the field with a survey of elderly persons conducted in Shanghai, People's Republic of China.     

Sample size requirements for studying small populations in gerontology research

Calculating sample sizes required to achieve a specified level of precision when estimating population parameters is a common statistical task. As consumer surveys become increasingly common for nursing homes, home care agencies, other service providers, and state and local administrative agencies, standard methods to calculate sample size may not be adequate. Standard methods typically assume a normal approximation and require the specification of a plausible value of the unknown population trait. This paper presents a strategy to estimate sample sizes for small finite populations and when a range of possible population values is specified. This sampling strategy is hierarchical, employing first a hypergeometric sampling model, which directly addresses the finite population concern. This level is then coupled with a beta-binomial distribution for the number of population elements possessing the characteristic of interest. This second level addresses the concern that the population trait may range over an interval of values. The utility of this strategy is illustrated using a study of resident satisfaction in nursing homes.