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 |
Maarten Buis <maartenlbuis@gmail.com> |

To |
statalist@hsphsun2.harvard.edu |

Subject |
Re: st: parametric survival analysis - choosing the probability distribution |

Date |
Wed, 8 Jun 2011 11:58:11 +0200 |

On Tue, Jun 7, 2011 at 10:56 PM, Magdalena Kapelko wrote: > Can you suggest me a way to solve the problem that the proportionality > assumption for cox model does not hold? Is there any other model I can > choose from? The proportional hazard function means that at every point in time the hazard in one group is always the same factor bigger or smaller than the hazard in the comparison group. These ratios are the hazard ratios you get in the output of -stcox-. When this assumption fails it means that these ratios aren't constant over time. You can include such interactions with time in your -stcox- model using the -tvc()- and -texp()- options. Often it is enough to split time into two groups and get an "early hazard ratio" and a "late hazard ratio" for only a few variables. Consider the example below. The -tvc()- option says which variable is allowed to change over time, and the -texp()- specifies the functional form of time for this interaction. In this case I used -_t>12-, which is a logical statement so if this statement is true it gets a 1 otherwise a 0. So this is an indicator/dummy variable indicating whether or not one year has passed (in this example time is measured in months). In the -tvc()- option you can see that I let the variable age change by time and I also add the main effect of age to the model. So, the main effect of age is the effect of age before 1 year passed and the interaction effect indicates the factor change in the main effect after one year. So getting a year older increases the hazard of death by a factor of 1.11 (i.e. 11%) in the first year after getting ill, while this effect of age increases by a factor of 1.04 (i.e. 4%) after the first year of getting ill. So the effect of age after one year is 1.04*1.11= 1.15. Notice that these hazard ratios (and ratio of hazard ratios for the interaction effect) are the exponentiated coefficients. So we can get the hazard after one year by summing the coefficients and exponentiating the result, this is what the -lincom- command with the -hr- option does. *----------------- begin example ------------------ sysuse cancer, clear stset studytime, failure(died) stcox i.drug age, tvc(age) texp(_t>12) // hazard ratio after 1 year lincom _b[main:age] + _b[tvc:age], hr *---------------- end example -------------------- (For more on examples I sent to the Statalist see: http://www.maartenbuis.nl/example_faq ) Hope this helps, Maarten -------------------------- Maarten L. Buis Institut fuer Soziologie Universitaet Tuebingen Wilhelmstrasse 36 72074 Tuebingen Germany http://www.maartenbuis.nl -------------------------- * * 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/

**References**:**st: parametric survival analysis - choosing the probability distribution***From:*Magdalena Kapelko <magdalena.kapelko@gmail.com>

**Re: st: parametric survival analysis - choosing the probability distribution***From:*Maarten Buis <maartenlbuis@gmail.com>

**Re: st: parametric survival analysis - choosing the probability distribution***From:*Magdalena Kapelko <magdalena.kapelko@gmail.com>

- Prev by Date:
**Re: st: imposing cross-equation constrains with nlsur** - Next by Date:
**Re: st: hosmer lemshow goodness of fit statistics** - Previous by thread:
**Re: st: parametric survival analysis - choosing the probability distribution** - Next by thread:
**st: proportional hazard assumption in parametric survival models** - Index(es):