3 resultados para PROGNOSTIC-SIGNIFICANCE

em Collection Of Biostatistics Research Archive


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Despite a growing awareness that the herbaceous layer serves a special role in maintaining the structure and function of forests, this stratum remainsan underappreciated aspect of forest ecosystems. In this article I review and synthesize information concerning the herb layer’s structure,composition, and dynamics to emphasize its role as an integral component of forest ecosystems. Because species diversity is highest in the herb layeramong all forest strata, forest biodiversity is largely a function of the herb-layer community. Competitive interactions within the herb layer candetermine the initial success of plants occupying higher strata, including the regeneration of dominant overstory tree species. Furthermore, the herblayer and the overstory can become linked through parallel responses to similar environmental gradients. These relationships between strata varyboth spatially and temporally. Because the herb layer responds sensitively to disturbance across broad spatial and temporal scales, its dynamics canprovide important information regarding the site characteristics of forests, including patterns of past land-use practices. Thus, the herb layer has asignificance that belies its diminutive stature.

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A marker that is strongly associated with outcome (or disease) is often assumed to be effective for classifying individuals according to their current or future outcome. However, for this to be true, the associated odds ratio must be of a magnitude rarely seen in epidemiological studies. An illustration of the relationship between odds ratios and receiver operating characteristic (ROC) curves shows, for example, that a marker with an odds ratio as high as 3 is in fact a very poor classification tool. If a marker identifies 10 percent of controls as positive (false positives) and has an odds ratio of 3, then it will only correctly identify 25 percent of cases as positive (true positives). Moreover, the authors illustrate that a single measure of association such as an odds ratio does not meaningfully describe a marker’s ability to classify subjects. Appropriate statistical methods for assessing and reporting the classification power of a marker are described. The serious pitfalls of using more traditional methods based on parameters in logistic regression models are illustrated.