Using regression equations built from summary data in the neuropsychological assessment of the individual case.

Abstract

Regression equations have many useful roles in neuropsychological assessment. This article is based on the premise that there is a large reservoir of published data that could be used to build regression equations; these equations could then be used to test a wide variety of hypotheses concerning the functioning of individual cases. This resource is currently underused because (a) not all neuropsychologists are aware that equations can be built with only basic summary data for a sample and (b) the computations involved are tedious and prone to error. To overcome these barriers, the authors set out the steps required to build regression equations from sample summary statistics and the further steps required to compute the associated statistics for drawing inferences concerning an individual case. The authors also develop, describe, and make available computer programs that implement the methods. Although caveats attach to the use of the methods, these need to be balanced against pragmatic considerations and against the alternative of either entirely ignoring a pertinent data set or using it informally to provide a clinical "guesstimate."Keywords: neuropsychological assessment, regression equations, single-case methods Regression equations serve a number of useful functions in the neuropsychological assessment of individuals (Chelune, 2003;Crawford, 2004;Crawford & Howell, 1998;Strauss, Sherman, & Spreen, 2006;Temkin, Heaton, Grant, & Dikmen, 1999). For example, they are widely used to estimate premorbid levels of ability in clinical populations by using psychological tests that are relatively resistant to neurological or psychiatric dysfunction (Crawford, 2004;Franzen, Burgess, & Smith-Seemiller, 1997;O'Carroll, 1995).Another common application of regression is in the assessment of change in neuropsychological functioning in the individual case. Here a regression equation can be built (usually by using healthy participants) to predict a patient's level of performance on a cognitive ability measure at retest from their score at initial testing. An obtained retest score that is markedly lower than the predicted score suggests cognitive deterioration (Crawford, 2004;Heaton & Marcotte, 2000;Sherman et al., 2003;Temkin et al., 1999).Clinical samples can also profitably be used to build regression equations for predicting retest scores. For example, Chelune, Naugle, Lüders, Sedlak, and Awad (1993) built an equation to predict memory scores at retest from baseline scores in a sample of temporal lobe epilepsy cases who had not undergone any surgical intervention in the intervening period. The equation was then used to assess the effects of surgery on memory functioning in further individual patients.Regardless of whether the equation was built from a healthy or clinical sample, this approach simultaneously factors in the strength of correlation between scores at test and retest (the higher the correlation the smaller the expected discrepancies), the effects of practice (typically scores will be higher on retest), and reg...

The Assessment of Change: Serial Assessments in Dementia Evaluations

“…As noted by Crawford and Garthwaite [ 35 ] , not all neuropsychologists are aware that it is possible to construct regression equations for predicting an individual's retest performance from their baseline performance simply using sample summary data, for which there is a potential wealth of clinically useful information available in test manuals and the published literature. To build univariate regression equations from summary data alone, one only needs the means and standard deviations for test and retest scores, the size of the sample, and the test-retest reliability coef fi cient (or alternately the t -value from a pair-samples t test).…”

“…While there is a growing body of such SRB equations for a variety of tests commonly used with older adults [ 8,9,14,17,33,34 ] , and some tests such as the fourth edition of the Wechsler Adult Intelligence and Memory Scales are incorporating RC algorithms into their scoring software [ 10 ] , there is still a paucity of published longitudinal SRB data. Fortunately, as will be seen in the next section, John Crawford and Paul Garthwaite have developed a simple but powerful tool for building regression equations from summary data that can be applied to the individual case [ 35 ] .…”

Développement d'un instrument d'évaluation de l'anomie tenant compte de l'effet de re-test

“…Si l'on désire de plus amples informations, on peut utiliser les programmes RegBuild_ES.exe, RegBuild_t_ES. exe ou encore RegBuild_MR.exe développés par Crawford et son équipe [9,10]. Les trois programmes sont expliqués dans l'annexe.…”

“…Au lieu de constater une amélioration ou une aggravation à l'aide des tableaux 8 et 9, on peut utiliser les programmes RegBuild_ES.exe e t RegBuild_t_ES.exe développés par Crawford et son équipe 4 (ces deux programmes remplacent le programme obsolète RegBuild.exe de Crawford et Garthwaite [9]). …”

Neuropsychological Assessment: Past and Future

“…For example, Crawford and colleagues leveraged existing datasets to develop enriched regression-based predictions of cognitive functioning for the individual case, which may also be applied longitudinally (Crawford & Garthwaite, 2007;Crawford, Garthwaite, Denham, & Chelune, 2012). Such programs that use the multitude of existing datasets to answer ongoing neuropsychological questions are innovative and will help optimize the growth of our field.…”