Message-ID: <alpine.LFD.2.20.1805041957440.1703@localhost>
Date: 2018-05-04T18:00:47Z
From: Eivind K. Dovik
Subject: Regression model fitting
In-Reply-To: <DB5PR02MB0904BA32D25EFC486293413280860@DB5PR02MB0904.eurprd02.prod.outlook.com>
On Fri, 4 May 2018, Allaisone 1 wrote:
>
> Hi all ,
>
>
> I have a dataframe (Hypertension) with following headers :-
>
>
>> Hypertension
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> ID Hypertension(before drug A) Hypertension(On drug A) On drug B? Healthy diet?
>
> 1 160 90 True True
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> 2 190 140 False False
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> 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) +
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> (On drug B?*Healthy diet?)) , data = Hypertension)
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>
> I expect that the result of "(On drug B?*Healthy diet?)" coefficient in the model would give the correct answer to my question.
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> Is this the best formula to answer my question or there would be better methods ?.
>
>
> Regards
>
Hi,
Could you provide R-code that generates your data.frame?
Eivind
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> [[alternative HTML version deleted]]
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