Testing homogeneity of difference of two proportions for stratified correlated paired binary data

In ophthalmologic or otolaryngologic study, each subject may contribute paired organs measurements to the analysis. A number of statistical methods have been proposed on bilateral correlated data. In practice, it is important to detect confounding effect by treatment interaction, since ignoring confounding effect may lead to unreliable conclusion. Therefore, stratified data analysis can be considered to adjust the effect of confounder on statistical inference. In this article, we investigate and derive three test procedures for testing homogeneity of difference of two proportions for stratified correlated paired binary data in the basis of equal correlation model assumption. The performance of proposed test procedures is examined through Monte Carlo simulation. The simulation results show that the Score test is usually robust on type I error control with high power, and therefore is recommended among the three methods. One example from otolaryngologic study is given to illustrate the three test procedures.     

Testing for serial correlation in linear model with validation data

In this paper, we investigate the testing for serial correlation in a linear model with validation data, then we apply the empirical likelihood method to construct the test statistic and derive the asymptotic distribution of the test statistic under null hypothesis. Simulation results show that our method performs well both in size and power with finite same size.     

Resampling method to estimate intra-cluster correlation for clustered binary data

ABSTRACT

Various methods have been proposed to estimate intra-cluster correlation coefficients (ICCs) for correlated binary data, and many are very sensitive to the type of design and underlying distributional assumptions. We proposed a new method to estimate ICC and its 95% confidence intervals based on resampling principles and U-statistics, where we resampled with replacement pairs of individuals from within and between clusters. We concluded from our simulation study that the resampling-based estimates approximate the population ICC more precisely than the analysis of variance and method of moments techniques for different event rates, varying number of clusters, and cluster sizes.     

Testing equality of two normal covariance matrices with monotone missing data

Abstract

The problem of testing equality of two multivariate normal covariance matrices is considered. Assuming that the incomplete data are of monotone pattern, a quantity similar to the Likelihood Ratio Test Statistic is proposed. A satisfactory approximation to the distribution of the quantity is derived. Hypothesis testing based on the approximate distribution is outlined. The merits of the test are investigated using Monte Carlo simulation. Monte Carlo studies indicate that the test is very satisfactory even for moderately small samples. The proposed methods are illustrated using an example.     

Testing the homogeneity of proportions for clustered binary data without knowing the correlation structure

A robust generalized score test for comparing groups of cluster binary data is proposed. This novel test is asymptotically valid for practically any underlying correlation configurations including the situation when correlation coefficients vary within or between clusters. This structure generally undermines the validity of the typical large sample properties of the method of maximum likelihood. Simulations and real data analysis are used to demonstrate the merit of this parametric robust method. Results show that our test is superior to two recently proposed test statistics advocated by other researchers.     

Testing serial correlation in partially linear models with validation data

This article investigates the testing for serial correlation in partially linear models with validation data and applies the empirical likelihood methods to construct serial tests statistics, and then we derive the asymptotic distribution of the test statistics under null hypothesis. Simulation results show that our method performs well.     

A new generalized p-value approach for testing equality of coefficients of variation in k normal populations

A new generalized p-value method is proposed for testing the equality of coefficients of variation in k normal populations. Simulation studies show that the type I error probabilities are close to the nominal level. The proposed test is also compared with likelihood ratio test, modified Bennett's test and score test through Monte Carlo simulation, the results demonstrate that the generalized p-value method has satisfactory performance in terms of sizes and powers.     

Exact methods for testing homogeneity of proportions for multiple groups of paired binary data

We consider four exact procedures to test the homogeneity of proportions for correlated multiple clustered data. Exact procedures are compared with the asymptotic approach based on the score statistic. We use a real example from a double-blind clinical trial studying the treatment of otitis media to illustrate the various test procedures and provide extensive numerical studies to compare procedures with regards to Type I error rates and powers under the unconditional framework. The exact unconditional procedure based on estimation followed by maximization is generally more powerful than other procedures.     

Accurate tests for the equality of coefficients of variation

ABSTRACT

A frequently encountered statistical problem is to determine if the variability among k populations is heterogeneous. If the populations are measured using different scales, comparing variances may not be appropriate. In this case, comparing coefficient of variation (CV) can be used because CV is unitless. In this paper, a non-parametric test is introduced to test whether the CVs from k populations are different. With the assumption that the populations are independent normally distributed, the Miller test, Feltz and Miller test, saddlepoint-based test, log likelihood ratio test and the proposed simulated Bartlett-corrected log likelihood ratio test are derived. Simulation results show the extreme accuracy of the simulated Bartlett-corrected log likelihood ratio test if the model is correctly specified. If the model is mis-specified and the sample size is small, the proposed test still gives good results. However, with a mis-specified model and large sample size, the non-parametric test is recommended.     

Testing equality of correlation coefficients in two populations via permutation methods

The present paper investigates the asymptotic behaviour of a studentized permutation test for testing equality of (Pearson) correlation coefficients in two populations. It is shown that this test is asymptotically of exact level and has the same power for contiguous alternatives as the corresponding asymptotic test. As a by-product we specify the assumptions needed for the validity of the permutation test suggested in Sakaori (2002). A small simulation study compares the finite sample properties of the considered tests.     

Test for the equality of several correlation coefficients

We derive two C(α) statistics and the likelihood-ratio statistic for testing the equality of several correlation coefficients, from k ≥ 2 independent random samples from bivariate normal populations. The asymptotic relationship of the C(α) tests, the likelihood-ratio test, and a statistic based on the normality assumption of Fisher's Z-transform of the sample correlation coefficient is established. A comparative performance study, in terms of size and power, is then conducted by Monte Carlo simulations. The likelihood-ratio statistic is often too liberal, and the statistic based on Fisher's Z-transform is conservative. The performance of the two C(α) statistics is identical. They maintain significance level well and have almost the same power as the other statistics when empirically calculated critical values of the same size are used. The C(α) statistic based on a noniterative estimate of the common correlation coefficient (based on Fisher's Z-transform) is recommended.     

Tests for equality of variances with paired data

The normal theory test for equality of variances with paired data is shown to be nonrobust to violation of the assumption of normality. Nonparametric tests are shown to provide a much safer alternative with little loss of efficiency.     

Conservative confidence intervals for the intraclass correlation coefficient for clustered binary data

Asymptotic approaches are traditionally used to calculate confidence intervals for intraclass correlation coefficient in a clustered binary study. When sample size is small to medium, or correlation or response rate is near the boundary, asymptotic intervals often do not have satisfactory performance with regard to coverage. We propose using the importance sampling method to construct the profile confidence limits for the intraclass correlation coefficient. Importance sampling is a simulation based approach to reduce the variance of the estimated parameter. Four existing asymptotic limits are used as statistical quantities for sample space ordering in the importance sampling method. Simulation studies are performed to evaluate the performance of the proposed accurate intervals with regard to coverage and interval width. Simulation results indicate that the accurate intervals based on the asymptotic limits by Fleiss and Cuzick generally have shorter width than others in many cases, while the accurate intervals based on Zou and Donner asymptotic limits outperform others when correlation and response rate are close to their boundaries.     

Using a correlated probit model approximation to estimate the variance for binary matched pairs

A correlated probit model approximation for conditional probabilities (Mendell and Elston 1974) is used to estimate the variance for binary matched pairs data by maximum likelihood. Using asymptotic data, the bias of the estimates is shown to be small for a wide range of intra-class correlations and incidences. This approximation is also compared with other recently published, or implemented, improved approximations. For the small sample examples presented, it shows a substantial advantage over other approximations. The method is extended to allow covariates for each observation, and fitting by iteratively reweighted least squares.     

Testing equality of two beta binomial proportions in the presence of unequal extra-dispersion parameters

Data in the form of proportions with extra-dispersion (over/under) arise in many biomedical, epidemiological, and toxicological applications. In some situations, two samples of data in the form of proportions with extra-dispersion arise in which the problem is to test the equality of the proportions in the two groups with unspecified and possibly unequal extra-dispersion parameters. This problem is analogous to the traditional Behrens-Fisher problem in which two normal population means with possibly unequal variances are compared. To deal with this problem we develop eight tests and compare them in terms of empirical size and power, using a simulation study. Simulations show that a C(α) test based on extended quasi-likelihood estimates of the nuisance parameters holds nominal level most effectively (close to the nominal level) and it is at least as powerful as any other statistic that is not liberal. It has the simplest formula, is based on estimates of the nuisance parameters only under the null hypothesis, and is easiest to calculate. Also, it is robust in the sense that no distributional assumption is required to develop this statistic.     

Effects of correlation in modeling clustered binary data

A simulation study is conducted to determine the effects of varying correlation structures on two estimation procedures used to model clustered binary data; a parametric model, the beta-binomial, and a non-parametric model, the exchangeable binary. The simulations detected bias in estimation of the mean response parameter and the correlation parameter when assuming a parametric model. In addition it was found that variance parameters can be severely underestimated if the correlation structure is considered strictly a nuisance parameter.     

Joint estimation of disease-specific sensitivities and specificities in reader-based multi-disease diagnostic studies of paired organs

Binocular data typically arise in ophthalmology where pairs of eyes are evaluated, through some diagnostic procedure, for the presence of certain diseases or pathologies. Treating eyes as independent and adopting the usual approach in estimating the sensitivity and specificity of a diagnostic test ignores the correlation between fellow eyes. This may consequently yield incorrect estimates, especially of the standard errors. The paper is concerned with diagnostic studies wherein several diagnostic tests, or the same test read by several readers, are administered to identify one or more diseases. A likelihood-based method of estimating disease-specific sensitivities and specificities via hierarchical generalized linear mixed models is proposed to meaningfully delineate the various correlations in the data. The efficiency of the estimates is assessed in a simulation study. Data from a study on diabetic retinopathy are analyzed to illustrate the methodology.     

Comparison of correlated proportions based on paired binary data from clustered samples

In this article, we explore hypothesis testing problems related to correlated proportions from clustered matched-pair binary data. Null hypotheses of equality in proportions, homogeneity, and non-inferiority of one to another are similar testing problems of linear contrasts of correlated proportions with suitable transformation. The covariance estimators of the test statistics are based on moment estimation under the null hypotheses. We present a general framework for testing linear contrasts of the correlated proportions from clustered matched-pair data based upon a class of unbiased estimators of the proportions. The corresponding testing procedures do not impose structure assumptions on the correlation matrix and are easy to use. Simulation results suggest that the proposed method is more likely to maintain the proper significance level and to improve power than the test proposed by Obuchowski.     

A multi sample test for the equality of coefficients of variation in normal populations

Two tests are derived for the hypothesis that the coefficients of variation of k normal populations are equal. The k samples may be of unequal size. The first test is the likelihood ratio test with the usual X²-approximation. A simulation study shows that the small sample behaviour under the null hypothesis is unsatisfactory. An alternative test, based on the sample coefficients of variation, appears to have somewhat better properties.     

A simulation study of a test for the equality of the coefficients of variation

Simulation study results are given for the size and power of a test for the equality of the coefficients of variation from r normal populations. Independent samples of equal and unequal size from the normal and three other distributions were used. The size and power of the test compare favorably to two tests developed by Doornbos and Dijkstra and the test statistic is simpler to compute.