st: st: Interval Censored Data Formatting and Intcens

[Ciril Bosch <cirilbosch@gmail.com>]

Hi Steve,
thank you for your reply, I really appreciate it. Indeed intcens comes from SSC.

My data is in a wide format and reads:

id dose event session

Where session stands for ordered session when doses where applied

In my example:

id   dose   event   session
1      13       1          1
2      13       0          2
3      21       1          1
4      21       1          2

My example might have induced to confusion as I was unclear in my
specification:

1) I don't actually apply two dosages to subjects. The t_0==1 and
t_1==3 that I have for all subjects is actually a "dummy treatment"
which never took place. I used it as a primitive way of telling Stata
that there is left censoring and that it should take that into account
(I do know that
dose==3 will always have event==0 for all subjects).

2) Treatments are completely independent across subjects even if they
are dosed in "batches". A dummy for session will be included in the
analysis, but this is just for the sake of completeness (or so I
hope!).

So as you can see my design cannot be simpler; select a small set of
doses to apply and then observe results on a series of subjects.

I will try the code you sent once I get onto my computer and see if I
can run it with intcens.

Again, thanks for your help,
Ciril

On Sat, Jun 2, 2012 at 6:17 PM, Steve Samuels <sjsamuels@gmail.com> wrote:

> You are asked in the FAQ to say where you obtained unofficial commands.
> -intcens- can be downloaded from SSC.
>
> You say you want to transfer the structure of your data to Stata, but
> you don't show that structure, so we have to guess. I'm guessing that
> your basic data looked something like that after the input statement
> below ( "wide form").
> If not, it might look something like the listed data after the
> -reshape- command (below), the "long form" If not that either, then
> you will have to put  your data into one of the two forms.
>
> For the wide form: let i = 1,3,13, 21 index coses and let statusi =
> observation just after dose i
>
> = 1 if death after dose i
> = 0 if no death after dose i
> = . no dose i given
>
> *************CODE BEGINS*************
> input id status1 status3 status13 status21
> 1 0 1 . .
> 2 0 0 1 .
> 3 0 0 0 1
> 4 0 0 . 1
> 5 0 . . 0
> end
>
> reshape long status, i(id) j(dose)
> drop if status==.
> list, sepby(id)  // long form
>
> egen event = max(status), by(id)
> gen t1 = dose if status==1
>
> bysort id: gen t0 = dose[_n-1] if status==1 bysort id: keep if _n==_N
> replace t0 = dose if t0==.
> list id t0 t1 status
> ***********CODE ENDS***************
>
> This is an interesting application of -intcens-, but two points
> concern me.
>
> 1. You apparently applied 2+
> doses to the each subject. If the effect of former dosing persists in
> any way, then the probability of an event will not depend only on
> current dose.
>
> 2. Dosing in groups could result in dependent responses.
>
> I am curious: Can you provide a methodological reference for this
> design?
>
> Steve
> sjsamuels@gmail.com
>
> On Jun 1, 2012, at 5:12 PM, Ciril Bosch-Rosa wrote:
>
> Hello,
> I am trying to apply survival analysis in an environment where I can
> gather data in a very limited way. I have been looking at literature
> on how to manage the data but am not being successful at transmitting
> the structure of my data to Stata.
>
> It turns out that my data is very limited because I can only observe
> once in time each subject. I am taking this as an interval censored
> problem, where all subjects start being at risk at t_0 and then are
> censored until I look at some of them at (say) t_13, where they are
> either 1 (dead) or 0 (right censored). So, either the subject died at
> some before (and including) t_13 or he survived in which case I will
> never see him again (hence the right censoring).
>
> I looked at Cleves et al. but they are not clear on how I can tell
> Stata that an even happened while "I was not looking". They tell me
> how to tell stata I was not looking, but not how to tell I was not
> looking & something
> happened:
>
> id   t_0   t_1   event
> 1     1      3      0
> 1     13    15    1
>
> the example above I tell stata there is a censored interval from t=3
> until
> t=13 and then I observe id=1 until he gets a failure at t=15, but I am
> not able to tell stata that they might have died in the t3-t13 interval:
>
> id   t_0   t_1   event
> 1     1      3      0
> 1     13    13    1
> 2     1      3      0
> 2     13    13     0
>
> In other words I observe you from t=1 to t=3 and then you get censored
> until I get one  peek at t=13, where I can see if you died between 3
> and 13 or not.
>
> I believe this can be done with ""intcens  depvar1 depvar2 " where
> depvar are my t=3 and t=13 respectively, my problem though is that
> there are different interval censoring for different subjects
>
> id   t_0   t_1   event
> 1     1      3      0
> 1     13    13    1
> 2     1      3      0
> 2     13    13     0
> 3     1      3      0
> 3     21    21    1
> 4     1      3      0
> 4     21    21     1
>
>
> how can I tell intcens, "intcens depvar1 depvar2 if id=1|2 but depvar1
> depvar3 if id=3|4" ??
>
> Any help/advice is welcome
>
> Ciril
>
> PS If you are wondering in what environment I get this kind of data;
> this is a lab experiment where I apply a dose to groups of subjects
> and get instantaneous results, hence my observing groups of them at
> the same "time".
> My X-axis is not time, but rather "dosage" and I am trying to plot the
> survivor curve given the very limited observable outcomes for each dosage.
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