Notice: On April 23, 2014, Statalist moved from an email list to a forum, based at statalist.org.

[Date Prev][Date Next][Thread Prev][Thread Next][Date Index][Thread Index]

From |
Ciril Bosch <[email protected]> |

To |
[email protected] |

Subject |
st: st: Interval Censored Data Formatting and Intcens |

Date |
Mon, 4 Jun 2012 11:08:44 -0700 |

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 <[email protected]> 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 > [email protected] > > 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. * * For searches and help try: * http://www.stata.com/help.cgi?search * http://www.stata.com/support/statalist/faq * http://www.ats.ucla.edu/stat/stata/

**Follow-Ups**:**Re: st: st: Interval Censored Data Formatting and Intcens***From:*Steve Samuels <[email protected]>

- Prev by Date:
**Re: st: instrumenting Moving average variable** - Next by Date:
**Re: st: st: Handling age in Hazard Ratios** - Previous by thread:
**st: Count if binomial variable changes** - Next by thread:
**Re: st: st: Interval Censored Data Formatting and Intcens** - Index(es):