Hi all,
I have a question about calculating a P for trend on my data. Let?s give
an example that is similar to my own situation first: I have a continuous
outcome, namely BMI. I want to investigate the effect of a specific
medicine, let?s call it MedA on BMI. MedA is a variable that is
categorical, coded as yes/no use of the medication. A also have the
duration of use of the MedA, divided in three categories: use of MedA for
1-30 days, use of MedA for 31-60 days and use of MedA for 61-120 days
(categories based on literature). I have performed a linear regression
analyses and it seems like there is some kind of trend: the longer the use
of MedA, the higher the BMI will be (the betas increase with time of use).
So an exemplary table:
Outcome: BMI
Beta
MedA use duration
Use for 1-30 days
0.060
Use for 31-60 days
0.074
Use for 61-120 da
0.081
So, I have created three variables and I modelled them in Rstudio (on a
multiple imputed dataset using MICE):
mod1 <- with(imp, lm(BMI ~ MedA_1to30))
pool_ mod1 <- pool(mod1)
summary(pool_ mod1, conf.int = TRUE)
mod2 <- with(imp, lm(BMI ~ MedA_31to60))
pool_ mod2 <- pool(mod2)
summary(pool_ mod2, conf.int = TRUE)
mod3 <- with(imp, lm(BMI ~ MedA_61to120))
pool_ mod3 <- pool(mod3)
summary(pool_ mod3, conf.int = TRUE)
Now that I have done this, I want to calculate a p for trend. I do know
what a P for trend measures, but I do not know how to calculate this
myself. I read something about the partial.cor.trend.test() function from
the trend package, but I do not know what I should fill in. Because I can
only fill in an x and y, but I have three time variables. So I do not know
how to solve this. Can somebody help me?
If more information is necessary, I am happy to give it to you!
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