[R] survreg with measurement uncertainties

Kyle Penner kpenner at as.arizona.edu
Wed Jun 12 21:48:40 CEST 2013

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

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',

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))



On Wed, Jun 12, 2013 at 6:51 AM, Terry Therneau <therneau at mayo.edu> wrote:
> 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

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