Regression Equations for Predicting Premorbid Performance on Executive Test Measures by Persons with Traumatic Brain Injuries

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Regression equations, using as predictors demographic variables and scores on tests resistant to the effects of brain injury, are often used to calculate predicted test scores on tests used in neuropsychological assessment. The discrepancy between a predicted and an obtained score can be used in the diagnosis of cognitive deficits resulting from brain damage. The aim of the present study was to develop regression equations that would allow the prediction of scores on tests sensitive to executive deficits and to apply these to data from persons with brain injuries. A total of 100 persons from the community were tested and the resultant equations were used to predict the scores of 40 persons with traumatic brain injuries. For those equations with multiple correlations in the order of .50, the numbers of persons classified using either the equations or the published normative tables were comparable. Procedures for determining the abnormality of an individual's test score discrepancy described by Crawford and Howell (1998), based on the output from multiple regression procedures, are discussed. Where normative data from a local sample are available, use of regression equations to determine the abnormality of a discrepancy can provide a useful means of validating conclusions based on the application of US or UK norms.

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In clinical practice, neuro-psychologists often need to determine whether a test score obtained from a person with neurological damage differs from what might have been expected if no brain injury had occurred. One means of achieving this is to estimate a client's expected score with a regression equation, using as predictors demographic variables such as age or socio-economic status, other test scores obtained either concurrently or pre-injury, or some combination of both. The standardised difference between the expected and obtained scores can then be used as an index of acquired cognitive impairment. In effect, regression equations are being used in this situation as an alternative to consulting conventional normative tables (Crawford & Howell, 1998).

Most manuals of neuropsychological tests contain tables of norms, often stratified by age and, in some instances by education levels, that allow evaluation of a client's obtained score in terms of a distribution of scores from a representative sample of healthy individuals. An abnormal score is one that lies at the extreme of the distribution. Although normative tables are a useful method of conveying information about expected scores, there can be advantages to developing regression equations. For example, the size of the sample is not so significant for regression-based norms, provided it is sufficiently large and diverse enough to provide stable correlations between the test and predictors. This is a significant advantage where local norms are being constructed and the resources for collecting a large and representative sample may not be available. For example, in New Zealand it may often be more cost effective to develop regression equations as an aid to test interpretation than to renorm neuro-psychological tests standardised in the United Kingdom or the United States. There are other advantages to using regression equations. The predicted or expected score is based on continuous variables rather than grouped variables, that is, on the client's actual age, for example, rather than on the age band in which they happen to fall. Thus the use of regression equations to estimate test scores can be more precise than referring to tables, and where more than one variable is used to stratify the sample, less cumbersome. As Crawford and Howell (1998) have observed, applying regression equations means that "... an individual's predicted score reflects his/her particular combination of demographic characteristics. Such an approach is in keeping with the emphasis placed on individual versus normative comparison standards in neuropsychological assessment (p. 755)."

There are numerous examples in the literature where regression equations have been constructed to convey normative information for the assessment of individual cases (e.g., Crawford, Allen, Cochrane, & Parker, 1990; Crawford & Howell, 1998; Crawford, Parker, Stewart, Besson, & Delacey, 1989; O'Brien, Godfrey, Freeman, & Perkins, 1999). Two classes of variables have commonly been used to estimate premorbid cognitive ability. One is "hold" tests, that is, measures based on over learned skills such as reading aloud or the application of semantic knowledge, which are relatively resistant to the effects of brain injury in non-aphasic patients. Of such tests, the most widely researched is the National Adult Reading Test (NART; Nelson & Willison, 1991), a measure of word reading ability. Performance on the NART has been shown to be largely unaffected by mild to moderate dementia (O'Carroll, Blaikie, & Whittick, 1987) and closed head injury (Crawford, Parker, & Besson, 1988; Moss & Dowd, 1991). Demographic variables such as age, education level, and pre-injury socioeconomic status are the other class of variable used to estimate premorbid functioning in regression equations (e.g., Barona, Reynolds, & Chastian, 1984; Franzen, Burgess, and Smith-Seemiller, 1989; Karzmark, Heaton, Grant, & Mathews, 1985). For example, Crawford and Allen (1997) found that regression equations developed using the demographic variables of education, race, occupation, and age had [R.sup.2] values of .53, .53, and .32 for predicting WAIS-R Verbal IQ, Performance IQ, and Full Scale IQ, respectively.

More recent approaches to estimating premorbid cognitive ability employ multiple regression equations to predict specific test scores for an individual using as predictors both demographic variables and hold tests (Crawford, 1992; Crawford, Moore, & Cameron, 1992). Combining demographic variables and performance on hold tests has produced the most accurate estimates of premorbid functioning currently available (Crawford, Stewart, Parker, Besson, & Cochrane, 1989; Crawford et al., 1992; Freeman & Godfrey, 1999; Krull, Scott, & Sherer, 1995; Van den Broek & Bradshaw, 1994), but to date, this procedure has not been widely applied to the assessment of executive deficits. Exceptions to this are the studies by Crawford et al. (1992), in which NART scores were successfully used to predict performance on a measure of verbal fluency, and Crawford, Obonsawin, and Allen (1998), where WAIS-R IQ, NART scores, and age were used to predict premorbid Paced Auditory Serial Addition Task (PASAT) scores.

The primary purpose of the present study was to develop regression equations for predicting scores on tests of neuropsychological functioning and to test their effectiveness on sample of persons with traumatic brain injury (TBI). TBI is a prevalent and expensive health problem in New Zealand (Caradoc-Davies & Dixon, 1995), commonly resulting in difficulties in the initiation and regulation of behaviour, often referred to as dysexecutive syndrome (Baddeley & Wilson, 1988). Impairments in executive functioning may present as difficulties with self-initiation, goal-oriented planning, sequencing and organising behaviour, and monitoring outcomes accurately (e.g., Milner, 1964; Stuss & Benson, 1986; Tate, 1999; Tranel, Anderson, & Benton, 1994). Research findings indicate that dysexecutive syndrome is often associated with loss of social autonomy and the inability to return to work after TBI (Mazaux, Masson, Levin, Alaoui, Maurette, & Barat, 1997). Because of the significant psychosocial impact of dysexecutive syndrome and the need to put in place appropriate rehabilitation strategies as soon as possible, timely and accurate diagnosis is a priority.

As part of the process of documenting the deficits resulting from TBI, clinical neuropsychologists frequently administer standardised tests of executive function. Accurate interpretation of the scores from the tests used in the diagnosis of dysexecutive syndrome is dependent upon the availability of normative data stratified for relevant demographic variables, such as education and age. For many such tests, however, the published norms are limited and often the only demographic variable taken into account is age. Although age-band norms are useful, frequently they do not allow sufficiently precise score interpretation. For …