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# 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("ADNIMERGE")
library(cusp)
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") )
rm(tmp_np)
# 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$mt2fa.ICV fit_avd <- cusp(y ~ mt2fa.AVDEL30MIN, alpha ~ WB +mt2fa.vAGE + mt2fa.PTGENDER +mt2fa.PTEDUCAT, beta ~ WB +mt2fa.vAGE + mt2fa.PTGENDER +mt2fa.PTEDUCAT, datac) summary(fit_avd) Amazing results ## Z-scores Now let's compare the weights of each variable on the model. We need to translate everything to z-scores (or just do another linear transformation that carry every thing to comparable values) datac$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$mt2fa.AVDEL30MIN) fit_avd_z <- cusp(y ~ zAVD, alpha ~ zWB + zAge + mt2fa.PTGENDER + zEduc, beta ~ zWB +zAge + mt2fa.PTGENDER + zEduc, datac) summary(fit_avd_z) The results are of course the same but the coefficients must be meaningful now, ## Composite scores First I'm going to try another NP test (Recognition) fit_avr <- cusp(y ~ zAVR, alpha ~ zWB + zAge + mt2fa.PTGENDER + zEduc, beta ~ zWB +zAge + mt2fa.PTGENDER + zEduc, datac) and this is not so good but still an improvement is done Now, let's try a composite score gfam <- data.frame(datac$zAVD, datac$zAVR) famod <- fa(gfam, scores="regression") datac$cs <- famod$scores fit_cs <- cusp(y ~ cs, alpha ~ zWB + zAge + mt2fa.PTGENDER + zEduc, beta ~ zWB +zAge + mt2fa.PTGENDER + zEduc, datac) And we get a very bad fit result That is, the composite score is not related through a cusp model to the independent variable analyzed here ## A try for ADAS-Cog data <- data.frame(mt2fa$WholeBrain, mt2fa$ICV, mt2fa$vAGE, mt2fa$PTGENDER, mt2fa$PTEDUCAT, mt2fa$Q4SCORE, mt2fa$Q8SCORE)
datac <- data[complete.cases(data),]