49,401 research outputs found
An Overview of Methods in the Analysis of Dependent ordered catagorical Data: Assumptions and Implications
Subjective assessments of pain, quality of life, ability etc. measured by rating scales and questionnaires are common in clinical research. The resulting responses are categorical with an ordered structure and the statistical methods must take account of this type of data structure. In this paper we give an overview of methods for analysis of dependent ordered categorical data and a comparison of standard models and measures with nonparametric augmented rank measures proposed by Svensson. We focus on assumptions and issues behind model specifications and data as well as implications of the methods. First we summarise some fundamental models for categorical data and two main approaches for repeated ordinal data; marginal and cluster-specific models. We then describe models and measures for application in agreement studies and finally give a summary of the approach of Svensson. The paper concludes with a summary of important aspects.Dependent ordinal data; GEE; GLMM; Logit; modelling
Detecting adaptive evolution in phylogenetic comparative analysis using the Ornstein-Uhlenbeck model
Phylogenetic comparative analysis is an approach to inferring evolutionary
process from a combination of phylogenetic and phenotypic data. The last few
years have seen increasingly sophisticated models employed in the evaluation of
more and more detailed evolutionary hypotheses, including adaptive hypotheses
with multiple selective optima and hypotheses with rate variation within and
across lineages. The statistical performance of these sophisticated models has
received relatively little systematic attention, however. We conducted an
extensive simulation study to quantify the statistical properties of a class of
models toward the simpler end of the spectrum that model phenotypic evolution
using Ornstein-Uhlenbeck processes. We focused on identifying where, how, and
why these methods break down so that users can apply them with greater
understanding of their strengths and weaknesses. Our analysis identifies three
key determinants of performance: a discriminability ratio, a signal-to-noise
ratio, and the number of taxa sampled. Interestingly, we find that
model-selection power can be high even in regions that were previously thought
to be difficult, such as when tree size is small. On the other hand, we find
that model parameters are in many circumstances difficult to estimate
accurately, indicating a relative paucity of information in the data relative
to these parameters. Nevertheless, we note that accurate model selection is
often possible when parameters are only weakly identified. Our results have
implications for more sophisticated methods inasmuch as the latter are
generalizations of the case we study.Comment: 38 pages, in press at Systematic Biolog
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