Regression model fitting

Hi all ,


I have a dataframe (Hypertension) with following headers :-

Hypertension

ID   Hypertension(before drug A)      Hypertension(On drug A)        On drug B?      Healthy diet?

1            160                                                           90                                           True                  True

2            190                                                          140                                          False                 False

3             170                                                          110                                         True                  False


I wanted to study whether patients on drug A + on drug B + on healthy diet would have better

blood pressure control (reduction) compared to patients with drug A but not on drug B or not on healthy diet or not on both.


I considered my outcome(y) variable to be hypertension measurements for all patients

on drug A (column 2).  Columns 1,3 and 4  are my explanatory(x) variables variables(column 1 is just the baseline measurements to adjust for the effect of drug A compared to the baseline) . So my regression formula using lm() function in R is as follow :-


Regression <- lm (formula= Hypertension(On drug A)~Hypertension(before drug A) +

(On drug B?*Healthy diet?))  , data = Hypertension)


I expect that the result of "(On drug B?*Healthy diet?)" coefficient in the model would give the correct answer to my question.

Is this the best formula to answer my question or there would be better methods ?.


Regards

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