02677nas a2200409 4500008004100000022001400041245014500055210006900200260001600269300001000285490000800295520154400303653001001847653000901857653002501866653002801891653001601919653001701935653001101952653001701963653001501980653001101995653001702006653000902023653001602032653002402048653002602072653001702098100001902115700001502134700001702149700001802166700001602184700001602200700001502216856003602231 1999 eng d a0002-926200aAnalytical and biologic variability in measures of hemostasis, fibrinolysis, and inflammation: assessment and implications for epidemiology.0 aAnalytical and biologic variability in measures of hemostasis fi c1999 Feb 01 a261-70 v1493 a
An increasing number of cardiovascular epidemiologic studies are measuring non-traditional risk markers of disease, most of which do not have established biovariability characteristics. When biovariability data have been reported, they usually represent a short time period, and, in any case, there is little consensus on how the information should be used. The authors performed a long-term (6-month) repeated measures study on 26 healthy individuals, and, using a nested analysis of variance (ANOVA) approach, report on the analytical (CVA), intraindividual (CVI), and between individual (CVG) variability of 12 procoagulant, fibrinolysis, and inflammation assays, including total cholesterol for comparison. The results suggest acceptable analytical variability (CVA < or = 1/2 CVI) for all assays. However, there was a large range of intraindividual variation as a proportion of total variance (2-78%), and adjusting for intraindividual and between individual variation in bivariate correlations increased the observed correlation by more than 30 percent for three of these assays. Overall, the assays showed a significant increase in intraindividual variation over 6 months (p < 0.05). While these findings suggest that most of these assays have biovariability characteristics similar to cholesterol, there is variation among assays. Some assays may be better suited to epidemiologic studies, and knowledge of an assay's biovariability data may be useful in interpreting simple statistics, and in designing multivariate models.
10aAdult10aAged10aAnalysis of Variance10aCardiovascular Diseases10aCholesterol10aEpidemiology10aFemale10aFibrinolysis10aHemostasis10aHumans10aInflammation10aMale10aMiddle Aged10aModels, Statistical10aMultivariate Analysis10aRisk Factors1 aSakkinen, P, A1 aMacy, E, M1 aCallas, P, W1 aCornell, E, S1 aHayes, T, E1 aKuller, L H1 aTracy, R P uhttps://chs-nhlbi.org/node/1522