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Biomarkers of Disease: An Evidence-Based Approach by Andrew K. Trull, Lawrence M. Demers, David W. Holt, Atholl

By Andrew K. Trull, Lawrence M. Demers, David W. Holt, Atholl Johnston, J. Michael Tredger, Christopher P. Price

This new e-book takes a severe, evidence-based examine the efficacy of recent diagnostic assessments that are more and more getting used to guage organ harm and disorder. using biomarkers is starting to be, with a gentle circulation of recent items being introduced out through the pharmaceutical undefined. notwithstanding, in lots of instances the proof which helps using those new equipment in preference to conventional biochemical checks has no longer but been tested, and it's meant that this quantity may also help make clear the strengths and weaknesses of utilizing those biomarkers throughout a variety of purposes and within the quite a few organs of the physique. This method will supply a useful evaluation of the varied functions of biomarkers in medication.

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Extra info for Biomarkers of Disease: An Evidence-Based Approach

Sample text

In addition, it offers the opportunity for better disease management, and has been recognized in many countries by the partial or complete funding by government of products for the home monitoring of diabetes. 1). It has been said that the potential alliance of pressure from manufacturers and patients inevitably leads to too many tests being carried out (in the sense of an ‘investigation carried out’ rather than a ‘procedure used to gather information about a patient to decide what action to take next’) [3].

Apart from sampling error, there may be several reasons why an IE/S is imperfect. Firstly, it is unlikely that a single measurement, possibly made several years before determination of the true CE, can fully represent the disease process. Secondly, the more variable an IE/S measurement is within patients, the less effective will its adjustment be in improving outcome. It is possible to design a study which uses repeated within-patient measures to gauge measurement error accurately. Finally, regression models are unlikely to be perfect representations of the disease process and should be chosen carefully, to reflect realistically both the disease process and all features of the study design, including adjustments for repeated within-patient measurements.

Overestimation of effectiveness (positive bias) of a diagnostic test was shown by a lower confidence interval for the relative diagnostic odds ratio of more than 1. 2. Use of different reference tests, lack of blinding and lack of a description of either the test or the population in which the test was studied led to positive bias. However, the largest factor leading to positive bias was evaluation of a test in a group of patients already known to have the disease and a separate group of normal patients – called a case-control study in the paper [5].

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