Multistage Sampling Designs and Estimating Equations

In some applications it is cost efficient to sample data in two or more stages. In the first stage a simple random sample is drawn and then stratified according to some easily measured attribute. In each subsequent stage a random subset of previously selected units is sampled for more detailed and costly observation, with a unit's sampling probability determined by its attributes as observed in the previous stages. This paper describes multistage sampling designs and estimating equations based on the resulting data. Maximum likelihood estimates (MLEs) and their asymptotic variances are given for designs using parametric models. Horvitz–Thompson estimates are introduced as alternatives to MLEs, their asymptotic distributions are derived and their strengths and weaknesses are evaluated. The designs and the estimates are illustrated with data on corn production.     

Tests for Trend in Developmental Toxicity Experiments with Correlated Binary Data

In this article, the operating characteristics of recently proposed tests for trend in correlated binary data arising in laboratory studies of developmental toxicity are examined using both computer-generated and experimental data. Specifically, we consider adjusted Cochran-Armitgc tests based on the Rao-Scott transformation which are of the same general form as that for uncorrelated data. In addition, generalized score tests based on generalized estimating equations allowing for extra-binomial variation in the data are discussed. Specific forms of these statistics demonstrating favorable type I and type II error rates are identified and recommended for use in practice. The application of these tests is illustrated using data from studies of developmental toxicity that have been reported in the literature.     

The Extensively Corrected Score for Measurement Error Models

In measurement error problems, two major and consistent estimation methods are the conditional score and the corrected score. They are functional methods that require no parametric assumptions on mismeasured covariates. The conditional score requires that a suitable sufficient statistic for the mismeasured covariate can be found, while the corrected score requires that the object score function can be estimated without bias. These assumptions limit their ranges of applications. The extensively corrected score proposed here is an extension of the corrected score. It yields consistent estimations in many cases when neither the conditional score nor the corrected score is feasible. We demonstrate its constructions in generalized linear models and the Cox proportional hazards model, assess its performances by simulation studies and illustrate its implementations by two real examples.     

基于 BSC 的环保资金绩效审计评价指标体系构建

平衡计分卡（BSC）是一种以组织战略目标为中心、对绩效进行因果分析的审计评价技术,其因独特的优越性和实效性迅速成为学术界和实践派的宠儿,近年来对其研究和应用更是炙手可热。基于平衡计分卡的基本原理,结合环保资金绩效审计评价指标体系的特点,将平衡计分卡的指标内容和结构关系加以修正、整合和扩展,从财务、社会与公众、项目流程、学习和成长及可持续发展5个维度对环保资金绩效审计评价指标体系构建进行探索,并从评价方法、标准设定、权重赋值、加权计算、结果运用5个方面探讨了环保资金绩效审计评价指标体系的操作路径。     

Does the supplemental nutrition assistance program really increase obesity? The importance of accounting for misclassification errors

The prevalence of obesity among US citizens has grown rapidly over the last few decades, especially among low-income individuals. This has led to questions about the effectiveness of nutritional assistance programs such as the Supplemental Nutrition Assistance Program (SNAP). Previous results on the effect of SNAP participation on obesity are mixed. These findings are however based on the assumption that participation status can be accurately observed, despite significant misclassification errors reported in the literature. Using propensity score matching, we conclude that there seems to be a positive effect of SNAP participation on obesity rates for female participants and no such effect for males, a result that is consistent with several previous studies. However, an extensive sensitivity analysis reveals that the positive effect for females is sensitive to misclassification errors and to the conditional independence assumption. Thus analogous findings should also be used with caution unless examined under the prism of classification errors and of other assumptions used for the identification of causal parameters.     

Testing a block exchangeable covariance matrix

Testing hypotheses about the structure of a covariance matrix for doubly multivariate data is often considered in the literature. In this paper the Rao's score test (RST) is derived to test the block exchangeable covariance matrix or block compound symmetry (BCS) covariance structure under the assumption of multivariate normality. It is shown that the empirical distribution of the RST statistic under the null hypothesis is independent of the true values of the mean and the matrix components of a BCS structure. A significant advantage of the RST is that it can be performed for small samples, even smaller than the dimension of the data, where the likelihood ratio test (LRT) cannot be used, and it outperforms the standard LRT in a number of contexts. Simulation studies are performed for the sample size consideration, and for the estimation of the empirical quantiles of the null distribution of the test statistic. The RST procedure is illustrated on a real data set from the medical studies.     

General purpose multiply robust data integration procedures for handling nonprobability samples

In recent years, there has been an increased interest in combining probability and nonprobability samples. Nonprobability sample are cheaper and quicker to conduct but the resulting estimators are vulnerable to bias as the participation probabilities are unknown. To adjust for the potential bias, estimation procedures based on parametric or nonparametric models have been discussed in the literature. However, the validity of the resulting estimators relies heavily on the validity of the underlying models. Also, nonparametric approaches may suffer from the curse of dimensionality and poor efficiency. We propose a data integration approach by combining multiple outcome regression models and propensity score models. The proposed approach can be used for estimating general parameters including totals, means, distribution functions, and percentiles. The resulting estimators are multiply robust in the sense that they remain consistent if all but one model are misspecified. The asymptotic properties of point and variance estimators are established. The results from a simulation study show the benefits of the proposed method in terms of bias and efficiency. Finally, we apply the proposed method using data from the Korea National Health and Nutrition Examination Survey and data from the National Health Insurance Sharing Services.     

Variance estimation of the risk difference when using propensity-score matching and weighting with time-to-event outcomes

Observational studies are increasingly being used in medicine to estimate the effects of treatments or exposures on outcomes. To minimize the potential for confounding when estimating treatment effects, propensity score methods are frequently implemented. Often outcomes are the time to event. While it is common to report the treatment effect as a relative effect, such as the hazard ratio, reporting the effect using an absolute measure of effect is also important. One commonly used absolute measure of effect is the risk difference or difference in probability of the occurrence of an event within a specified duration of follow-up between a treatment and comparison group. We first describe methods for point and variance estimation of the risk difference when using weighting or matching based on the propensity score when outcomes are time-to-event. Next, we conducted Monte Carlo simulations to compare the relative performance of these methods with respect to bias of the point estimate, accuracy of variance estimates, and coverage of estimated confidence intervals. The results of the simulation generally support the use of weighting methods (untrimmed ATT weights and IPTW) or caliper matching when the prevalence of treatment is low for point estimation. For standard error estimation the simulation results support the use of weighted robust standard errors, bootstrap methods, or matching with a naïve standard error (i.e., Greenwood method). The methods considered in the article are illustrated using a real-world example in which we estimate the effect of discharge prescribing of statins on patients hospitalized for acute myocardial infarction.