User Tools

Site Tools


This is an old revision of the document!

Using ADNI data for Cusp model fitting

Simple way

Auditory Verbal Learning Test fitted with Whole gray matter and covariables.

 # Estevez-Gonzalez, A., Kulisevsky, J., Boltes, A., Otermin, P., & Garcia-Sanchez, C. (2003). 
 # Rey verbal learning test is a useful tool for differential diagnosis in the preclinical phase
 # of Alzheimer's disease: comparison with mild cognitive impairment and normal aging. 
 # International Journal of Geriatric Psychiatry. 18 (11), 1021.
library(psych) #for composite scores
# Let's get the data
tmp_np <- merge(adas, neurobat, by=c("RID", "VISCODE") )
mt2fa <- merge(tmp_np, adnimerge, by=c("RID", "VISCODE") )
# Calculate the subject age at every point
mt2fa$vAGE = mt2fa$AGE + mt2fa$Years
data <- data.frame(mt2fa$WholeBrain, mt2fa$ICV, mt2fa$vAGE, mt2fa$PTGENDER, mt2fa$PTEDUCAT, mt2fa$AVDEL30MIN, mt2fa$AVDELTOT)
datac <- data[complete.cases(data),]
datac$WB = datac$mt2fa.WholeBrain/datac$mt2fa.ICV
fit_avd <- cusp(y ~ mt2fa.AVDEL30MIN, alpha ~ WB +mt2fa.vAGE + mt2fa.PTGENDER +mt2fa.PTEDUCAT, beta ~ WB +mt2fa.vAGE + mt2fa.PTGENDER +mt2fa.PTEDUCAT, datac)

Amazing results


Now let's compare the weights of each variable on the model. We need to translate everything to z-scores (or just do another linear transformation that carry every thing to comparable values)

datac$zWB = (datac$WB - mean(datac$WB))/sd(datac$WB)
datac$zAge = (datac$mt2fa.vAGE - mean(datac$mt2fa.vAGE))/sd(datac$mt2fa.vAGE)
datac$zEduc = (datac$mt2fa.PTEDUCAT - mean(datac$mt2fa.PTEDUCAT))/sd(datac$mt2fa.PTEDUCAT)
datac$zAVD = (datac$mt2fa.AVDEL30MIN - mean(datac$mt2fa.AVDEL30MIN))/sd(datac$mt2fa.AVDEL30MIN)
fit_avd_z <- cusp(y ~ zAVD, alpha ~ zWB + zAge + mt2fa.PTGENDER + zEduc, beta ~ zWB +zAge + mt2fa.PTGENDER + zEduc, datac)

The results are of course the same but the coefficients must be meaningful now,

Composite scores

First I'm going to try another NP test (Recognition)

fit_avr <- cusp(y ~ zAVR, alpha ~ zWB + zAge + mt2fa.PTGENDER + zEduc, beta ~ zWB +zAge + mt2fa.PTGENDER + zEduc, datac)

and this is not so good but still an improvement is done

Now, let's try a composite score

gfam <- data.frame(datac$zAVD, datac$zAVR)
famod <- fa(gfam, scores="regression")
datac$cs <- famod$scores
fit_cs <- cusp(y ~ cs, alpha ~ zWB + zAge + mt2fa.PTGENDER + zEduc, beta ~ zWB +zAge + mt2fa.PTGENDER + zEduc, datac)

And we get a very bad fit result

That is, the composite score is not related through a cusp model to the independent variable analyzed here

neuroimagen/adni_cusp.1450100487.txt.gz · Last modified: 2020/08/04 10:47 (external edit)