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st: cumulative probabilities under competing risks with covariateadjustment


From   Uri Goldbourt <goldbu1@post.tau.ac.il>
To   statalist@hsphsun2.harvard.edu
Subject   st: cumulative probabilities under competing risks with covariateadjustment
Date   Thu, 13 Mar 2008 11:45:15 +0200

Dear Enzo Coviello,

 

I believe you are the one who co-wrote stcompet which provides analytical possibilities crucial in studies with along follow up and competing risks, resulting in cumulative probabilities that could be quite different from those obtained ignoring the existence of competing risks.

In the last North American Stata users meeting in Boston,  Mr. Guttierez who represented STATA seemed to include my proposal to complete the routine, by adding adjustment for covariates, in the tasks that stata will pursue. Or so it seemed

I have not heard from him since nor noticed that anything.

 

Do you know of any plan to advance this matter? This has enormous importance in analysis of the old cohort studies where long-term follow up of mortality and/or the incidence of dementia have been instituted and relating them to mid-life levels of risk factors is sought.

 

Many thanks for your attention,

 

Uri Goldbourt

Department of Epidemiology and Preventive medicine

Tel Aviv University

 

-----Original Message-----
From: owner-statalist@hsphsun2.harvard.edu [mailto:owner-statalist@hsphsun2.harvard.edu] On Behalf Of Enzo Coviello
Sent: Monday, March 10, 2008 5:50 PM
To: statalist@hsphsun2.harvard.edu
Subject: Re: st: simulation of survival datasets in Stata

 

I would add to the very useful reply of Paul a

reference whose reading my be of some practical help:

 

-Generating survival times to simulate Cox proportional hazards model-

Stat in Med 2005; 24: 1713-1723

 

Best wishes.

 

Enzo

 

 

At 13.20 10/03/2008, you wrote:

>You need to simulate time-to-event followed by

>censoring.  The easiest is exponential or Weibull.

>Weibull is :

>S=exp(-lambda*t^p)

>where lambda is the hazard ratio:

>lambda=exp(sum beta x)

> 

>beta_0 is a constant reflecting the baseline

>hazard.  beta_1 ... reflect the effects of covariates.

>p is the Weibull shape parameter (positive

>number), and if valued p=1 reduces to exponential.

> 

>Generate lambda with hazard ratio 1.9 for drug treatment (0,1):

> 

>- lambda=-2.5+ln(1.9)*drug -

> 

>To simulate time-to-event, substitute the random

>unit variate R for S, and solve for t.

>t=(-ln(R)/lambda)^(1/p)

> 

>or in Stata:

> 

>- gen t=(-ln(uniform())/lambda)^(1/p) -

> 

>Some of the t values will be very large, and you

>will need a censoring variable to reflect the end of study (say 90 days).

>- gen fail=1 -

>- replace fail=0 if t>90 -

>- replace t=90 if t>90 -

> 

> 

>Then, look at your handiwork-

>- stset t, fail(fail) -

>- sts graph, by(drug) -

>- stcox drug -

> 

>Paul

> 

> 

> 

> 

> 

> 

> 

>Michael McCulloch wrote:

>>Hello Statalist members,

>>I'm seeking examples of simulation of survival

>>datasets in Stata. Can anyone point me in the

>>right direction? My goal is to simulate a

>>dataset to validate a Stata survival program I've written.

>> 

>>Thank you.

> 

>--

>E. Paul Wileyto, Ph.D.

>Assistant Professor of Biostatistics

>Tobacco Use Research Center

>School of Medicine, U. of Pennsylvania

>3535 Market Street, Suite 4100

>Philadelphia, PA  19104-3309

> 

>215-746-7147

>Fax: 215-746-7140

>epw@mail.med.upenn.edu

>http://mail.med.upenn.edu/~epw/

>*

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Enzo Coviello

Unità di Epidemiologia e Statistica ASL BARI

Piazza V.  Emanuele 14

70054 Giovinazzo (Bari)

Italy

tel./fax +39 080 3357867

mobile +39 347 5016 016

home +39 0883 695055

 

 

*

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