[R] Survival analysis MLE gives NA or enormous standard errors
Christopher David Desjardins
desja004 at umn.edu
Tue Jul 27 17:22:38 CEST 2010
Hi Charles,
On Fri, 2010-07-23 at 14:40 -0700, Charles C. Berry wrote:
> 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?
>
You wrote:
> 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.
>
Would that be recoding lifedxm (presently a dummy variable where 0 -
BIPOLAR, 1 - CONTROL, and 2 - MAJOR DEPRESSED) as three seperate
variables: CONTROL (0 - No, 1 - Yes), BIPOLAR (0 - N0, 1 - Yes), and
MAJOR DEPRESSED (0 - No, 1 - Yes) and then comparing the following
models with anova()?
CONTROL + BIPOLAR to MAJOR
CONTROL + MAJOR to BIPOLAR
I am sorry I am just a little confused. Basically I want to know if
BIPOLAR is a higher risk than MAJOR and CONTROL and if MAJOR is a higher
risk than CONTROL.
Thank you very much for all your help,
Chris
> >
> > 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
>
>
--
Christopher David Desjardins
PhD student, Quantitative Methods in Education
MS student, Statistics
University of Minnesota
192 Education Sciences Building
http://cddesjardins.wordpress.com
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