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Residual Correlation (residual + correlation)
Selected AbstractsSeasonal confounding and residual correlation in analyses of health effects of air pollutionENVIRONMETRICS, Issue 4 2007Isabella R. Ghement Abstract To investigate the health effects of air pollution via a partially linear model, one must choose an appropriate amount of smoothing for accurate estimation of the linear pollution effects. This choice is complicated by the dependencies between the various covariates and by the potential residual correlation. Most existing approaches to making this choice are inadequate, as they neither target accurate estimation of the linear pollutant effects nor handle residual correlation. In this paper, we illustrate two new adaptive and objective methods for determining an appropriate amount of smoothing. We construct valid confidence intervals for the linear pollutant effects, intervals that account for residual correlation. We use our inferential methods to investigate the same-day effects of PM10 on daily mortality in two data sets for the period 1994 to 1996: one collected in Mexico City, an urban area with high levels of air pollution, and the other collected in Vancouver, British Columbia, an urban area with low levels of air pollution. For Mexico City, our methodology does not detect a PM10 effect. In contrast, for Vancouver, a PM10 effect corresponding to an expected 2.4% increase (95% confidence interval ranging from 0.0% to 4.7%) in daily mortality for every 10,µg/m3 increase in PM10 is identified. Copyright © 2006 John Wiley & Sons, Ltd. [source] Tests for genetic association using family dataGENETIC EPIDEMIOLOGY, Issue 2 2002Mei-Chiung Shih Abstract We use likelihood-based score statistics to test for association between a disease and a diallelic polymorphism, based on data from arbitrary types of nuclear families. The Nonfounder statistic extends the transmission disequilibrium test (TDT) to accommodate affected and unaffected offspring, missing parental genotypes, phenotypes more general than qualitative traits, such as censored survival data and quantitative traits, and residual correlation of phenotypes within families. The Founder statistic compares observed or inferred parental genotypes to those expected in the general population. Here the genotypes of affected parents and those with many affected offspring are weighted more heavily than unaffected parents and those with few affected offspring. We illustrate the tests by applying them to data on a polymorphism of the SRD5A2 gene in nuclear families with multiple cases of prostate cancer. We also use simulations to compare the power of these family-based statistics to that of the score statistic based on Cox's partial likelihood for censored survival data, and find that the family-based statistics have considerably more power when there are many untyped parents. The software program FGAP for computing test statistics is available at http://www.stanford.edu/dept/HRP/epidemiology/FGAP. Genet. Epidemiol. 22:128,145, 2002. © 2002 Wiley-Liss, Inc. [source] Power and robustness of a score test for linkage analysis of quantitative traits using identity by descent data on sib pairsGENETIC EPIDEMIOLOGY, Issue 4 2001Darlene R. Goldstein Abstract Identification of genes involved in complex traits by traditional (lod score) linkage analysis is difficult due to many complicating factors. An unfortunate drawback of non-parametric procedures in general, though, is their low power to detect genetic effects. Recently, Dudoit and Speed [2000] proposed using a (likelihood-based) score test for detecting linkage with IBD data on sib pairs. This method uses the likelihood for ,, the recombination fraction between a trait locus and a marker locus, conditional on the phenotypes of the two sibs to test the null hypothesis of no linkage (, = ½). Although a genetic model must be specified, the approach offers several advantages. This paper presents results of simulation studies characterizing the power and robustness properties of this score test for linkage, and compares the power of the test to the Haseman-Elston and modified Haseman-Elston tests. The score test is seen to have impressively high power across a broad range of true and assumed models, particularly under multiple ascertainment. Assuming an additive model with a moderate allele frequency, in the range of p = 0.2 to 0.5, along with heritability H = 0.3 and a moderate residual correlation , = 0.2 resulted in a very good overall performance across a wide range of trait-generating models. Generally, our results indicate that this score test for linkage offers a high degree of protection against wrong assumptions due to its strong robustness when used with the recommended additive model. Genet. Epidemiol. 20:415,431, 2001. © 2001 Wiley-Liss, Inc. [source] Multivariate Association Test Using Haplotype Trend RegressionANNALS OF HUMAN GENETICS, Issue 4 2009Yu-Fang Pei Summary Genetic association analyses with haplotypes may be more powerful than analyses with single markers, under certain conditions. Furthermore, simultaneously considering multiple correlated traits may make use of additional information that would not be considered when analyzing individual traits. In this study, we propose a haplotype based test of association for multivariate quantitative traits in unrelated samples. Specifically, we extend a population based haplotype trend regression (HTR) approach to multivariate scenarios. We mainly focused on bivariate HTR, and the simulation results showed that the proposed method had correct pre-specified type-I error rates. The power of the proposed method was largely influenced by the size and source of correlation between variables, being greatest when correlation of a specific gene was opposite in sign to the residual correlation. [source] Latent Transition Regression for Mixed OutcomesBIOMETRICS, Issue 3 2003Diana L. Miglioretti Summary. Health status is a complex outcome, often characterized by multiple measures. When assessing changes in health status over time, multiple measures are typically collected longitudinally. Analytic challenges posed by these multivariate longitudinal data are further complicated when the outcomes are combinations of continuous, categorical, and count data. To address these challenges, we propose a fully Bayesian latent transition regression approach for jointly analyzing a mixture of longitudinal outcomes from any distribution. Health status is assumed to be a categorical latent variable, and the multiple outcomes are treated as surrogate measures of the latent health state, observed with error. Using this approach, both baseline latent health state prevalences and the probabilities of transitioning between the health states over time are modeled as functions of covariates. The observed outcomes are related to the latent health states through regression models that include subject-specific effects to account for residual correlation among repeated measures over time, and covariate effects to account for differential measurement of the latent health states. We illustrate our approach with data from a longitudinal study of back pain. [source] Isoresistive dynamometer measurement of trunk muscle velocity at different angular phases of flexion and extensionCLINICAL PHYSIOLOGY AND FUNCTIONAL IMAGING, Issue 4 2001J. Surakka Isoresistive trunk muscle dynamometer is a potentially useful piece of equipment in evaluation of trunk muscle velocity, but to date, studies analysing the possibilities and limitations of such measurements are scarce. The aim of this study was to analyse the trunk muscle velocity in repetitive flexion and extension movements at three different angular phases, using an isoresistive trunk muscle dynamometer, and to assess the reliability of the measurements. The study population consisted of 120 healthy, sedentary men and women who volunteered for the study. The measurements were carried out before and after a 22-week training intervention programme. The results show that the peak velocities of the phases between 15 and 35° in flexion and 20,0° in extension (i.e. the second phases) correlated highly (r=0·99 in flexion and in extension) with the peak velocity of the whole movement ranging from ,5 to 55° in flexion and 40 to ,20° in extension. Correlations were high, both before and after the intervention. The LISREL model analysis showed high reliability of measurement for the second angular phases (in flexion and extension). According to the model, the correlation between the first and second measurement (with a 22-week training intervention in between) was 0·78 in flexion and 0·81 in extension. In conclusion, the angular phases from 15 to 35° in flexion and from 20 to 0° in extension represent the peak velocity of the whole movement. Negative residual correlations between the first and last angular phases in the LISREL model reflect the way of performing the movement: the faster the start the slower the end, and vice versa. 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