By J. Philip Miller
Crucial Statistical tools for scientific records offers purely key contributions that have been chosen from the amount within the guide of statistics: clinical records, quantity 27 (2009). whereas using facts in those fields has a protracted and wealthy background, the explosive development of technological know-how in most cases, and of scientific and epidemiological sciences specifically, has resulted in the advance of latest equipment and cutting edge variations of normal tools. This quantity is safely centred for people operating in those fields. participants are the world over popular specialists of their r. Read more...
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A reliable biomarker must exhibit adequate levels of both types of reliability. The reliability of a biomarker must be established before validity can be examined; if the biomarker cannot be assumed to provide an equivalent result upon repeated determinations on the same biological material, it will not be useful for practical application. The validity of a biomarker is deﬁned to be the extent to which it measures what it is intended to measure. For example, Qiao et al. (1997) proposed that the expression of a tumor-associated antigen by exfoliated sputum epithelial cells could be used as a biomarker in the detection of preclinical, localized lung cancer.
L. Hagan identifying an appropriate transformation for biomarker data that appear to violate a distributional assumption are discussed in the following section. 4. , 1990). However, authors usually provide no justiﬁcation for such a transformation other than that it is commonly used in analyzing the type of data collected in the study. 2 above. If one concludes that the log-transformed data are not normally distributed, then there are many other possible transformations that one could try. Several families of possible transformations have been proposed, including the Box–Cox family (Box and Cox, 1964), the Tukey ‘‘ladder of powers’’ (Tukey, 1977, pp.
L. Hagan pp. 49–51) refers to as ‘‘inter-rater bias’’). Let n denote the number of biological specimens under study, and let k denote the number of biomarkers being compared. Let yij denote the determination (usually ‘‘positive’’ or ‘‘negative’’) based on the jth biomarker for the ith specimen, where yij ¼ 1 for ‘‘positive’’ and yij ¼ 0 for ‘‘negative,’’ and let yi ¼ k X yij j¼1 denote the number of positive ratings on the ith specimen. Fleiss (1971) generalized Cohen’s kappa to a new measure, k^ f as follows: k^ f ¼ p0 À p^e , 1 À p^e where p0 ¼ 1 À n 2X yi ðk À yi Þ , n i¼1 kðk À 1Þ ^ À pÞ, ^ p^e ¼ 1 À 2pð1 and n P yi p^ ¼ i¼1 .
Essential Statistical Methods for Medical Statistics by J. Philip Miller