survreg with measurement uncertainties

Hi Terry,

Thanks for your quick reply.  I am talking about uncertainty in the
response.  I have 2 follow up questions:

1) my understanding from the documentation is that 'id' in cluster(id)
should be the same when the predictors are not independent.  Is this
correct?  (To be more concrete: my data are brightnesses at different
wavelengths.  Each brightness is an independent measurement, so the
elements of id should all be different?)

2) I tested survreg with uncertainties on an example where I already
know the answer (and where I am not using limits), and it does not
converge.  Below is the code I used, does anything jump out as
incorrect?

data = c(144.53, 1687.68, 5397.91)
err = c(8.32, 471.22, 796.67)
model = c(71.60, 859.23, 1699.19)
id = c(1, 2, 3)

This works (2.9 is the answer from simple chi_sq fitting):

survreg(Surv(time = data, event = c(1,1,1))~model-1, dist='gaussian',
init=c(2.9))

This does not converge (2.1 is the answer from chi_sq fitting):

survreg(Surv(time = data, event = c(1,1,1))~model-1+cluster(id),
weights=1/(err^2), dist='gaussian', init=c(2.1))

And this does, but the answer it returns is wonky:

data[2] = 3*err[2] # data[2] is very close to 3*err[2] already
survreg(Surv(time = data, event = c(1,2,1))~model-1+cluster(id),
weights=1/(err^2), dist='gaussian', init=c(2.1))

Thanks,

Kyle

I will assume that you are talking about uncertainty in the response.  Then
one simple way to fit the model is to use case weights that are proprional
to 1/variance, along with +cluster(id) in the model statement to get a
correct variance for this case.  In linear models this would be called the
"White" or "Horvitz-Thompsen" or "GEE working independence" variance
estimate, depending on which literature you happen to be reading (economics,
survey sampling, or biostat).

Now if you are talking about errors in the predictor variables, that is a
much harder problem.

Terry Therneau



On 06/12/2013 05:00 AM, Kyle Penner wrote:

Hello,

I have some measurements that I am trying to fit a model to.  I also
have uncertainties for these measurements.  Some of the measurements
are not well detected, so I'd like to use a limit instead of the
actual measurement.  (I am always dealing with upper limits, i.e. left
censored data.)

I have successfully run survreg using the combination of well detected
measurements and limits, but I would like to include the measurement
uncertainty (for the well detected measurements) in the fitting.  As
far as I can tell, survreg doesn't support this.  Does anyone have a
suggestion for how to accomplish this?

Thanks,

Kyle