Overdose of Amitriptyline — Overdose • heplots

Data on overdoses of the drug amitriptyline. Amitriptyline is a drug prescribed by physicians as an antidepressant. However, there are also conjectured side effects that seem to be related to the use of the drug: irregular heart beat, abnormal blood pressure and irregular waves on the electrocardiogram (ECG). This dataset (originally from Rudorfer, 1982) gives data on 17 patients admitted to hospital after an overdose of amitriptyline. The two response variables are: TCAD and AMI. The other variables are predictors.

Usage

data("Overdose")

Format

A data frame with 17 observations on the following 7 variables.

TCAD: total TCAD plasma level, a numeric vector
AMI: amount of amitriptyline present in the TCAD plasma level, a numeric vector
Gender: a factor with levels Male Female
amount: amount of drug taken at time of overdose, a numeric vector
BP: diastolic blood pressure, a numeric vector
ECG_PR: ECG PR wave measurement, a numeric vector
ECG_QRS: ECG QRS wave measurement, a numeric vector

Source

Johnson & Wichern (2005), Applied Multivariate Statistical Analysis, Exercise 7.25, p. 426.

References

Rudorfer, M. V. Cardiovascular changes and plasma drug levels after amitriptyline overdose. (1982). J. Toxicology - Clinical Toxicology. 19(1),67-78. doi:10.3109/15563658208990367 , PMID: 7154142.

Clay Ford, "Getting started with Multivariate Multiple Regression", https://library.virginia.edu/data/articles/getting-started-with-multivariate-multiple-regression.

ECG measurements:

PR: https://en.wikipedia.org/wiki/PR_interval
QRS: https://en.wikipedia.org/wiki/QRS_complex

Examples

data(Overdose)
str(Overdose)
#> 'data.frame':	17 obs. of  7 variables:
#>  $ TCAD   : int  3389 1101 1131 596 896 1767 807 1111 645 628 ...
#>  $ AMI    : int  3149 653 810 448 844 1450 493 941 547 392 ...
#>  $ Gender : Factor w/ 2 levels "Male","Female": 2 2 1 2 2 2 2 1 2 2 ...
#>  $ amount : int  7500 1975 3600 675 750 2500 350 1500 375 1050 ...
#>  $ BP     : int  0 0 60 60 70 60 80 70 60 60 ...
#>  $ ECG_PR : int  220 200 205 160 185 180 154 200 137 167 ...
#>  $ ECG_QRS: int  140 100 111 120 83 80 98 93 105 74 ...
pairs(Overdose) 


over.mlm <- lm(cbind(TCAD, AMI) ~ Gender + amount + BP + ECG_PR + ECG_QRS, data = Overdose)
coef(over.mlm)
#>                       TCAD           AMI
#> (Intercept)  -2879.4782461 -2728.7085444
#> GenderFemale   675.6507805   763.0297617
#> amount           0.2848511     0.3063734
#> BP               7.2511714     7.2055597
#> ECG_PR          10.2721328     8.8961977
#> ECG_QRS          7.5982397     4.9870508

# check for outliers
cqplot(over.mlm)


# HE plot shows that relations of responses to predictors are essentially one-dimensional
heplot(over.mlm)


# canonical correlation analysis 
if(require(candisc)) {
cancor(cbind(TCAD, AMI) ~ as.numeric(Gender) + amount + BP + ECG_PR + ECG_QRS, data = Overdose)
}
#> Warning: non-list contrasts argument ignored
#> 
#> Canonical correlation analysis of:
#> 	 5   X  variables:  as.numeric(Gender), amount, BP, ECG_PR, ECG_QRS 
#>   with	 2   Y  variables:  TCAD, AMI 
#> 
#>     CanR CanRSQ Eigen percent    cum                          scree
#> 1 0.9437 0.8905 8.131   96.35  96.35 ******************************
#> 2 0.4853 0.2355 0.308    3.65 100.00 *                             
#> 
#> Test of H0: The canonical correlations in the 
#> current row and all that follow are zero
#> 
#>      CanR LR test stat approx F numDF denDF  Pr(> F)   
#> 1 0.94366      0.08372   4.9120    10    20 0.001223 **
#> 2 0.48528      0.76451   0.8471     4    11 0.524104   
#> ---
#> Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1