# st: Analysis of Individual event data

 From Paul Seed <[email protected]> To [email protected] Subject st: Analysis of Individual event data Date Fri, 17 Jun 2005 17:34:28 +0100

Dear all,

I have a data set that consists of the dates of certain arbitrary events
(hospital appointments), together with various predictors; mainly
date, numeric code for referring doctor (GP) & intervention (pre/post ).
I am interested in whether the intervention is associated with a fall in
the monthly rate of appointments, after adjusting for secular trend
and clustering by GP.

One approach is to collapse the data & perform a Poisson or negative
binomial regression. However, the code needed for this is surprisingly
complex (see below), as I need to ensure that the practices with zero frequencies in
one time period are properly included, and the only way I could see to do this
was to tabulate, save the table as a matrix and then convert it to a data set.

Two questions:
1) Is there a simpler way of collapsing the data without the zero problem
2) Is there a way of analysing the data using it's present structure (or something like it)?

. use "westminster2", clear

. li uniqid aptdate apttype pre_ post_ in 1/5

+----------------------------------------------+
| uniqid aptdate apttype pre_ post_ |
|----------------------------------------------|
1. | 6220 11 Apr 02 first vi 0 1 |
2. | 6159 12 Mar 02 first vi 0 1 |
3. | 6175 21 Mar 02 first vi 0 1 |
4. | 6210 09 Apr 02 first vi 0 1 |
5. | 6165 14 Mar 02 first vi 0 1 |
+----------------------------------------------+

tempname temp

tab refcode prepost if hosp == 1, matcell(temp) matrow(gp_ref_cd)

svmat temp, names(col)
svmat gp_ref_cd ,names(gp_ref_cd)

keep c1 c2 gp_ref_cd
rename gp_ref_cd1 gp_ref_cd
drop if gp_ref_cd >=.

reshape long c, i(gp_ref_cd) j(prepost)
rename c n_appt
gen post = prepost == 2

svyset , psu(gp_ref_cd)
svynbreg n_appt post , irr

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