Home About us Contact
|
Home About us Contact | ||
|
|
||
Variance Structure (variance + structure)
Selected AbstractsAssessing the trade-offs between crossover and parallel group designs in sleep researchJOURNAL OF SLEEP RESEARCH, Issue 4 2006CHARLES C. BERRY Summary Sleep researchers invariably struggle with decisions regarding the optimal design for their studies. Whether such studies involve treatment for insomnia, obstructive sleep apnea, or any other sleep disorder, questions arise regarding the respective trade-offs between a parallel group and a crossover design. This study analyzed the variance structure of commonly measured polysomnographic variables in an effort to describe the statistical impact of these alternate designs. The study examined the effects of opioids on sleep and employed multiple crossovers between placebo, MS-contin, and methadone using a double-blind, randomized crossover design. Thirty-seven healthy subjects were studied. Four of the subjects were unable to complete the protocol for a variety of reasons, and polysomnogram data was unavailable for one subject. Data from 37 subjects provide the basis for this analysis. Despite dropouts, the crossover study was approximately four times as efficient as the parallel group design in terms of being able to recognize differences in deep sleep across these conditions. Other polysomnographic variables also favored the crossover design to varying extents. Despite the operational complexity of a crossover design, the statistical efficiency of this approach makes it a preferable approach for designing intervention studies in sleep research. [source] Testing for Genetic Association in the Presence of Linkage and Gene,Covariate InteractionsBIOMETRICAL JOURNAL, Issue 1 2010Andrea Callegaro Abstract In order to study family-based association in the presence of linkage, we extend a generalized linear mixed model proposed for genetic linkage analysis (Lebrec and van Houwelingen (2007), Human Heredity64, 5,15) by adding a genotypic effect to the mean. The corresponding score test is a weighted family-based association tests statistic, where the weight depends on the linkage effect and on other genetic and shared environmental effects. For testing of genetic association in the presence of gene,covariate interaction, we propose a linear regression method where the family-specific score statistic is regressed on family-specific covariates. Both statistics are straightforward to compute. Simulation results show that adjusting the weight for the within-family variance structure may be a powerful approach in the presence of environmental effects. The test statistic for genetic association in the presence of gene,covariate interaction improved the power for detecting association. For illustration, we analyze the rheumatoid arthritis data from GAW15. Adjusting for smoking and anti-cyclic citrullinated peptide increased the significance of the association with the DR locus. [source] Construction of Resolvable Spatial Row,Column DesignsBIOMETRICS, Issue 1 2006E. R. Williams Summary Resolvable row,column designs are widely used in field trials to control variation and improve the precision of treatment comparisons. Further gains can often be made by using a spatial model or a combination of spatial and incomplete blocking components. Martin, Eccleston, and Gleeson (1993, Journal of Statistical Planning and Inference34, 433,450) presented some general principles for the construction of robust spatial block designs which were addressed by spatial designs based on the linear variance (LV) model. In this article we define the two-dimensional form of the LV model and investigate extensions of the Martin et al. principles for the construction of resolvable spatial row,column designs. The computer construction of efficient spatial designs is discussed and some comparisons made with designs constructed assuming an autoregressive variance structure. [source] Variances Are Not Always Nuisance ParametersBIOMETRICS, Issue 2 2003Raymond J. Carroll Summary In classical problems, e.g., comparing two populations, fitting a regression surface, etc., variability is a nuisance parameter. The term "nuisance parameter" is meant here in both the technical and the practical sense. However, there are many instances where understanding the structure of variability is just as central as understanding the mean structure. The purpose of this article is to review a few of these problems. I focus in particular on two issues: (a) the determination of the validity of an assay; and (b) the issue of the power for detecting health effects from nutrient intakes when the latter are measured by food frequency questionnaires. I will also briefly mention the problems of variance structure in generalized linear mixed models, robust parameter design in quality technology, and the signal in microarrays. In these and other problems, treating variance structure as a nuisance instead of a central part of the modeling effort not only leads to inefficient estimation of means, but also to misleading conclusions. [source] Personality Over Time: Methodological Approaches to the Study of Short-Term and Long-Term Development and ChangeJOURNAL OF PERSONALITY, Issue 6 2003Jeremy C. Biesanz We consider a variety of recent methods of longitudinal data analysis to examine both short-term and long-term development and change in personality, including mean-level analyses both across and within individuals across time, variance structures across time, and cycles and dynamic models across time. These different longitudinal analyses can address classic as well as new questions in the study of personality and its development. We discuss the linkages among different longitudinal analyses, measurement issues in temporal data, the spacing of assessments, and the levels of generalization and potential insights afforded by different longitudinal analyses. [source] A quantitative genetic analysis of intermediate asthma phenotypesALLERGY, Issue 3 2009S. F. Thomsen Aim:, To study the relative contribution of genetic and environmental factors to the correlation between exhaled nitric oxide (FeNO), airway responsiveness, airway obstruction, and serum total immunoglobulin E (IgE). Methods:, Within a sampling frame of 21 162 twin subjects, 20,49 years of age, from the Danish Twin Registry, a total of 575 subjects (256 intact pairs and 63 single twins) who either themselves and/or their co-twins reported a history of asthma at a nationwide questionnaire survey, were clinically examined. Traits were measured using standard techniques. Latent factor models were fitted to the observed data using maximum likelihood methods. Results:, Additive genetic factors explained 67% of the variation in FeNO, 43% in airway responsiveness, 22% in airway obstruction, and 81% in serum total IgE. In general, traits had genetically and environmentally distinct variance structures. The most substantial genetic similarity was observed between FeNO and serum total IgE, genetic correlation (,A) = 0.37, whereas the strongest environmental resemblance was observed between airway responsiveness and airway obstruction, specific environmental correlation (,E) = ,0.46, and between FeNO and airway responsiveness, ,E = 0.34. Conclusions:, Asthma is a complex disease characterized by a set of etiologically heterogeneous biomarkers, which likely constitute diverse targets of intervention. [source] | |||||||||||||