Abstract

The Repeatable Battery for the Assessment of Neuropsychological Status (RBANS) has become a popular cognitive screening instrument, particularly in elderly patients. Prior studies presented lookup tables for RBANS normative data based on age, gender, education, and race using a group of 718 community-dwelling older adults. However, regression-based normative formulae that simultaneously correct for all demographic variables may be more sensitive for detecting late life cognitive decline. Using data from the prior studies, linear regression was used to generate such formulae in the Indexes and subtests of the RBANS. Results indicated that ∼11% of the variance of Index scores was accounted for by these demographic variables, and 13% of variance in subtest scores. Although some differences were present between the lookup and regression-based norms, it is expected that these current results will present more accurate demographic corrections that allow clinicians and researchers to better interpret individual performances on the RBANS.

Introduction

Demographic variables, such as age, education, and gender, have long been noted to alter scores on a variety of neuropsychological measures (Heaton, Miller, Taylor, & Grant, 2004; Vanderploeg, Axelrod, Sherer, Scott, & Adams, 1995). As such, demographic corrections are routinely applied to normative data for various assessment instruments (Lezak, Howieson, Bigler, & Tranel, 2012; Mitrushina, Boone, Razani, & D'Elia, 2005; Strauss, Sherman, & Spreen, 2006). For example, Heaton and colleagues (2004) provided normative data for an expanded Halstead-Reitan Neuropsychological Battery that corrects for age, education, and gender using stepwise linear regression. Similarly, Ivnik and colleagues presented age, education, and ethnicity corrections for an extensive battery of neuropsychological tests in their large cohort from the Mayo Clinics (Ivnik et al., 1992; Ivnik, Malec, Smith, Tangalos, & Petersen, 1996; Lucas et al., 2005). Race and ethnicity has also routinely been shown to influence cognitive test performances (Manly et al., 1998, 2011; Manly, Jacobs, Touradji, Small, & Stern, 2002).

In prior work with the Repeatable Battery for the Assessment of Neuropsychological Status (RBANS), we presented four separate sets of lookup normative tables for the RBANS that corrected for age, gender, education, and race using a group of 718 community-dwelling older adults (Duff et al., 2003; Duff, Schoenberg, Mold, Scott, & Adams, 2011; Patton et al., 2003). However, regression-based normative formulae that simultaneously correct for all demographic variables may be both more practical for the busy clinician and more sensitive for detecting late life cognitive decline in the patient. Therefore, the current study sought to streamline the normative process for the RBANS by providing a single regression-based formula that corrected for multiple demographic variables at the same time. Based on our prior work, it was expected that age, gender, education, and race would be useful in predicting subtest and Index scores on the RBANS, and that regression-based normative formulae could be generated from these data.

Method

Participants and Procedure

Data for the present study were taken from the Oklahoma Longitudinal Assessment of Health Outcomes in Mature Adults (OKLAHOMA) study, which is more fully described in Duff and colleagues (2003). Briefly, participants were recruited from the practices of family physicians throughout Oklahoma if they were 65 years or older and had an office visit in the past 18 months. From an initial pool of 4,024 individuals, 2,575 individuals were contacted about the study, and 824 individuals were eventually enrolled. Participants provided informed consent, completed questionnaires about health-related factors, and completed checks of hearing, vision, gait, and balance. Form A of the RBANS was also administered during a research visit.

Of these 824 participants, 106 were eliminated from the following analyses due to a variety of self-reported co-morbid medical conditions that would be likely to negatively impact cognitive functioning (stroke or transient ischemic attack = 52; head injury = 33; concussion = 19; seizures = 12; Parkinson's disease = 5; brain hemorrhage = 1; note that some participants reported more than one exclusionary condition). Sensory deficits (e.g., macular degeneration) precluded some participants from completing visuoperceptual subtests.

Measure

The RBANS (Randolph, 1998) is a brief, individually administered test measuring attention, language, visuospatial/constructional abilities, and immediate and delayed memory. It consists of 12 subtests, which yield 5 Index scores and a Total Scale score. Normative information from the manual, which is used to calculate the Index and Total scores, is based on 540 healthy adults who ranged in age from 20 to 89 years old. All subtests were administered and scored as defined in the manual, with the exception of the Figure Copy and Figure Recall, which is more fully described in Duff and colleagues (2007).

Data Analysis

A series of linear regressions were calculated, one for each subtest and Index score of the RBANS. In each regression model, the raw scores on the subtests (or age-corrected standard scores on the Indexes) were the criterion variables, and age (in years), gender (coded as 0 = female, 1 = male), education (coded as 1 =< 12 years, 2 = 12 years, 3 = 13–15 years, 4 => 15 years), and race (0 = white, non-Hispanic, 1 = other) were the predictor variables that were entered as a single block (i.e., “enter” model). Given that multiple regression models were examined, a p-value of <.01 was used throughout. For each regression model, the constant and unstandardized coefficients were used to generate normative formulae.

Results

In the final sample of 718 participants, their mean age was 73.3 (5.8) years, most were female (58% vs. 42% male), their modal level of education was some college (<12 years = 14%, 12 years = 26%, 13–15 years = 32%, >15 years = 28%), and most were white, non-Hispanic (86%). Mean scores on the RBANS Indexes were all in the average range (see Table 2 in Duff et al., 2003, for means and standard deviations on all subtests and Indexes) for this cohort.

The results of the single step of each regression analysis are presented in Table 1. Since all demographic variables were entered as a single block, they contributed to the prediction of each RBANS subtest and Index, albeit to different degrees. Age, gender, education, and race accounted for 5–28% of the variance in the regression models, with a mean of 11% of the variance in the Index scores being explained by these demographic variables, and 13% of the variance in subtest scores.

Table 1.

Regression equations for RBANS subtests and indexes

Index F(dfR2 SEesta Normative equationb 
List Learning 23.28 (4,717) 0.12 5.56 40.84 − (age × 0.25) − (gender × 1.70) + (educ × 0.76) − (race × 1.44) 
Story Memory 26.19 (4,717) 0.13 4.24 25.38 − (age × 0.16) − (gender × 0.38) + (educ × 0.73) − (race × 1.83) 
Figure Copy 16.23 (4,716) 0.08 2.02 22.76 − (age × 0.07) + (gender × 0.08) + (educ × 0.21) − (race × 0.70) 
Line Orientation 28.77 (4,716) 0.14 3.36 16.40 − (age × 0.04) + (gender × 1.50) + (educ × 0.48) − (race × 1.60) 
Picture Naming 28.37 (4,715) 0.14 0.76 10.37 − (age × 0.02) + (gender × 0.19) + (educ × 0.12) − (race × 0.44) 
Semantic Fluency 21.01 (4,717) 0.11 4.57 30.59 − (age × 0.18) − (gender × 1.99) + (educ × 0.34) − (race × 1.79) 
Digit Span 9.13 (4,717) 0.05 2.72 12.43 − (age × 0.03) + (gender × 0.42) + (educ × 0.27) − (race × 0.77) 
Coding 69.80 (4,716) 0.28 9.14 91.63 − (age × 0.82) − (gender × 3.38) + (educ × 1.57) − (race × 4.80) 
List Recall 30.72 (4,717) 0.15 2.44 12.47 − (age × 0.11) − (gender × 1.25) + (educ × 0.26) − (race × 1.39) 
List Recognition 19.42 (4,716) 0.10 1.55 22.53 − (age × 0.05) − (gender × 0.64) + (educ × 0.15) − (race × 0.66) 
Story Recall 28.13 (4,717) 0.14 2.77 16.62 − (age × 0.14) − (gender × 0.68) + (educ × 0.41) − (race × 1.07) 
Figure Recall 21.86 (4,716) 0.11 4.07 23.98 − (age × 0.17) + (gender × 0.47) + (educ × 0.29) − (race × 2.30) 
Immediate Memory 16.81 (4,717) 0.09 17.24 95.54 − (age × 0.13) − (gender × 4.36) + (educ × 2.93) − (race × 6.65) 
Visuospatial/Constructional 31.09 (4,716) 0.15 16.20 103.34 − (age × 0.18) + (gender × 5.20) + (educ × 2.94) − (race × 8.79) 
Language 13.73 (4,715) 0.07 10.94 92.77 − (age × 0.01) − (gender × 3.25) + (educ × 1.23) − (race × 5.74) 
Attention 18.12 (4,716) 0.09 15.43 106.92 − (age × 0.21) − (gender × 2.07) + (educ × 2.55) − (race × 7.35) 
Delayed Memory 22.65 (4,715) 0.11 16.02 125.23 − (age × 0.43) − (gender × 5.20) + (educ × 2.12) − (race × 9.93) 
Total Scale 34.47 (4,714) 0.16 14.60 105.01 − (age × 0.24) − (gender × 2.86) + (educ × 3.24) − (race × 10.33) 
Index F(dfR2 SEesta Normative equationb 
List Learning 23.28 (4,717) 0.12 5.56 40.84 − (age × 0.25) − (gender × 1.70) + (educ × 0.76) − (race × 1.44) 
Story Memory 26.19 (4,717) 0.13 4.24 25.38 − (age × 0.16) − (gender × 0.38) + (educ × 0.73) − (race × 1.83) 
Figure Copy 16.23 (4,716) 0.08 2.02 22.76 − (age × 0.07) + (gender × 0.08) + (educ × 0.21) − (race × 0.70) 
Line Orientation 28.77 (4,716) 0.14 3.36 16.40 − (age × 0.04) + (gender × 1.50) + (educ × 0.48) − (race × 1.60) 
Picture Naming 28.37 (4,715) 0.14 0.76 10.37 − (age × 0.02) + (gender × 0.19) + (educ × 0.12) − (race × 0.44) 
Semantic Fluency 21.01 (4,717) 0.11 4.57 30.59 − (age × 0.18) − (gender × 1.99) + (educ × 0.34) − (race × 1.79) 
Digit Span 9.13 (4,717) 0.05 2.72 12.43 − (age × 0.03) + (gender × 0.42) + (educ × 0.27) − (race × 0.77) 
Coding 69.80 (4,716) 0.28 9.14 91.63 − (age × 0.82) − (gender × 3.38) + (educ × 1.57) − (race × 4.80) 
List Recall 30.72 (4,717) 0.15 2.44 12.47 − (age × 0.11) − (gender × 1.25) + (educ × 0.26) − (race × 1.39) 
List Recognition 19.42 (4,716) 0.10 1.55 22.53 − (age × 0.05) − (gender × 0.64) + (educ × 0.15) − (race × 0.66) 
Story Recall 28.13 (4,717) 0.14 2.77 16.62 − (age × 0.14) − (gender × 0.68) + (educ × 0.41) − (race × 1.07) 
Figure Recall 21.86 (4,716) 0.11 4.07 23.98 − (age × 0.17) + (gender × 0.47) + (educ × 0.29) − (race × 2.30) 
Immediate Memory 16.81 (4,717) 0.09 17.24 95.54 − (age × 0.13) − (gender × 4.36) + (educ × 2.93) − (race × 6.65) 
Visuospatial/Constructional 31.09 (4,716) 0.15 16.20 103.34 − (age × 0.18) + (gender × 5.20) + (educ × 2.94) − (race × 8.79) 
Language 13.73 (4,715) 0.07 10.94 92.77 − (age × 0.01) − (gender × 3.25) + (educ × 1.23) − (race × 5.74) 
Attention 18.12 (4,716) 0.09 15.43 106.92 − (age × 0.21) − (gender × 2.07) + (educ × 2.55) − (race × 7.35) 
Delayed Memory 22.65 (4,715) 0.11 16.02 125.23 − (age × 0.43) − (gender × 5.20) + (educ × 2.12) − (race × 9.93) 
Total Scale 34.47 (4,714) 0.16 14.60 105.01 − (age × 0.24) − (gender × 2.86) + (educ × 3.24) − (race × 10.33) 

Notes: All F tests are significant at p < .001. Subtest scores are raw scores. Index scores are age-corrected standard scores from the manual.

aStandard error of the estimate.

bAge is in years; gender is coded as 1 = male, 0 = female; education is coded as 1 =< 12 years, 2 = 12 years, 3 = 13–15 years, 4 => 15 years; and race is coded as 0 = white, non-Hispanic, 1 = other.

Discussion

The current study extends the normative data of the RBANS by providing regression-based age-, gender-, education-, and race-corrected subtest, Index, and Total Scale scores for a large sample of community-dwelling older adults. These data, which were previously available through four sets of lookup tables (Duff et al. 2003, 2011; Patton et al., 2003), continue to allow clinicians to make direct comparisons between individual components of the RBANS. Additionally, as lookup tables tend to “smooth” data, these regression formulae may allow for more specific and sensitive reporting of scores, which could improve clinical decision making in evaluations using the RBANS with older adults.

Age, education, gender, and race predicted 5%–28% of the variance in RBANS scores. Approximately 11% of the variance in Index scores was accounted for by these demographic variables. Across the 12 subtests of the RBANS, 13% of the variance was explained. Admittedly, these are relatively small effects. However, this is relatively consistent with other regression-based normative data. For example, Moses, Pritchard, and Adams (1999) found that age and education accounted for ∼10% of the variance on the Halstead-Reitan battery, whereas Heaton and colleagues (2004) found that demographic variables explained ∼30% of the variance on this same battery. Manly and colleagues (2011) found that race/ethnicity accounted for ∼5% of cognitive test scores. More relevant to the current study, Lim, Collinson, Feng, and Ng (2010) found age and education explained 3%–27% of the variance on the RBANS Index scores in an elderly Chinese sample. The variability in these findings is likely multifactorial, including restriction of age range in the samples (e.g., elderly vs. entire adult spectrum), neuropsychological measures examined (e.g., screening measures vs. more comprehensive tests), sociocultural variables, and scoring procedures (e.g., figure scoring of Duff et al., 2007 vs. manual).

Not surprisingly, these four demographic variables did not contribute to all subtests and Indexes evenly in the regression models. For example, looking at standardized β weights (not presented but available from the first author), age contributed the most variance to 8 of the 12 subtest scores, but none of the 6 Index scores (including Total Scale). Even in our reasonably age-truncated sample (e.g., 65–94 years old), raw scores on most subtests were related to age, with all significant findings going in the expected direction (i.e., older age associated with lower cognitive scores). It may have been that no Index scores had very large age effects because the Index scores were already corrected for age from the RBANS normative sample. Education, which is not controlled for in the RBANS normative data, contributed the most to three subtests and four Index scores. As expected, higher levels of education led to higher RBANS scores. Although the original RBANS normative data are not corrected for education, the manual presents data to suggest that our findings are consistent with the standardization sample. For example, in Table 5.4 of the RBANS manual, it shows that 70–89 year olds with less than high school education average approximately 90 on the Total Scale score, whereas those with high school education average 100, and those with greater than high school education average 107 (Randolph, 1998). Failing to account for education may lead to misinterpretation of RBANS scores, especially on the Indexes. Gender contributed the most variance to 3 of 12 subtest scores, but none of Index scores. Since females were coded 0 and males 1, a review of the directionality of the β weights shows that females performed better than males on List Learning, Story Memory, Semantic Fluency, Coding, List Recall, List Recognition, Story Recall, Immediate Memory, Language, Attention, Delayed Memory, and Total Scale, whereas males outperformed females on Line Orientation, Figure Copy, Picture Naming, Digit Span, Figure Recall, and Visuospatial/Constructional. Similarly, given the coding of race in these analyses, it was observed that whites outperformed non-whites on all subtests and Index scores. Although these findings have been previously presented in individual articles (Duff et al., 2003, 2011; Patton et al., 2003), the current analyses allows for a clinician to correct for all relevant demographic variables at the same time. Each equation is tailored toward the demographic variables that are known to influence performance on the RBANS in this elderly sample. This could minimize undercorrections and overcorrections of scores, and provide more accurate interpretations of cognitive performances.

These regression-based normative formulae are not meant to replace the original normative data presented in the RBANS manual. Instead, these current results can complement the existing information. For example, the RBANS manual and more recent supplemental material (Randolph, 2012) present age-corrected scores for the individual subtests and Indexes of the RBANS, which may be appropriate when comparing a specific patient to his/her age-matched peers. However, the current regression-based norms might allow for comparison with a more fine-grained peer group, which may be more appropriate for individuals who deviate from the typical patient.

Clinicians less familiar with regression-based normative formulae might benefit from a case example to demonstrate their use. For example, a 78-year-old white male with 16 years of education obtains a raw score of 18 on the List Learning subtest. Using the look-up tables in Duff and colleagues (2003), his subtest raw score would equate to an age-corrected scaled score of 8, and this age-corrected score would equate to an education-corrected scaled score of 8. Applying this age- and education-corrected score to the look-up table in Duff and colleagues (2011), his age-, education-, and gender-corrected scaled score would be 8 (25th percentile). Unfortunately, this score cannot be easily incorporated into the race-corrections of Patton and colleagues (2003), as only corrections for African Americans were presented in that paper. Alternatively, using the regression equation in Table 1, his fully corrected demographic score on this subtest would be 22.68 (i.e., 40.84−[age × 0.25] + [educ × 0.76]−[gender × 1.7]−[race × 1.44] = 40.84−[78 × 0.25] + [4 × 0.76]−[1 × 1.7]−[0 × 1.44] = 22.68). This predicted score is compared with his observed score, which is divided by the standard error of the estimate for that subtest, to yield a z-score of −0.84 (i.e., [18–22.68]/5.56 = −0.84). From an interpretive standpoint, this patient's observed score falls nearly 1 SD below age-, education-, gender-, and race-matched peers (or roughly at the 20th percentile compared with his peers). Not surprisingly, the results of the regression equation and the look-up tables are similar, as they are based on the same dataset. However, it is thought that the regression equation would more precisely parse out the score variations due to demographic variables than the look-up tables. See Table 2 for additional RBANS subtest and Index scores based on the same demographic information. The interested reader can contact the authors for a copy of an Excel spreadsheet that automatically applies these regression equations for an individual patient. Interestingly, if the age-corrected normative data from the RBANS supplemental material (Randolph, 2012) are used for this case, then a much lower estimate of functioning is observed (i.e., [18–26.6]/5.0 = z = −1.72 = 4th percentile). This seems to show some inherent differences between the standardization sample and the OKLAHOMA sample, as well as highlighting the need for complementary sets of normative data.

Table 2.

Case example of regression equations for 78-year-old male with 16 years of education

Index Observed score Predicted score Standard error of estimate Z-Score Percentile 
List Learning 18 22.68 5.56 −0.84 20 
Story Memory 14 15.44 4.24 −0.34 37 
Figure Copy 13 18.22 2.02 −2.58 
Line Orientation 16.70 3.36 −2.59 <1 
Picture Naming 10 9.58 0.76 0.55 70 
Semantic Fluency 13 15.92 4.57 −0.64 26 
Digit Span 12 11.59 2.72 0.15 56 
Coding 16 30.57 9.14 −1.59 
List Recall 3.68 2.44 −1.51 
List Recognition 18 18.59 1.55 −0.38 35 
Story Recall 6.66 2.77 0.12 54 
Figure Recall 12.35 4.07 −1.07 14 
Immediate Memory 81 92.76 17.24 −0.68 25 
Visuospatial/Constructional 64 106.26 16.2 −2.61 <1 
Language 88 93.66 10.94 −0.52 30 
Attention 82 98.67 15.43 −1.08 14 
Delayed Memory 78 94.97 16.02 −1.06 14 
Total Scale 73 96.39 14.6 −1.60 
Index Observed score Predicted score Standard error of estimate Z-Score Percentile 
List Learning 18 22.68 5.56 −0.84 20 
Story Memory 14 15.44 4.24 −0.34 37 
Figure Copy 13 18.22 2.02 −2.58 
Line Orientation 16.70 3.36 −2.59 <1 
Picture Naming 10 9.58 0.76 0.55 70 
Semantic Fluency 13 15.92 4.57 −0.64 26 
Digit Span 12 11.59 2.72 0.15 56 
Coding 16 30.57 9.14 −1.59 
List Recall 3.68 2.44 −1.51 
List Recognition 18 18.59 1.55 −0.38 35 
Story Recall 6.66 2.77 0.12 54 
Figure Recall 12.35 4.07 −1.07 14 
Immediate Memory 81 92.76 17.24 −0.68 25 
Visuospatial/Constructional 64 106.26 16.2 −2.61 <1 
Language 88 93.66 10.94 −0.52 30 
Attention 82 98.67 15.43 −1.08 14 
Delayed Memory 78 94.97 16.02 −1.06 14 
Total Scale 73 96.39 14.6 −1.60 

Notes:Z-score = (observed score – predicted score)/standard error of estimate.

Cautious interpretation is advised when applying the current normative data to individuals who are not comparable with the present sample. For example, this cohort was elderly (e.g., 65–94 years old) and well educated (e.g., most with at least some college). The utility of the normative formulae for much younger and/or poorer educated patients is less clear. Additionally, our sample was predominantly Caucasian, with limited representation of participants who were African American, Hispanic American, Asian American, or Native American. As such, these models may not accurately capture all of the effects of racial/ethnic status, which should be examined in future studies (Lucas et al., 2005). Furthermore, the inclusion of premorbid intellect or occupational functioning may have added to the accuracy of these prediction equations.

Several limitations of the present study should be noted. First, as with most regression-based prediction formulas (Tabachnick & Fidell, 1996), the current RBANS equations are likely to provide the best estimates of current performances for individuals who do not fall at the extremes of functioning (e.g., <2nd percentile or >98th percentile). In these cases, the prediction equations are more susceptible to regression to the mean effects, and will likely overestimate or underestimate actual scores. Readers are reminded that all Figure Copy and Figure Recall protocols were scored using modified scoring criteria (Duff et al., 2007), and not the one specified in the RBANS manual. Finally, participants in the current study do not represent “optimal” aging, as defined by the APA Working Group on the Older Adult (1998). Rather, they may be more representative of “normal” aging patterns, which include individuals with numerous medical conditions. It is possible that some within this group of older adults might be slightly declining in their cognitive abilities. Despite these limitations, we hope that the resulting equations will allow clinicians and researchers to more accurately account for demographic variables in their patient and participants and more precisely assess cognition in older individuals.

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