Hi,
I am working with a study where we expect an exponential decay relationship between increasing dose and a pharmacodynamic response variable of interest. An initial idea was therefore to model this using an exponential regression model on the form
y = ab x
where b is the parameter of interest to estimate (x is the dose). This could be done fairly easily, since it transforms to linear model on log-scale. However, there are also repeated measurements per patient, since the same patient is measured at different dose levels. To avoid a potential problem of correlated data points in the model, a proposal is to
1) fit the model separately for each patient and
2) Retrieve all estimates of b and
3) Perform a t-test of the values of b to investigate if mean of b is below 1 (proving a dose-dependent response in Y).
However, while this seems to solve the problem of correlated data points, I am doubtful if it is the most efficient/powerful approach to analyse the data. Some patients have very few datapoints (as few as 3), so it does not seem very robust to me to analyse all of them separately. I am contemplating whether the current model could be extended instead with a mixed effect for patient, something like this:
y = apb x
where p is the random effect for patient, allowing observations to be correlated within the same individual.
Would this be a viable alternative? Which approach is preferable? Do I need to extend the modelling approach even further allowing for a random coefficient model?
Any input and ideas would be helpful.
Kind Regards,
JoakimE
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I am using proc transpose to change the table from wide to long. But in the column "old-column" the old some variable names are truncated. What can I do, to get them in the original length?
proc transpose data=have
name =old_column
out=want
by column_to_keep;
run;
Thanks,
Markus
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SAS 9 Content Assessment 2024.05 Released! Enhancements include the following:
The CommonRootPath variable has been removed from the SAS Content Assessment data model.
For the publish application, the --encrypt-sas option has been renamed --encode-sas for clarity.
A variable named objectId was added to the SAS Content Assessment data model.
The application usage application now supports SHA3-224 obfuscation.
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