Dear Statalist users,

I am setting up a survival model, where I look at the farmers' time to adoption of a certain technology. I am having difficulties matching up my data structure with conventional examples such as STB-49 (p. 38) by Mario Cleves, and I have the feeling I am loosing important information (see example below where I drop strata == .). Given that my research subjects experience the adoption decision every single year, I wonder if I should use an observation for each year, rather than periods of adoption/non-adoption.

Initially, I've arranged my data so that continuous years of non-adoption (i.e. status = 0) are clustered as periods of time, and the same procedure for years of adoption (i.e. status = 1). As I have information for up to 17 years for some individuals, I've determined to use a conditional risk set model (gap model) to account for dis-adoption (i.e. status = 0) and re-adoption (i.e. status = 1) events. I've labeled as "strata" changes in adoption behaviour (i.e. change of mind) regarding technology adoption.

I am using Stata version 15.1.

The data looks as follows:

Code:
* Example generated by -dataex-. To install: ssc install dataex
clear
input int idd float tt byte(time0 gaptimeg status strata)
2 17 0  9 0 1
2 17 0  . 0 .
2 17 0  . 0 .
2 17 0  . 0 .
2 17 0  . 0 .
2 17 0  . 0 .
2 17 0  . 0 .
2 17 0  . 0 .
2 17 0  . 0 .
2 17 0  8 1 2
2 17 0  . 1 .
2 17 0  . 1 .
2 17 0  . 1 .
2 17 0  . 1 .
2 17 0  . 1 .
2 17 0  . 1 .
2 17 0  . 1 .
3 11 0 11 1 1
3 11 0  . 1 .
3 11 0  . 1 .
3 11 0  . 1 .
3 11 0  . 1 .
3 11 0  . 1 .
3 11 0  . 1 .
3 11 0  . 1 .
3 11 0  . 1 .
3 11 0  . 1 .
3 11 0  . 1 .
4  6 0  1 0 1
4  6 0  5 1 2
4  6 0  . 1 .
4  6 0  . 1 .
4  6 0  . 1 .
4  6 0  . 1 .
5  6 0  6 0 1
5  6 0  . 0 .
5  6 0  . 0 .
5  6 0  . 0 .
5  6 0  . 0 .
5  6 0  . 0 .
end
And the coding looks as follows:

Code:
drop if strata == .
(4,226 observations deleted)

stset gaptimeg, fail(status) exit(tt) enter(time0)

 failure event:  status != 0 & status < .
obs. time interval:  (0, gaptimeg]
 enter on or after:  time time0
 exit on or before:  time tt

------------------------------------------------------------------------------
        797  total observations
          0  exclusions
------------------------------------------------------------------------------
        797  observations remaining, representing
        317  failures in single-record/single-failure data
      5,013  total analysis time at risk and under observation
                                                at risk from t =         0
                                     earliest observed entry t =         0
                                          last observed exit t =        17
Any suggestions would be much appreciated.

Jesus P.