PubMed · 14875539
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J JAKOBKIEWICZ. 1951-05-14. [Progress in plague control].. https://pubmed.ncbi.nlm.nih.gov/14875539/
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The same target luminance in different contexts can elicit markedly different perceptions of brightness, a fact that has long puzzled vision scientists. Here we test the proposal that the visual system encodes not luminance as such but rather the statistical relationship of a particular luminance to all possible luminance values experienced in natural contexts during evolution. This statistical conception of vision was validated by using a database of natural scenes in which we could determine the probability distribution functions of co-occurring target and contextual luminance values. The distribution functions obtained in this way predict target brightness in response to a variety of challenging stimuli, thus explaining these otherwise puzzling percepts. That brightness is determined by the statistics of natural light patterns implies that the relevant neural circuitry is specifically organized to generate these probabilistic responses.
Drop-out is a prevalent complication in the analysis of data from longitudinal studies, and remains an active area of research for statisticians and other quantitative methodologists. This tutorial is designed to synthesize and illustrate the broad array of techniques that are used to address outcome-related drop-out, with emphasis on regression-based methods. We begin with a review of important assumptions underlying likelihood-based and semi-parametric models, followed by an overview of models and methods used to draw inferences from incomplete longitudinal data. The majority of the tutorial is devoted to detailed analysis of two studies with substantial rates of drop-out, designed to illustrate the use of effective methods that are relatively easy to apply: in the first example, we use both semi-parametric and fully parametric models to analyse repeated binary responses from a clinical trial of smoking cessation interventions; in the second, pattern mixture models are used to analyse longitudinal CD4 counts from an observational cohort study of HIV-infected women. In each example, we describe exploratory analyses, model formulation, estimation methodology and interpretation of results. Analyses of incomplete data requires making unverifiable assumptions, and these are discussed in detail within the context of each application. Relevant SAS code is provided.
The evaluation of a new medical diagnostic test may focus on two different scientific questions: (1). The new test may replace an existing one because of lower cost or higher validity. A related question would be the selection of the 'best' test(s) from a bundle of new or established measurements. (2). The new test may be used supplementary to other new or established procedures. In a recent publication, Leisenring and co-workers (Stat Med 1997; 16:1263-1281) developed a general marginal regression model for comparisons of diagnostic tests focussing on question (1). i.e. on the selection of the 'best' procedure. They applied the GEE approach of Liang and Zeger (Biometrika 1987; 73:13-22) to adjust for the correlation of data as a nuisance parameter. Using the general framework provided by Leisenring et al., we extend their approach and apply the GEE methodology to question (2). i.e. to the investigation of which of several diagnostic tests should be used supplementary to each other. We analyse data from a longitudinal study concerning pathogenesis, diagnosis and long-term course of the eye disease glaucoma. We find a dependence of the correlation structure of several diagnostic measurements on the severity of the disease. This result may be useful in clinical applications as regards the selection of subsets of diagnostic measurements in individual diagnostic processes but also in investigations concerning the relationship of the pathogenic process and the rationales of the different diagnostic procedures.