score2adjust | R Documentation |
Convert neuropsychological test scores to demographically adjusted norms.
score2adjust( data = NULL, test.score = NULL, group.id = NULL, control.id = NULL, all.controls = FALSE, demographics = NULL, mfp.alpha = 1, rnd.a = FALSE, mean.a = 50, sd.a = 10 )
data |
a data frame containing the variables needed for the norming process. |
test.score |
a character string specifying the name of the test to be
normed, usually the output of the |
group.id |
a character string specifying the name of the variable containing group identification (i.e. control vs exposed/test/risk). Ignored, if all.controls = TRUE. |
control.id |
a character string specifying the label of the control group within group.id variable. Ignored, if all.controls = TRUE. |
all.controls |
a logical indicating whether all observations should be treated as controls. Overwrites group.id and control.id. |
demographics |
a single or multiple character strings (concatenated by
|
mfp.alpha |
a numeric value between 0 and 1 that sets significance level
for inclusion of demographic predictors into normative formula. Passed to the
|
rnd.a |
a logical indicating whether the adjusted scores (T-scores) should be rounded. Default is FALSE. |
mean.a |
numeric value for the mean of adjusted score (T-score) distribution. |
sd.a |
numeric value for the standard deviation of adjusted score (T-score) distribution. |
The score2adjust()
function can be used by neuropsychologists, who
wish to construct normative formulas for cognitive tests that adjust for
expected effects of demographic characteristics (e.g., age), using methods
described in Heaton et al. (2003 & 2009). The adjusted scores are sometimes
referred to as T-scores in the literature. The norming procedure makes use of
the mfp()
function from the mfp
package to explore nonlinear
associations between cognition and demographic variables. Detailed
description of the procedure will be found in Umlauf et al. (in revision).
A list consisting of 3 objects. The first two are vectors containing the
non-adjusted test scores and the calculated demographically adjusted scores.
The last item in the output list is also a list called MFP.formulas
.
It contains the information for calculation of adjusted scores, including
variable transformations (if any), multiple fractional polynomial (MFP) model
coefficients, the standard deviation of residuals resulting from the MFP
modeling, and a matrix with number of rows equal to the number of predictors
and 2 columns containing powers (in numeric form) selected for variable
transformations.
Anya Umlauf
Umlauf A et al. (2022) Automated procedure for demographic adjustments on cognitive test scores. Manuscript submitted for publication.
Heaton RK, Taylor MJ, & Manly J (2003) Demographic effects and use of demographically corrected norms with the WAIS-III and WMS-III. In: Tulsky D et al. (Eds.) Clinical Interpretation of the WAIS-III and WMS-III. San Diego, CA: Academic Press, 183-210.
Heaton RK, Ryan L, & Grant I (2009) Demographic influences and use of demographically corrected norms in neuropsychological assessment. In Grant I & Adams KM (Eds.) Neuropsychological Assessment of Neuropsychiatric and Neuromedical Disorders. New York, NY: Oxford University Press, 127-155.
Benner A (2005) mfp: Multivariable fractional polynomials. R News 5(2): 20–23.
data(PsychTestData) PsychTestData$scaledscore <- raw2scaled(data=PsychTestData, test="rawscore", test.min=0, test.max=36, test.better="High", group.id="group", control.id="control")[[2]] score2adjust(data = PsychTestData, test.score = "scaledscore", group.id = "group", control.id = "control", demographics = c("age", "sex"))
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