Chapter 6: Covariate analysis Flashcards
why is covariate model building done
To identify sub-groups of patients that may be at potential risk of toxicity or sub-therapuetic effect
Increase the understanding of a studied system
Confirm the absence of important influence from the covariate.
Define a covariate
A covariate in the context of pharmacokinetic modelling is a variable that is considered in the model to potentially explain the variability in pharmacokinetic parameters among individuals.
List examples of covariates
Demographics: Gender, Age, Weight, Height, BMI
Lab values: serum creatinine, albumin, bilirubin
Disease parameters: Baseline, severity of injury
Environmental factors: Smoking, alcohol
Therapy related: Dialysis
What are the types of covariates
Continuous variable: Age, weight, serum creatinine
Binary categorical/dichotomous variable: Sex
Ordered categorical variable: Grade of renal injury
Un-ordered categorical variable: Race, Pharmacogenomic variant
Why is covariate model building done
- To identify sub-groups of patients that may be at potential risk of toxicity or sub-therapeutic effect
- Increase the understanding of a studied system
- Confirm the absence of important influence from the covariate
Describe the concept of step-wise covariate modelling to select covariates to test
Step-wise Covariate Modeling (SCM): Involves a systematic approach where covariates/variables are added one at a time (forward selection) in a step-wise fashion.
If inclusion of a variable provides an improved model fit, it is retained in the model as a covariate and the program then moves on to test the next variable. This is known as stepwise covariate modelling (SCM). This can be a time-consuming (and processor hungry) process. This method identifies which covariates significantly improve the model.
Describe the concept of using biological and pharmacological prior information to select covariates to test
Instead of a purely statistical approach, covariates may be selected based on existing biological and pharmacological knowledge. This method relies on understanding the mechanism of action of the drug and the known effects of certain biological or demographic factors on drug metabolism and distribution.
For example, in the knowledge that a drug is renally excreted, it would be prudent to test the effect of some measure of kidney function (for example serum creatinine or estimated glomerular filtration rate) on drug clearance.
Describe how plots of model parameters vs variables can be used to identify potential covariates to test in a model:
Plots of model parameters versus potential covariates can visually indicate trends or relationships that may justify including a covariate in a model.
E.g. plotting individual clearance rates against weight can show whether heavier individuals tend to have higher clearance rates.
It can also highlight any inter-individual variability.
Describe the concept of allometric scaling
Pharmacokinetic parameters scale with body size. Therefore, dosing is often weight based.
Allometric scaling is the typical change of pharmacokinetic parameters with body size
Explain how the objective function value (OFV) can aid decisions on whether to retain a covariate in a model
The OFV can be used to test an improvement in model fit. A lower OFV indicates a better model fit.
if you add one parameter to your model and compare the objective function with and without that parameter, a drop in objective function value >3.84 suggests a statistically significant (p<0.05) improvement in model fit.
Functions describing the concentration time course
Structural model: Part of the fixed effects: Clearance, V, Ka.
Covariate model : covariates interact with structural model. covariates can be individual specific characteristics that modify the structure.
Statistical model: describes variability (around the structural model)
