设计效应分解在复杂样本设计中的应用研究

在复杂样本设计中,多种抽样方法的结合使得设计效应的直接应用受到限制。通过对设计效应的影响因素进行分解,给出分层、类集、加权调整产生的要素设计效应模型。研究结果表明:复杂样本设计通过影响总体单位的相关性来影响设计效应,分层设计带来的负相关性将有限减少设计效应,类集设计带来的正相关性将显著增大设计效应,加权调整带来的权数变化一般会增大设计效应,所以控制非比例分配和类集的设计效应是进行有效样本设计的关键。对于子群、分析统计量的设计效应,虽然样本设计引起的总体单位间相关性减弱,仍可以通过样本均值的设计效应进行推断。     

住户调查中无回答误差分析与调整方法研究

一、研究背景在抽样调查中,我们需要给样本中的每个单元赋予一个权数,通过最终权数和观测值得到观测变量的估计值。加权过程应考虑到并校正覆盖不完整、抽样可变性和无回答的错误。加权进行得越好,估计的方差和偏差就会越小。权数的计算通常有以下三步:抽样权数即设计权数、无回答的调整和校准。其中抽样权数是在设计阶段计算的,无回答的调整是将设计权数通过无回答的补偿来调整,校准是通过已知总体的辅助信     

抽样调查中的权数问题研究

用样本数据推断总体,权数的作用十分重要。使用权数,不仅能将样本还原到总体,还能调整样本结构,使其与总体结构相一致,因此正确的使用权数是我们进行统计推断的基础。本文系统阐述了抽样调查分析中权数的获取过程,以及后期对初始权数调整过程。由于权数是把双刃剑,在提高精度的同时,有可能提高估计量的误差,本文提出了对权数进行评估的方法,研讨如何对权数进行控制,最后根据我国综合社会调查项目（CGSS）的数据进行实证分析,按照所给方法不仅能提高估计精度,而且能够降低抽样推断中的权效应。     

走出事后分层应用的误区

顾名思义,事后分层方法是在抽样设计中(事先)没有进行分层处理的前提下,在数据处理阶段(事后),利用抽样框信息或者可靠的外部信息,对样本进行事后分层处理,对样本的权数进行调整,以达到提高估计量精度的效果。外部信息可以是各事后层(子总体)的单位数,也可以是各事后层(子总体)单位数占总体单位数的比重等,一般来源于抽样框信息,或者建立抽样框与样本调查期间进行的普查信息等。应用事后分层方法,需要遵循三点基本原则:第一,调查时点样本单位的属性决定该样本单位的推算归属;第二,样本单位的基础权数及其加权调整都决定于该样本单位在抽样框…     

权数在人口抽样调查估计中的应用研究

很多全国性大型人口抽样调查项目中的核心技术包括抽样方案设计、样本单元的权数计算等。用样本数据推断总体,权数的作用很关键,尤其是抽样调查越来越难保证做到等概率抽样的情况。为此,采用第四次中国城乡老年人生活状况抽样调查中的具体调查方案和实际调查数据,系统地对抽样过程中的权数进行测算,同时根据权效应对权数进行调整和控制,利用刀切法对相关统计量的标准误差和变异系数进行对比分析,来验证权数控制在提高精度方面的有效性。结合老年人生活状况的调查数据进行实证分析,结果表明,利用刀切法计算权数截取的标准误差比未调整权数的标准误差更有效,得到变量的变异系数更小。     

网络访问固定样本调查的统计推断研究

如何解决网络访问固定样本调查的统计推断问题,是大数据背景下网络调查面临的严重挑战。针对此问题,提出将网络访问固定样本的调查样本与概率样本结合,利用倾向得分逆加权和加权组调整构造伪权数来估计目标总体,进一步采用基于有放回概率抽样的Vwr方法、基于广义回归估计的Vgreg方法与Jackknife方法来估计方差,并比较不同方法估计的效果。研究表明:无论概率样本的样本量较大还是较小,本研究所提出的总体均值估计方法效果较好,并且在方差估计中Jackknife方法的估计效果最好。     

大数据背景下非概率抽样的统计推断问题

利用大数据进行抽样,很多情况下抽样框的构造比较困难,使得抽取的样本属于非概率样本,难以将传统的抽样推断理论应用到非概率样本中,如何解决非概率抽样的统计推断问题,是大数据背景下抽样调查面临的严重挑战。本文提出了解决非概率抽样统计推断问题的基本思路：一是抽样方法,可以考虑基于样本匹配的样本选择、链接跟踪抽样方法等,使得到的非概率样本近似于概率样本,从而可采用概率样本的统计推断理论;二是权数的构造与调整,可以考虑基于伪设计、模型和倾向得分等方法得到类似于概率样本的基础权数;三是估计,可以考虑基于伪设计、模型和贝叶斯的混合概率估计。最后,以基于样本匹配的样本选择为例探讨了具体解决方法。     

关于编制物价指数的思考

物价指数是统计指数的一种,它是反映商品和服务价格动态变化的商品零售价格指数和居民消费价格指数。实际经济工作中,我国是按固定权数计算物价的加权平均数。有关样本的代表性、有关各类零售商品结构、服务结构权数的确定等都还不够严密、科学,从而使物价指数不能充分发挥其应有功能。 一、目前物价指数编制中存在的问题 １、样本的代表性不高。目前我国的价格调查商品的样本目录基本上还同于市场经济初期的范围,其中虽经过适当调整,５不能适应当今市场的变化。 ２、价格的可比性较低。我国政府对外发布的月度价格指数,通常都是以前…     

基于“三新”企业分层抽样单元权重动态调整的估计方法

对“三新”企业进行抽样调查是及时掌握和监测“三新”经济发展的重要手段。考虑到这一类调查总体单元变动比较迅速,抽样框信息变动大,无法及时覆盖总体的最新特征,依此抽样框得到的样本数据结构与总体的分布结构差异较大,样本的代表性较低,会对总体数量特征的有效估计产生影响。因此,基于调查总体单元的变动特征,把抽样框中的单元划分为保留单元和转移单元,在此基础上,依据样本单位分层结构的变动,设计了基于“三新”企业分层抽样单元权重动态调整的估计方法。首先,通过事后分层方法挖掘出不同层的单位特征,并预测抽样框各层容量;其次,依据层规模的变动预测对目标变量估计量的权重进行修正;最后,通过自我加权设计构造出总体动态变动后数量特征的复合估计量,并对其进行优良性讨论。在对“三新”企业的模拟数据进行多次重复抽样实验中,相比于固定抽样框下的传统方法,基于分层抽样单元权重动态调整的估计方法具有更高的抽样效率,构造的关于总体数量特征的估计量具有无偏性和有效性。     

权数对基于模型推断的影响分析

利用抽样调查数据对总体参数进行推断通常分为两种途径：一种是基于设计的推断体系；另一种是基于模型的推断体系。基于设计的推断以随机化理论为基础，推断依赖于抽样设计，在大样本下估计量具有无偏性和一致性，但在样本量较小或存在非抽样误差等情况下效率较低。基于模型的推断认为有限总体是一个来自无限超总体的随机样本，推断依赖于模型假设，构建超总体模型具有很大的灵活性，有利于充分利用总体辅助信息并提高估计精度，但在模型假定有误或样本的入样过程不具有无信息性时存在估计误差。如何将两种推断途径相结合，在体现样本对总体代表性的同时，保证估计效率和估计量的优良性质，尚待研究。权数在基于设计的推断中起着核心作用，能够反映抽样设计对样本的影响，实现样本对总体的还原。将权数引入基于模型的推断，可以使基于模型推断的结果具有总体代表性，能更好地发挥两种推断体系的组合优势，并削弱模型假定对推断效果的影响。据此，从权数对于模型推断的影响入手，针对因果推断问题，提出将权数同时引入倾向得分模型和预测模型的建模过程，来构造双稳健估计的方法，并通过模拟研究加以验证。最终结果表明，根据文章所提出的方法进行处理效应的估计，能够充分发挥权数的作用，得到更准确、更稳健的估计结果。实证部分采用2017年CGSS调查数据进行分析，进一步说明在基于调查数据进行模型推断时应充分考虑抽样设计的影响，为科研人员进行因果推断以及其他基于调查数据开展的研究提供参考。     

A convenient formula for sample size calculations in clinical trials with multiple co-primary continuous endpoints

The clinical efficacy of a new treatment may often be better evaluated by two or more co-primary endpoints. Recently, in pharmaceutical drug development, there has been increasing discussion regarding establishing statistically significant favorable results on more than one endpoint in comparisons between treatments, which is referred to as a problem of multiple co-primary endpoints. Several methods have been proposed for calculating the sample size required to design a trial with multiple co-primary correlated endpoints. However, because these methods require users to have considerable mathematical sophistication and knowledge of programming techniques, their application and spread may be restricted in practice. To improve the convenience of these methods, in this paper, we provide a useful formula with accompanying numerical tables for sample size calculations to design clinical trials with two treatments, where the efficacy of a new treatment is demonstrated on continuous co-primary endpoints. In addition, we provide some examples to illustrate the sample size calculations made using the formula. Using the formula and the tables, which can be read according to the patterns of correlations and effect size ratios expected in multiple co-primary endpoints, makes it convenient to evaluate the required sample size promptly.     

Optimizing trial design in pharmacogenetics research: comparing a fixed parallel group,group sequential,and adaptive selection design on sample size requirements

Two‐stage clinical trial designs may be efficient in pharmacogenetics research when there is some but inconclusive evidence of effect modification by a genomic marker. Two‐stage designs allow to stop early for efficacy or futility and can offer the additional opportunity to enrich the study population to a specific patient subgroup after an interim analysis. This study compared sample size requirements for fixed parallel group, group sequential, and adaptive selection designs with equal overall power and control of the family‐wise type I error rate. The designs were evaluated across scenarios that defined the effect sizes in the marker positive and marker negative subgroups and the prevalence of marker positive patients in the overall study population. Effect sizes were chosen to reflect realistic planning scenarios, where at least some effect is present in the marker negative subgroup. In addition, scenarios were considered in which the assumed ‘true’ subgroup effects (i.e., the postulated effects) differed from those hypothesized at the planning stage. As expected, both two‐stage designs generally required fewer patients than a fixed parallel group design, and the advantage increased as the difference between subgroups increased. The adaptive selection design added little further reduction in sample size, as compared with the group sequential design, when the postulated effect sizes were equal to those hypothesized at the planning stage. However, when the postulated effects deviated strongly in favor of enrichment, the comparative advantage of the adaptive selection design increased, which precisely reflects the adaptive nature of the design. Copyright © 2013 John Wiley & Sons, Ltd.     

Evaluation of adjustments for partial non-response bias in the US National Immunization Survey

Summary.  Many health surveys conduct an initial household interview to obtain demographic information and then request permission to obtain detailed information on health outcomes from the respondent's health care providers. A 'complete response' results when both the demographic information and the detailed health outcome data are obtained. A 'partial response' results when the initial interview is complete but, for one reason or another, the detailed health outcome information is not obtained. If 'complete responders' differ from 'partial responders' and the proportion of partial responders in the sample is at least moderately large, statistics that use only data from complete responders may be severely biased. We refer to bias that is attributable to these differences as 'partial non-response' bias. In health surveys it is customary to adjust survey estimates to account for potential differences by employing adjustment cells and weighting to reduce bias from partial response. Before making these adjustments, it is important to ask whether an adjustment is expected to increase or decrease bias from partial non-response. After making these adjustments, an equally important question is 'How well does the method of adjustment work to reduce partial non-response bias?'. The paper describes methods for answering these questions. Data from the US National Immunization Survey are used to illustrate the methods.     

Sample Size Calculation and Blinded Sample Size Recalculation in Clinical Trials Where the Treatment Effect is Measured by the Relative Risk

In clinical trials with binary endpoints, the required sample size does not depend only on the specified type I error rate, the desired power and the treatment effect but also on the overall event rate which, however, is usually uncertain. The internal pilot study design has been proposed to overcome this difficulty. Here, nuisance parameters required for sample size calculation are re-estimated during the ongoing trial and the sample size is recalculated accordingly. We performed extensive simulation studies to investigate the characteristics of the internal pilot study design for two-group superiority trials where the treatment effect is captured by the relative risk. As the performance of the sample size recalculation procedure crucially depends on the accuracy of the applied sample size formula, we firstly explored the precision of three approximate sample size formulae proposed in the literature for this situation. It turned out that the unequal variance asymptotic normal formula outperforms the other two, especially in case of unbalanced sample size allocation. Using this formula for sample size recalculation in the internal pilot study design assures that the desired power is achieved even if the overall rate is mis-specified in the planning phase. The maximum inflation of the type I error rate observed for the internal pilot study design is small and lies below the maximum excess that occurred for the fixed sample size design.     

An Application of Non–Ignorable Non–Response Models for Gross Flows Estimation in the British Labour Force Survey

In previous work, non–response adjustments based on calibration weighting have been proposed for estimating gross flows in economic activity status from the quarterly Labour Force Survey. However, even after adjustment there may be residual non–response bias. The weighting is based on estimates of cross–sectional distributions and so cannot adjust for bias if non–response is associated with individual flows between quarters. To investigate this possibility, it was decided to apply models for estimating gross flows when non–response depends on the flows. This paper has two aims: first to describe the many problems encountered when attempting to implement these models; and second to outline a solution to the major problem that arose, namely, that comparing the model results directly with the weighting results was not possible. A simulation study was used to compare the results indirectly and it was tentatively concluded that non–response is not strongly associated with the flows and that the weighting provides an adequate adjustment.     

The Finite Sample Performance of Inference Methods for Propensity Score Matching and Weighting Estimators

ABSTRACT

This article investigates the finite sample properties of a range of inference methods for propensity score-based matching and weighting estimators frequently applied to evaluate the average treatment effect on the treated. We analyze both asymptotic approximations and bootstrap methods for computing variances and confidence intervals in our simulation designs, which are based on German register data and U.S. survey data. We vary the design w.r.t. treatment selectivity, effect heterogeneity, share of treated, and sample size. The results suggest that in general, theoretically justified bootstrap procedures (i.e., wild bootstrapping for pair matching and standard bootstrapping for “smoother” treatment effect estimators) dominate the asymptotic approximations in terms of coverage rates for both matching and weighting estimators. Most findings are robust across simulation designs and estimators.     

Sample size determination for testing equality in frequency data under an incomplete block crossover design

When there are more than two treatments under comparison, we may consider the use of the incomplete block crossover design (IBCD) to save the number of patients needed for a parallel groups design and reduce the duration of a crossover trial. We develop an asymptotic procedure for simultaneously testing equality of two treatments versus a control treatment (or placebo) in frequency data under the IBCD with two periods. We derive a sample size calculation procedure for the desired power of detecting the given treatment effects at a nominal-level and suggest a simple ad hoc adjustment procedure to improve the accuracy of the sample size determination when the resulting minimum required number of patients is not large. We employ Monte Carlo simulation to evaluate the finite-sample performance of the proposed test, the accuracy of the sample size calculation procedure, and that with the simple ad hoc adjustment suggested here. We use the data taken as a part of a crossover trial comparing the number of exacerbations between using salbutamol or salmeterol and a placebo in asthma patients to illustrate the sample size calculation procedure.