Within-subject variability

Analysis of variance (ANOVA) tests whether one group of subjects (e.g., batch, method, laboratory, etc.) differs from the population of subjects investigated (several batches of one product different methods for the same parameter several laboratories participating in a round-robin test to validate a method, for examples see Refs. 5, 9, 21, 30. Multiple measurements are necessary to establish a benchmark variability ( within-group ) typical for the type of subject. Whenever a difference significantly exceeds this benchmark, at least two populations of subjects are involved. A graphical analogue is the Youden plot (see Fig. 2.1). An additive model is assumed for ANOVA. 

Often you want to redefine an already existing variable within a SAS DATA step. As simple as this may sound, it can lead to unexpected results if not done carefully. The following example displays some unexpected behavior that may occur when you redefine a variable within a DATA step. In this example you want to flag the subject who had the Fatal MI adverse event as having died (death =1). 

Variability of data obtained Between-subject differences Within-subject differences... 

In terms of the refinement illustrated here, the function Y is the function Fix], The first point, Xj, is represented by all variable bond lengths, bond angles, conformation angles, chain position parameters, coordinates of the solvent of crystallization, etc., of the initial model. All other points X2,. .., Xj, represent trial values for the same n variables within the desired interval limits and subject to any other constraints, such as coupling of variables or hydrogen bond formation. Clearly, the number and type of variables, and their limits and constraints are easily changed in this procedure, as is the form of the function. The search procedure is also relatively rapid and does not suffer from a slowdown in the vicinity of the minimum, as may occur in steep-est-descent methods. 

Several methods of terminating an MWT trial can be chosen depending on the subject population and the purposes of the test. It is true that such protocol variability can limit comparability of data from study to study. However, variability within a particular subject population can actually be reduced by the judicious selection of MWT protocol parameters so that ceiling effects are minimized and incentives to comply with instructions are optimized. Possible trial termination rules include the following ... 

Because the activity meter evaluates spontaneous behavior, the behavioral baseline is intrinsically variable and subject to many kinds of influence, including lighting, apparatus cleanliness, ambient temperature, noise level, and even time of day. As a consequence, particular care has to be taken to ensure constant experimental conditions to obtain reproducible results. Furthermore, when comparing different drug treatments, it is important to take advantage of the fact that several animals can be evaluated simultaneously, by distributing the different treatments in a balanced fashion over the test period, and even in the positions within the experimental apparatus or of the observation chambers in the experimental room. 

In addition to within-room spatial variability, within-room temporal variability in both air and dust contamination has been the subject of several recent studies. For air, Hazrati and Harrad (2006) reported appreciable (but not always statistically significant) seasonal variation in concentrations of PCBs and tri-hexa-PBDEs in four homes and four offices. The seasonal variability was less pronounced than observed widely for PCBs in outdoor air (e.g. Currado and Harrad, 2000 Hillery et al, 1997). This was attributed to (a) the narrower temperature range indoors and (b) the mitigation of summer peaks in concentration due to... 

Population pharmacokinetic parameters quantify population mean kineticS/ between-subject variability (intersubject variability)/ and residual variability. Residual variability includes within-subject variability/ model misspecification/ and measurement error. This information is necessary to design a dosage regimen for a drug. If all patients were identical/ the same dose would be appropriate for all. However/ since... 

Inherent in any reported laboratory test results on patients are influences of (1) biological variation, (2) inherent analytical error, (3) preanalytical and postanalytical sources of variation, and (4) possible pathophysiological alterations. When repeated measurements are made over time in one individual, even under standardized conditions, there is a considerable variability in the test results. The variability within the individual is attributable to both analytical and intraindividual factors but the intraindividual (within-subject) variability is typically less than the variability among a group of individuals. This means that when ana-... 

Concentrations of urinary resorption markers are usually normalized by dividing by the urinary creatinine concentration. The variability (within- and between-method) of creatinine measurements, within-subject variability in urinary creatinine, and creatinine s dependence on muscle mass contribute to the overall variability of urinary resorption markers. 

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