I have the following data:
- A random group of patients with a certain disease who receive a drug at a certain time.
- Dose of the drug given to the patient. This drug has an affect to the heart which puts the patient to the risk of cardio attack.
- Four parameters ($p_1,...,p_4$) that are measured before and after the drug is given to the patient. These parameters determine patient's condition.
- General information about the patients (age, BMI, etc.).
- A binary variable indicating whether the patient survived or not.
The data looks like this:
d1 = data.frame(age= sample(20:50,5), BMI = rnorm(5,20,40),p1_pre= rnorm(5,0.5,2),p2_pre=rnorm(5,0.5,2), p3_pre=rnorm(5,0.5,2),p4_pre=rnorm(5,0.5,2),p1_post=rnorm(5,0.5,2),p2_post=rnorm(5,0.5,2), p3_post=rnorm(5,0.5,2),p4_post=rnorm(5,0.5,2),survived=c(1,1,0,0,1))
Current approach: the current believe is that only one of the four parameters, (in item 3 of the above list; let’s call it p1) is an important indicator for knowing the patient's condition who has taken the drug is bad and therefore we should stop giving him/her the drug.
So the practice is that p1 is calculated before and after the treatment and if the value of p1 falls below an acceptable value, the drug is stopped
Hypothesis: the hypothesis is that 𝑝1 is not the only indicator, but the other 3 parameters (in item 3 of the above list) are also important indicators for knowing the patient's condition after taking the drug.
Question: What is the best way to study this data? how can I use the general information of the patients and the binary outcome?
Any help is greatly appreciated.
Thanks in advance.