Carl Mason --- Director Demography Lab
2001-Dec-21 00:20 UTC
[R] proportional hazard with parametric baseline function: can it be estimated in R
Greetings -- I would like to estimate a proportional hazard model with a weibull or lognormal baseline. I have looked at both the coxph() and survreg() functions and neither appear (to me ) to do it. Am I missing something in the docs or is there another terrific package out there that will do this. Many Thanks. Carl Mason -.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.- r-help mailing list -- Read http://www.ci.tuwien.ac.at/~hornik/R/R-FAQ.html Send "info", "help", or "[un]subscribe" (in the "body", not the subject !) To: r-help-request at stat.math.ethz.ch _._._._._._._._._._._._._._._._._._._._._._._._._._._._._._._._._._._._._._._._
Thomas Lumley
2001-Dec-21 00:48 UTC
[R] proportional hazard with parametric baseline function: can it be estimated in R
On Thu, 20 Dec 2001, Carl Mason --- Director Demography Lab wrote:> > Greetings -- > > I would like to estimate a proportional hazard model with a weibull or > lognormal baseline. I have looked at both the coxph() and survreg() > functions and neither appear (to me ) to do it. Am I missing something in > the docs or is there another terrific package out there that will do this. >survreg() will fit accelerated failure models with Weibull or lognormal baselines. For the Weibull case these are equivalent to proportional hazards models. To convert the accelerated failure coefficients into proportional hazards coefficients one just divides by -estimated scale, which is given in the standard output. Lognormal proportional hazards models are an unusual choice as the lognormal family is not a proportional hazards family. If the distribution is lognormal for all covariates=0 it will not be lognormal at any other covariate value. This means that eg centering the covariates changes the model. Unless censoring is strongly related to your covariates or you want to use the model for extrapolation to longer followup than you observed there is no real advantage to a parametric proportional hazards model, so you might as well use coxph(). -thomas -.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.- r-help mailing list -- Read http://www.ci.tuwien.ac.at/~hornik/R/R-FAQ.html Send "info", "help", or "[un]subscribe" (in the "body", not the subject !) To: r-help-request at stat.math.ethz.ch _._._._._._._._._._._._._._._._._._._._._._._._._._._._._._._._._._._._._._._._
Apparently Analagous Threads
- parametric proportional hazard regression
- Proportional hazard model with weibull baseline hazard
- Parametric survival models with left truncated, right censored data
- Fitting weibull, exponential and lognormal distributions to left-truncated data.
- Interval censored Data in survreg() with zero values!