[R] Competing risks Kalbfleisch & Prentice method

[Ravi Varadhan]

Hi Terry,

My "this" was your (a), i.e. the smoothed hazard rate function. 

I apologize if I came across as being rude.  I was only curious to see if you had any scientific/statistical rationale for not including the smoothed hazard option in your "survival" package, which is, by far, the most widely used tool for time-to-event analysis in R.  Therefore, I just felt that having this, fairly useful, capability in "survival" would be nice.  

I have a couple of questions related to your two other points:

point (b):  How would  you estimate the effect of a treatment on the cumulative incidence of primary outcome, adjusted for covariates, using the K&P approach (both point and interval estimation)?

point (c):   I don't quite understand why you find the F&G model completely biologically untenable.  I view it as mathematical trickery to obtain a compact summary of the impact of a covariate on the cumulative incidence.  The F&G model is especially useful in estimating covariate adjusted treatment effect, provided the proportionality assumption on the sub-distribution hazard is reasonable.  The K&P approach does not provide such compactness as you have to model all the cause-specific hazards.  

Best,
Ravi.

____________________________________________________________________

Ravi Varadhan, Ph.D.
Assistant Professor,
Division of Geriatric Medicine and Gerontology
School of Medicine
Johns Hopkins University

Ph. (410) 502-2619
email: rvaradhan at jhmi.edu


----- Original Message -----
From: Terry Therneau <therneau at mayo.edu>
Date: Friday, March 27, 2009 9:52 am
Subject: RE: Competing risks Kalbfleisch & 	Prentice method
To: er339 at medschl.cam.ac.uk, tuechler at gmx.at, Ravi Varadhan <rvaradhan at jhmi.edu>
Cc: r-help at r-project.org


> Ravi's last note finished with
>  >  I am wondering why Terry Therneau's "survival" package doesn't
>  >  have this option.  
>  
>    The short answer is that there are only so many hours in a day.  
>  
>    I've recently moved the code base from an internal Mayo repository 
> to R-forge, 
>  one long term goal with this is to broaden the developer base to n>2 
> (me and 
>  Thomas Lumley).  
>    
>    A longer statistical answer:
>    
>    I'm not sure if the "this" of Ravi's question is a. smoothed 
> hazards, b. the 
>  K&P cumulative incidence or c. the Fine & Gray model.
>    
>    b. I like the CI model and am using it more.  We also have local 
> code. The 
>  latest version of survival (on rforge, likely in the next default R 
> release) has 
>  added simple CI curves to the survfit function.  Adding code for 
> survfit on Cox 
>  models is on the todo list.  But -- this release also fixes up 
> survfit.coxph to 
>  handle weighted Cox models and that was on my list for approx 10 
> years, i.e., 
>  don't hold your breath.  I don't release something until it also has 
> a set of 
>  worked out test cases to add to the 'tests' directory.
>    
>    a. smoothed hazards.  For the case at hand I don't see any 
> particular 
>  advantage of this.  On the other hand, I often would like to display 
> hazard 
>  functions instead of CI functions for Cox models; with time dependent 
> covariates 
>  I don't think a survival curve makes sense.  But I haven't had the 
> time to think 
>  through exactly which methods should be added.
>    
>    c. Fine & Gray model, i.e., where covariates have a direct 
> influence on the 
>  competing risk.  I find the model completely untenable from a 
> biologic point of 
>  view, so have no interest in adding it.  (Due to finite time, 
> everything in the 
>  survival package is code that I needed for an analysis; medical 
> research is what 
>  pays my salary.)  Assume that I have competing processes/risks, say 
> progression 
>  of a tumor and heart disease;  I expect that the tumor process pays 
> no attention 
>  whatsoever to what is going on in the heart.  But this is necessary 
> if 
>  "type=squamous" is modeled as an absolute beta=__ increase in the CI 
> for cancer. 
>   The squamous cells need to "step up the pace" of invasion if heart 
> failure 
>  threatens, like jockeys in a horse race. 
>    
>     Terry T. 
>