[R] Survival analysis MLE gives NA or enormous standard errors

Fri Jul 23 23:40:27 CEST 2010

On Fri, 23 Jul 2010, Christopher David Desjardins wrote:

> Sorry. I should have included some data. I've attached a subset of my
> data (50/192) cases in a Rdata file and have pasted it below.
>
> Running anova I get the following:
>
>> anova(sr.reg.s4.nore)
>                   Df Deviance Resid. Df    -2*LL P(>|Chi|)
> NULL               NA       NA        45 33.89752        NA
> as.factor(lifedxm)  2 2.438211        43 31.45931 0.2954943
>
> That would just be an omnibus test right and should that first NULL NA
> line be worrisome? What if I want to test specifically that CONTROL and
> BIPOLAR were different and that MAJOR DEPRESSION and BIPOLAR were
> different?

Construct a likelikehood ratio test for each hypothesis by fitting three 
models - two containing each term  and one containing both - and comparing 
each simpler model to the fuller model.

>
> I'll look at Hauck-Donner effect.
>
> Thanks,
> Chris
>
>> bip.surv.s
>   age_sym4 sym4 lifedxm
> 1  16.12868    0   MAJOR
> 2  19.32649    0   MAJOR
> 3  16.55031    0 CONTROL
> 4  19.36756    0 CONTROL
> 5  16.09035    0   MAJOR
> 6  21.50582    0   MAJOR
> 7  16.36140    0   MAJOR
> 8  20.57221    0   MAJOR
> 9  16.45722    0 CONTROL
> 10 19.94524    0 CONTROL
> 11 15.79192    0   MAJOR
> 12 20.76660    0   MAJOR
> 13 16.15058    0 BIPOLAR
> 14 19.25804    0 BIPOLAR
> 15 17.36345    0   MAJOR
> 16 21.18001    0   MAJOR
> 17       NA    0 BIPOLAR
> 18       NA    0 BIPOLAR
> 19 16.31759    1   MAJOR
> 20 18.29706    0   MAJOR
> 21 16.40794    0   MAJOR
> 22 19.13758    0   MAJOR
> 23 16.19439    0 CONTROL
> 24 21.36893    0 CONTROL
> 25 15.89049    0 CONTROL
> 26 18.99795    0 CONTROL
> 27       NA    0 BIPOLAR
> 28 18.90486    0 BIPOLAR
> 29 16.36413    0   MAJOR
> 30 20.42710    0   MAJOR
> 31 16.65982    0   MAJOR
> 32 19.45791    0   MAJOR
> 33 16.64339    0 CONTROL
> 34 19.40041    0 CONTROL
> 35 15.37303    1 BIPOLAR
> 36 19.83847    0 BIPOLAR
> 37 15.42231    1   MAJOR
> 38 19.37029    0   MAJOR
> 39 15.06913    0   MAJOR
> 40 17.81520    0   MAJOR
> 41 15.50445    0 BIPOLAR
> 42 17.92197    0 BIPOLAR
> 43 15.34565    0 CONTROL
> 44 18.07529    0 CONTROL
> 45 15.59480    0 CONTROL
> 46 19.67420    0 CONTROL
> 47 14.78987    0   MAJOR
> 48 20.05476    0   MAJOR
> 49 14.78713    0   MAJOR
> 50 19.86858    0   MAJOR
>
>
> On Fri, 2010-07-23 at 11:52 -0700, Charles C. Berry wrote:
>> On Fri, 23 Jul 2010, Christopher David Desjardins wrote:
>>
>>> Hi,
>>> I am trying to fit the following model:
>>>
>>> sr.reg.s4.nore <- survreg(Surv(age_sym4,sym4), as.factor(lifedxm),
>>> data=bip.surv)
>>
>> Next time include a reproducible example. i.e. something we can run.
>>
>> Now, Google "Hauck Donner Effect" to understand why
>>
>>  	anova(sr.reg.s4.nore)
>>
>> is preferred.
>>
>> Chuck
>>
>>
>>>
>>> Where age_sym4 is the age that a subject develops clinical thought
>>> problems; sym4 is whether they develop clinical thoughts problems (0 or
>>> 1); and lifedxm is mother's diagnosis: BIPOLAR, MAJOR DEPRESSION, or
>>> CONTROL.
>>>
>>> I am interested in whether or not survival differs by this covariate.
>>>
>>> When I run my model, I am getting the following output:
>>>
>>>> summary(sr.reg.s4.nore)
>>>
>>> Call:
>>> survreg(formula = Surv(age_sym4, sym4) ~ as.factor(lifedxm),
>>>    data = bip.surv)
>>>                           Value Std. Error     z       p
>>> (Intercept)                4.037      0.455  8.86643
>>> 0.000000000000000000755
>>> as.factor(lifedxm)CONTROL 14.844   4707.383  0.00315
>>> 0.997484052845082791450
>>> as.factor(lifedxm)MAJOR    0.706      0.447  1.58037
>>> 0.114022774867277756905
>>> Log(scale)                -0.290      0.267 -1.08493
>>> 0.277952437474223823521
>>>
>>> Scale= 0.748
>>>
>>> Weibull distribution
>>> Loglik(model)= -76.3   Loglik(intercept only)= -82.6
>>> 	Chisq= 12.73 on 2 degrees of freedom, p= 0.0017
>>> Number of Newton-Raphson Iterations: 21
>>> n=186 (6 observations deleted due to missingness)
>>>
>>>
>>> I am concerned about the p-value of 0.997 and the SE of 4707. I am
>>> curious if it has to do with the fact that the CONTROL group doesn't
>>> have a mixed response, meaning that all my subjects do not develop
>>> clinical levels of thought problems and subsequently 'survive'.
>>>
>>>> table(bip.surv$sym4,bip.surv$lifedxm)
>>>
>>>    BIPOLAR CONTROL MAJOR
>>>  0      41      60    78
>>>  1       7       0     6
>>>
>>> Is there some sort of way that I can overcome this? Is my model
>>> misspecified? Is this better suited to be run as a Bayesian model using
>>> priors to overcome the lack of a mixed response?
>>>
>>> Also, please cc me on an email as I am a digest subscriber.
>>> Thanks,
>>> Chris
>>>
>>>
>>> --
>>> Christopher David Desjardins
>>> PhD student, Quantitative Methods in Education
>>> MS student, Statistics
>>> University of Minnesota
>>> 192 Education Sciences Building
>>> http://cddesjardins.wordpress.com
>>>
>>> ______________________________________________
>>> R-help at r-project.org mailing list
>>> https://stat.ethz.ch/mailman/listinfo/r-help
>>> PLEASE do read the posting guide http://www.R-project.org/posting-guide.html
>>> and provide commented, minimal, self-contained, reproducible code.
>>>
>>
>> Charles C. Berry                            (858) 534-2098
>>                                              Dept of Family/Preventive Medicine
>> E mailto:cberry at tajo.ucsd.edu	            UC San Diego
>> http://famprevmed.ucsd.edu/faculty/cberry/  La Jolla, San Diego 92093-0901
>>
>>
>
> -- 
> Christopher David Desjardins
> PhD student, Quantitative Methods in Education
> MS student, Statistics
> University of Minnesota
> 192 Education Sciences Building
> http://cddesjardins.wordpress.com
>

Charles C. Berry                            (858) 534-2098
                                             Dept of Family/Preventive Medicine
E mailto:cberry at tajo.ucsd.edu	            UC San Diego
http://famprevmed.ucsd.edu/faculty/cberry/  La Jolla, San Diego 92093-0901