[Date Prev][Date Next][Thread Prev][Thread Next][Date index][Thread index]

From |
Ronan Conroy <rconroy@rcsi.ie> |

To |
statalist@hsphsun2.harvard.edu |

Subject |
Re: st: comparing survival models: Cox vs AFT |

Date |
Thu, 22 Aug 2002 10:52:12 +0100 |

on 22/8/02 7:48 AM, Moran, John (NWAHS) at John.Moran@nwahs.sa.gov.au wrote: > I am not quite sure as to direction here; any advice would be most welcome. > > I have a multi-record per patient survival data set with 28 day (from acute > diagnosis) mortality as the outcome. A Cox model with (significant) > time-varying covariates gives a "good" fit , by conventional means (residual > analysis etc) . > > A log normal AFT model (parameterized in the time-ratio sense) seems to do a > "good" job as well (again, by conventional diagnostics). The shape of the > baseline Cox model hazard (using stkerhaz, recently posted) certainly has a > log-normal profile. With 28-day survival, you will have complete data (or you will have in less than a month...). For this reason, you might consider logistic regression or, indeed, -binreg- as the first options. With 28-day survival, the shape of the survival distribution is generally of little interest (I am guessing that this is something like acute coronary syndrome, where there is a significant hazard in the first 28 days). Logistic regression allows you to estimate the effects of risk factors as odds ratios. -binreg-, on the other hand, will try to estimate risk ratios, which are easier to interpret, since a risk ratio is simply the ratio of two probabilities, but you aren't guaranteed that any model will converge. Both Cox regression and AFT models can give you hazard ratios, which are also useful measures of the effect of risk factors, though harder to explain properly than risk ratios. The advantage of AFT models, and other parametric approaches such as fractional polynomials, is that you can characterise the shape of the hazard function. Cox regression, on the other hand, treats the shape as a high dimensional nuisance parameter - something that just has to be got out of the way before we do the interesting work parametrising the risk factors. In general, if you are interested in factors which predict outcome, I would go for simple binary models using -logistic- or -binreg- and the hell with the shape of the survival function. If the survival function's shape is actually interesting, then parametric approaches allow you to characterise it, while Cox regression simply takes it as a given, so I would opt first for simple parametric methods, and then investigate the gain from using something like fractional polynomials. I would beware of making a model that is more complex than the underlying theory! Ronan M Conroy (rconroy@rcsi.ie) Lecturer in Biostatistics Royal College of Surgeons Dublin 2, Ireland +353 1 402 2431 (fax 2329) -------------------- Too busy fighting terror to worry about the planet? Gosh! It's hard being President... * * For searches and help try: * http://www.stata.com/support/faqs/res/findit.html * http://www.stata.com/support/statalist/faq * http://www.ats.ucla.edu/stat/stata/

**References**:**st: comparing survival models: Cox vs AFT***From:*"Moran, John (NWAHS)" <John.Moran@nwahs.sa.gov.au>

- Prev by Date:
**st: comparing survival models: Cox vs AFT** - Next by Date:
**st: Problem with genhw...** - Previous by thread:
**st: comparing survival models: Cox vs AFT** - Next by thread:
**RE: st: comparing survival models: Cox vs AFT** - Index(es):

© Copyright 1996–2017 StataCorp LLC | Terms of use | Privacy | Contact us | What's new | Site index |