I'm hoping someone can help me with a data problem (Nik sorry I emailed
you directly rather than putting on the list).
A person I work with was able to calculate this in SAS and I'm hopefully
able to do the same in STATA. I'm trying to use stata to calculate a
time in range for blood pressure data
here is what my data looks like
------------------------------
-----------------------------------------------------------------
Pt_ID Appt_date appt_status bp_systolic
1 21 Apr 05 No Show
1 07 Jun 05 Completed 140
1 11 Jan 06 Completed 150
1 16 Feb 06 Completed 150
1 30 Mar 06 Canceled
1 02 May 06 Canceled
1 09 May 06 Completed 138
1 11 Jul 06 Completed 142
1 19 Sep 06 Completed 150
2 30 May 06 No Show
2 11 Jul 06 Completed 176
2 25 Jul 06 Completed 121
2 01 Aug 06 Canceled
2 01 Aug 06 Completed 101
2 15 Aug 06 Completed 118
2 22 Aug 06 Canceled
2 12 Sep 06 Completed 136
2 14 Nov 06 Completed 137
2 16 Jan 07 Completed 113
2 06 Feb 07 No Show
2 03 May 06 Canceled
2 25 Jul 06 Canceled
2 01 Aug 06 Canceled
2 07 Feb 07 Canceled
-------------------------------------------------------------------------------------------------------
Basically I assume a linear slope (interpolated) between successive
dates, set a threshold, then ask STATA to calculate the proportion of
total observation time that the curve was above my threshold.
I think this falls under the cross sectional time series definition.
Basically what I'm trying to do is each individual (where iis Pt_ID) has
a unique observation time (tis Appt_date) and a set of observed systolic
blood pressures (bp_systolic). There are no set observation times, this
is observational data. For each time, I assume a linear slope of blood
pressure change to the next observation time. I need to calculate the
area that the slope measure predicts blood pressure over my threshold
(say 140 for example). Slopes can be positive or negative or flat.
Each individual has a variable number of observations, they are not
equally spaced, and missing data is a possibility.
Andrew O'Connor, DO, MPH
Division of Nephrology
Case Western Reserve University
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