ReviewDetermining the extent of cognitive change after coronary surgery: a review of statistical procedures
Section snippets
Issues associated with serial cognitive assessment
Most conventional cognitive tests are designed for the identification of brain dysfunction, rather than the assessment of change in brain functions over time [14]. Although such tests are suitable for the investigation of brain-behavior relationships in cognitively impaired individuals, they possess two psychometric properties that restrict their applicability for serial assessment. First, many cognitive tests have limited or nonequivalent alternate forms that may result in performance changes
Statistical techniques for determining the significance of change in cognitive test score
Table 1 lists some common statistical procedures for assessing change in cognitive test score, defines them statistically, and provides a reference where they have been applied to cognitive data. In this section, we discuss those techniques that provide the best correction for sources of measurement error and other systematic influences on test performance.
Examples from published reports
A number of recent studies have investigated the ability of these different statistical techniques to differentiate between “true” changes in cognition and changes attributable to measurement error. These studies aimed to determine the practical ability of these models to predict a follow-up score from a baseline score. For example, Temkin and colleagues [25] compared standard and practice effect–corrected RCIs with linear and multiple regression techniques as predictors of follow-up
Summary and conclusions
As noted above, the statistical techniques described here have arisen partly because of the psychometric limitations of many conventional cognitive and neuropsychological tests (eg, ceiling effects, poor reliability, limited range of possible scores). These techniques may be broken into three broad subtypes according to whether they attempt to account for none, some, or all possible sources of measurement error and the influence of other systematic influences on test performance. The first
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