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The intraclass correlation coefficient ρ is used as a measure of the degree of resemblance among family members with respect to some biological or psychological attribute, such as blood pressure level or IQ. Many studies have been done in the area. However, a significant gap in the literature is that few statistics are available to test the significance ( H 0 : ρ = 0 against H A : ρ ≠ 0 ) of this important biological measure and no study is available as to the performance of these statistics with respect to level and power. In this paper we develop three large sample test statistics and compare these, in terms of level and power, using extensive simulation studies of these, the Wald test developed in this paper is found to have a very simple form and highest power while maintaining level well. [ABSTRACT FROM AUTHOR]

The generalized Gamma distribution is widely used in survival analysis, engineering and economics, and it includes the Gamma distribution as a special case. In real life applications, the Generalized Gamma distribution may be an unnecessarily complicated distribution to fit when one of the simpler models may be sufficient to analyze the data. The aim of this paper is to develop tests of goodness of fit of the two parameter gamma distribution against its three parameter counterpart. Specifically, we develop Wald, C (α) , score, likelihood ratio, and gradient tests. These tests are then compared in terms of empirical size and power using a simulation study. [ABSTRACT FROM AUTHOR]

Many problems in biomedical and other sciences are subject to biased estimates (maximum likelihood or of similar types). In two seminal papers Cox and Snell (1968) and Firth (1993) deal with first order bias of maximum likelihood estimates. Cox and Snell obtain a correction term that corrects, approximately, first order bias and Firth uses an adjustment to the score function; the solution of the estimating equation obtained by solving the adjusted score function to zero, removes the first order bias of the maximum likelihood estimates approximately. In many applications authors use one of these two procedures for bias correction without being aware that the other exists or whether these two procedures are equivalent. In this paper we investigate the equivalence issue of the two methods through theoretical analysis, simulation study and data analysis. We show that the two methods yield either exactly the same estimates or that the preventive method has some edge over the other. [ABSTRACT FROM AUTHOR]

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 problemwe 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. [ABSTRACT FROM AUTHOR]

Two samples count data with unequal over-dispersions arise in many applied statistics problems (biostatistics, epidemiology, etc.). In this situation, it is of interest to test the equality of the means. The traditional Behrens–Fisher problem is to test the equality of the means μ1and μ2of two normal populations where the variancesandare unknown. The purpose of this paper is to deal with the corresponding problem for over-dispersed count data. We develop six test procedures, namely, a likelihood ratio test LR, a likelihood ratio test based on the bias corrected maximum likelihood estimates of the nuisance parameters LR(bc), a score testT2, a score test based on the bias corrected estimates of the nuisance parametersT2(bc), aC(α) test based on the method of moments estimates of the nuisance parametersT1with Welch's [The significance of the difference between two means when the population variances are unequal. Biometrika. 1937;29:350–362] degree of freedom correction, and a testTNusing the asymptotic normal distribution ofT1. These procedures are then compared in terms of size and power using simulations. Simulations show the best overall performance of the statisticT1in terms of size and power and it is easy to calculate. For large sample sizes, for example, forn1=n2=50, all six statistics do well in terms of level and their power performances are also similar. So, for large sample sizes, the statisticTNshould be used as it is very easy to use in practice. [ABSTRACT FROM PUBLISHER]

Longitudinal or clustered response data arise in many applications such as biostatistics, epidemiology and environmental studies. The repeated responses cannot in general be assumed to be independent. One method of analysing such data is by using the generalized estimating equations (GEE) approach. The current GEE method for estimating regression effects in longitudinal data focuses on the modelling of the working correlation matrix assuming a known variance function. However, correct choice of the correlation structure may not necessarily improve estimation efficiency for the regression parameters if the variance function is misspecified [Wang YG, Lin X. Effects of variance-function misspecification in analysis of longitudinal data. Biometrics. 2005;61:413–421]. In this connection two problems arise: finding a correct variance function and estimating the parameters of the chosen variance function. In this paper, we study the problem of estimating the parameters of the variance function assuming that the form of the variance function is known and then the effect of a misspecified variance function on the estimates of the regression parameters. We propose a GEE approach to estimate the parameters of the variance function. This estimation approach borrows the idea of Davidian and Carroll [Variance function estimation. J Amer Statist Assoc. 1987;82:1079–1091] by solving a nonlinear regression problem where residuals are regarded as the responses and the variance function is regarded as the regression function. A limited simulation study shows that the proposed method performs at least as well as the modified pseudo-likelihood approach developed by Wang and Zhao [A modified pseudolikelihood approach for analysis of longitudinal data. Biometrics. 2007;63:681–689]. Both these methods perform better than the GEE approach. [ABSTRACT FROM AUTHOR]

Inference concerning the negative binomial dispersion parameter, denoted by c, is important in many biological and biomedical investigations. Properties of the maximum-likelihood estimator of c and its bias-corrected version have been studied extensively, mainly, in terms of bias and efficiency [W.W. Piegorsch, Maximum likelihood estimation for the negative binomial dispersion parameter, Biometrics 46 (1990), pp. 863–867; S.J. Clark and J.N. Perry, Estimation of the negative binomial parameter κ by maximum quasi-likelihood, Biometrics 45 (1989), pp. 309–316; K.K. Saha and S.R. Paul, Bias corrected maximum likelihood estimator of the negative binomial dispersion parameter, Biometrics 61 (2005), pp. 179–185]. However, not much work has been done on the construction of confidence intervals (C.I.s) for c. The purpose of this paper is to study the behaviour of some C.I. procedures for c. We study, by simulations, three Wald type C.I. procedures based on the asymptotic distribution of the method of moments estimate (mme), the maximum-likelihood estimate (mle) and the bias-corrected mle (bcmle) [K.K. Saha and S.R. Paul, Bias corrected maximum likelihood estimator of the negative binomial dispersion parameter, Biometrics 61 (2005), pp. 179–185] of c. All three methods show serious under-coverage. We further study parametric bootstrap procedures based on these estimates of c, which significantly improve the coverage probabilities. The bootstrap C.I.s based on the mle (Boot-MLE method) and the bcmle (Boot-BCM method) have coverages that are significantly better (empirical coverage close to the nominal coverage) than the corresponding bootstrap C.I. based on the mme, especially for small sample size and highly over-dispersed data. However, simulation results on lengths of the C.I.s show evidence that all three bootstrap procedures have larger average coverage lengths. Therefore, for practical data analysis, the bootstrap C.I. Boot-MLE or Boot-BCM should be used, although Boot-MLE method seems to be preferable over the Boot-BCM method in terms of both coverage and length. Furthermore, Boot-MLE needs less computation than Boot-BCM. [ABSTRACT FROM AUTHOR]

The importance of the normal distribution for fitting continuous data is well known. However, in many practical situations data distribution departs from normality. For example, the sample skewness and the sample kurtosis are far away from 0 and 3, respectively, which are nice properties of normal distributions. So, it is important to have formal tests of normality against any alternative. D'Agostino et al. [A suggestion for using powerful and informative tests of normality, Am. Statist. 44 (1990), pp. 316-321] review four procedures Z2(g1), Z2(g2), D and K2 for testing departure from normality. The first two of these procedures are tests of normality against departure due to skewness and kurtosis, respectively. The other two tests are omnibus tests. An alternative to the normal distribution is a class of skew-normal distributions (see [A. Azzalini, A class of distributions which includes the normal ones, Scand. J. Statist. 12 (1985), pp. 171-178]). In this paper, we obtain a score test (W) and a likelihood ratio test (LR) of goodness of fit of the normal regression model against the skew-normal family of regression models. It turns out that the score test is based on the sample skewness and is of very simple form. The performance of these six procedures, in terms of size and power, are compared using simulations. The level properties of the three statistics LR, W and Z2(g1) are similar and close to the nominal level for moderate to large sample sizes. Also, their power properties are similar for small departure from normality due to skewness (γ1≤0.4). Of these, the score test statistic has a very simple form and computationally much simpler than the other two statistics. The LR statistic, in general, has highest power, although it is computationally much complex as it requires estimates of the parameters under the normal model as well as those under the skew-normal model. So, the score test may be used to test for normality against small departure from normality due to skewness. Otherwise, the likelihood ratio statistic LR should be used as it detects general departure from normality (due to both skewness and kurtosis) with, in general, largest power. [ABSTRACT FROM AUTHOR]

The authors reflect on the potential for a covalent human immunodeficiency virus (HIV) vaccine. They stress the importance of a vaccine to prevent the HIV pandemic. They cite that the importance of defining preclinical tests to predict the effectiveness of covalent vaccination is highlighted by failures of candidate HIV vaccines. They add that animal HIV models are not fully predictive and some monkeys demonstrate susceptibility to infection with engineered versions of the HIV ancestor.

Rationale : The neuropeptide vasoactive intestinal peptide (VIP) is one of the physiologic mediators of non-adrenergic, non-cholinergic smooth muscle relaxation of the airway and an important modulator of innate and adaptive immune responses. VIP catalytic autoantibodies are increased in asthma and serum VIP level is decreased during acute exacerbation of asthma. The effect of pregnancy on asthma is variable and depends in part on the severity of pre-existing asthma, along with other physiological and pathophysiological changes. We hypothesized that hydrolysis of VIP by circulating catalytic VIP antibodies will be increased in pregnancy in patients with asthma. Study objective : To determine the level of catalytic autoantibodies to VIP in pregnant asthmatics compared to non-pregnant asthmatics and control pregnant women without asthma. Methods: We prospectively enrolled eight pregnant asthmatics (age, 26.5 ± 2.6 years; mean ± SEM), nine pregnant women without asthma (32.0 ± 3.0 years), seven non-pregnant women with asthma (25.0 ±1.9 years), and seven non-pregnant women without asthma (34.4 ± 2 years) into the study. VIP hydrolysis was performed in all subjects. Results: Immunoglobulin G (IgG) autoantibodies that catalyze the hydrolysis of vasoactive intestinal peptide (VIP) were present at greater levels in the blood of pregnant women with asthma (7.6 ± 1.1 pM VIP/6 h) compared to pregnant women without asthma (4.0 ± 0.5; p <: 0.001), non-pregnant asthmatics (4.9 ± 0.9; p < 0.05) or non-pregnant women without asthma (1.9 ± 0.7; p < 0.05). Conclusion: An increase in the VIP hydrolyzing activity of IgG is independently associated with asthma and pregnancy. The autoantibodies hold the potential of affecting the pathophysiology of the airways in pregnant asthmatics. [ABSTRACT FROM AUTHOR]

This paper is concerned with properties (bias, standard deviation, mean square error and efficiency) of twenty six estimators of the intraclass correlation in the analysis of binary data. Our main interest is to study these properties when data are generated from different distributions. For data generation we considered three over-dispersed binomial distributions, namely, the beta-binomial distribution, the probit normal binomial distribution and a mixture of two binomial distributions. The findings regarding bias, standard deviation and mean squared error of all these estimators, are that (a) in general, the distributions of biases of most of the estimators are negatively skewed. The biases are smallest when data are generated from the beta-binomial distribution and largest when data are generated from the mixture distribution; (b) the standard deviations are smallest when data are generated from the beta-binomial distribution; and (c) the mean squared errors are smallest when data are generated from the beta-binomial distribution and largest when data are generated from the mixture distribution. Of the 26, nine estimators including the maximum likelihood estimator, an estimator based on the optimal quadratic estimating equations of Crowder (1987), and an analysis of variance type estimator is found to have least amount of bias, standard deviation and mean squared error. Also, the distributions of the bias, standard deviation and mean squared error for each of these estimators are, in general, more symmetric than those of the other estimators. Our findings regarding efficiency are that the estimator based on the optimal quadratic estimating equations has consistently high efficiency and least variability in the efficiency results. In the important range in which the intraclass correlation is small (≤0 5), on the average, this estimator shows best efficiency performance. The analysis of variance type estimator seems to do well for larger values of the... [ABSTRACT FROM AUTHOR]