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From |
Angus Campbell <a.campbell@unimelb.edu.au> |

To |
statalist@hsphsun2.harvard.edu |

Subject |
st: modelling asynchronous interval-censored data with time-varyingcovariates |

Date |
Fri, 27 Aug 2004 16:32:00 +1000 |

Dear Stata-listers,

I inadvertantly left the subject off this post when I put it up 2 days ago, so am re-posting in case it was overlooked.

I am attempting survival analysis of asynchronously interval-censored data. Some of my covariates start out equal to zero (before the subjects acquire particular attributes) but at later, different, times take on a value which then remains constant. I've used -stsplit- to set this up. I would like to analyse this time-varying characteristic in the model if possible.

I can find techniques that handle interval censored data, or time-varying covariates, but not both. The asynchronous interval censoring seems to be the sticking point. I have tried using -stpm- but can't make it work with the stsplit data if I also try to specify interval-censoring.

Here is an example of a 'fail' & 'non-fail' record:

. st

-> stset dayssurv, id(record) failure(fail)

id: record

failure event: fail != 0 & fail < .

obs. time interval: (dayssurv[_n-1], dayssurv]

exit on or before: failure

. list record _t0 _t _d dayssurv fail left invwwt gr2t if record ==1 | record ==801, noobs

+------------------------------------------------------------------+

| record _t0 _t _d dayssurv fail left invwwt gr2t |

|------------------------------------------------------------------|

| 1 0 60 0 60 . 60 .048 0 |

| 1 60 100 0 100 . 100 .048 0 |

| 1 100 150 0 150 . 150 .048 0 |

| 1 150 180 0 180 . 180 .048 .051 |

| 1 180 210 0 210 . 210 .048 .051 |

| 1 210 288 0 288 0 288 .048 .051 |

|------------------------------------------------------------------|

| 801 0 60 0 60 . 60 .364 0 |

| 801 60 100 0 100 . 100 .364 0 |

| 801 100 143 1 143 1 100 .364 0 |

+------------------------------------------------------------------+

* 'invwwt' isn't time-varying; gr2t is; 'left' == _t if no failure within the interval; left == _t0 if failure occurs within the interval

. xi: stpm invwwt gr1 gr2t gr3t i.sex, left(left) scale(hazard) df(2)

i.sex _Isex_0-100 (naturally coded; _Isex_1 omitted)

cannot have both interval censoring and late entry

r(198);

Is anyone aware of techniques I can use in Stata to model this kind of situation, please?

Thanks in advance for any thoughts,

Angus Campbell

---------------------------------

Dr Angus Campbell

The Mackinnon Project

Faculty of Veterinary Science, University of Melbourne

250 Princes Highway

Werribee, Victoria, Australia

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