I have a series of intraoperative blood pressures that are repeated 5 times.
Comparing each pressure to a gold standard at that time.
Besides comparing each type of measure to the gold standard at each specific time point, is there a way to put all the repeated measures into one analysis so we can get an overall estimate of the precision accounting all repeated measures?
There is no reason to think that there is an effect of time on the differences in blood pressures .
Ok, here is a dataset with fake numbers. I cant share any part of the original database.
I have 5 different ways of measuring the blood pressure. One is a gold standard (arterial line). One is a device, and the others are cuffs at different locations. I'm just using mean arterial pressures here.
These are taken intra-operatively in a sample of patents before an event. Then repeated 5 times .
Ive done Bland Altman Plots, Lin's concordance coefficient, and ran a linear regression with all the Blood Pressure parameters measured each time within one model comparing to each other.
What I don't have is a way to include all the data within one model comparing the precision and bias of all the measures at each repeated time point to the gold standard. If I just take a mean of each measure at each time point, then we lose the granularity of the data at each time point.
The Bland Altman tests and Lin's Con concordance coefficient do a great job of comparing MAP measures to the MAP arterial line, but only for specified times where data are measured.
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