demograficos

## 3 observations missing `PIB` have been removed. To include these observations, use `forcats::fct_explicit_na()` on `PIB` column before passing to `tbl_summary()`.
Characteristic negative, N = 61 positive, N = 141
MMSE 27.50 (25.25, 29.00) 24.00 (21.25, 25.75)
CDR 0.75 (0.50, 1.00) 0.50 (0.50, 1.00)
FDG
AD pattern 3 (50%) 12 (86%)
Others 2 (33%) 2 (14%)
Vascular 1 (17%) 0 (0%)
Memoria alterada 5 (83%) 11 (79%)
Lenguaje alterado 3 (50%) 9 (64%)
1 Median (IQR); n (%)

Capacidad diagnostica

Aca testeamos el poder diagnostico de obtener un PIB positivo representado por el area abajo de la curva roc (AUC)

## Setting levels: control = 0, case = 1
## Setting direction: controls < cases
## 
## Call:
## roc.default(response = MCI_CUDIM$DX, predictor = MCI_CUDIM$`Recuerdo de palabras - - puntaje`)
## 
## Data: MCI_CUDIM$`Recuerdo de palabras - - puntaje` in 6 controls (MCI_CUDIM$DX 0) < 14 cases (MCI_CUDIM$DX 1).
## Area under the curve: 0.6429
## Setting levels: control = 0, case = 1
## Setting direction: controls > cases
## 
## Call:
## roc.default(response = MCI_CUDIM$DX, predictor = MCI_CUDIM$`Praxia constructiva - puntaje`)
## 
## Data: MCI_CUDIM$`Praxia constructiva - puntaje` in 6 controls (MCI_CUDIM$DX 0) > 14 cases (MCI_CUDIM$DX 1).
## Area under the curve: 0.5774
## Setting levels: control = 0, case = 1
## Setting direction: controls < cases
## 
## Call:
## roc.default(response = MCI_CUDIM$DX, predictor = MCI_CUDIM$`Recuerdo de palabras - puntaje`)
## 
## Data: MCI_CUDIM$`Recuerdo de palabras - puntaje` in 6 controls (MCI_CUDIM$DX 0) < 14 cases (MCI_CUDIM$DX 1).
## Area under the curve: 0.631
## Setting levels: control = 0, case = 1
## Setting direction: controls < cases
## 
## Call:
## roc.default(response = MCI_CUDIM$DX, predictor = MCI_CUDIM$`Reconocimiento - puntaje`)
## 
## Data: MCI_CUDIM$`Reconocimiento - puntaje` in 6 controls (MCI_CUDIM$DX 0) < 14 cases (MCI_CUDIM$DX 1).
## Area under the curve: 0.7202
## Setting levels: control = 0, case = 1
## Setting direction: controls > cases
## 
## Call:
## roc.default(response = MCI_CUDIM$DX, predictor = MCI_CUDIM$`Fluencia verbal semántica`)
## 
## Data: MCI_CUDIM$`Fluencia verbal semántica` in 6 controls (MCI_CUDIM$DX 0) > 14 cases (MCI_CUDIM$DX 1).
## Area under the curve: 0.5536
## Setting levels: control = 0, case = 1
## Setting direction: controls > cases
## 
## Call:
## roc.default(response = MCI_CUDIM$DX, predictor = MCI_CUDIM$RecordTotalCorrect)
## 
## Data: MCI_CUDIM$RecordTotalCorrect in 6 controls (MCI_CUDIM$DX 0) > 11 cases (MCI_CUDIM$DX 1).
## Area under the curve: 0.6212
## Setting levels: control = 0, case = 1
## Setting direction: controls < cases
## 
## Call:
## roc.default(response = MCI_CUDIM$DX, predictor = MCI_CUDIM$`Recuerdo de palabras - - puntaje`,     percent = TRUE, plot = TRUE, legacy.axes = FALSE, col = "salmon",     lwd = 2, print.auc = TRUE)
## 
## Data: MCI_CUDIM$`Recuerdo de palabras - - puntaje` in 6 controls (MCI_CUDIM$DX 0) < 14 cases (MCI_CUDIM$DX 1).
## Area under the curve: 64.29%
## Setting levels: control = 0, case = 1
## Setting direction: controls < cases
## Setting levels: control = 0, case = 1
## Setting direction: controls < cases
## Setting levels: control = 0, case = 1
## Setting direction: controls > cases

Punto de corte del reconocimiento

En estos resulatos encontraremos el punto de corte optimo con su sensibilidad y especificidad, en este caso el pto de corte es 13.

## 
## Attaching package: 'cutpointr'
## The following objects are masked from 'package:pROC':
## 
##     auc, roc
## Assuming the positive class is 1
## Assuming the positive class has higher x values
## Running bootstrap...
## Method: oc_mean 
## Predictor: Reconocimiento - puntaje 
## Outcome: DX 
## Direction: >= 
## Nr. of bootstraps: 100 
## 
##     AUC  n n_pos n_neg
##  0.7202 20    14     6
## 
##  optimal_cutpoint sum_sens_spec  acc sensitivity specificity tp fn fp tn
##              13.1        1.4048 0.65      0.5714      0.8333  8  6  1  5
## 
## Predictor summary: 
##     Data Min.   5% 1st Qu. Median      Mean 3rd Qu.   95% Max.       SD NAs
##  Overall    0 1.90    8.75   12.0 13.100000   19.25 23.05   24 7.304649   0
##        0    2 2.75    6.00    9.5  9.166667   10.75 16.25   18 5.492419   0
##        1    0 2.60    9.25   17.5 14.785714   20.75 23.35   24 7.495420   0
## 
## Bootstrap summary: 
##           Variable  Min.    5% 1st Qu. Median  Mean 3rd Qu.   95%  Max.   SD
##   optimal_cutpoint 10.80 11.50   12.57  13.45 13.48   14.10 15.70 17.10 1.28
##              AUC_b  0.43  0.53    0.68   0.73  0.73    0.81  0.89  0.98 0.11
##            AUC_oob  0.25  0.40    0.60   0.71  0.71    0.83  1.00  1.00 0.17
##    sum_sens_spec_b  0.92  1.07    1.33   1.47  1.44    1.60  1.71  1.85 0.21
##  sum_sens_spec_oob  0.50  0.89    1.25   1.40  1.40    1.62  1.81  2.00 0.30
##              acc_b  0.40  0.55    0.60   0.65  0.68    0.75  0.80  0.90 0.09
##            acc_oob  0.29  0.43    0.57   0.67  0.65    0.75  0.88  1.00 0.14
##      sensitivity_b  0.31  0.46    0.56   0.62  0.62    0.67  0.80  0.86 0.10
##    sensitivity_oob  0.20  0.25    0.50   0.59  0.57    0.67  0.83  1.00 0.18
##      specificity_b  0.33  0.50    0.75   0.83  0.82    1.00  1.00  1.00 0.17
##    specificity_oob  0.00  0.50    0.67   1.00  0.83    1.00  1.00  1.00 0.24
##     cohens_kappa_b -0.05  0.06    0.24   0.36  0.36    0.50  0.61  0.80 0.18
##   cohens_kappa_oob -0.29 -0.08    0.16   0.33  0.32    0.46  0.71  1.00 0.26
##  NAs
##    0
##    0
##    3
##    0
##    3
##    0
##    0
##    0
##    0
##    0
##    3
##    0
##    0
## Warning: Removed 3 rows containing non-finite values (stat_density).