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@r-project.org mailing list
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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:cbe...@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:cbe...@tajo.ucsd.edu UC San Diego
http://famprevmed.ucsd.edu/faculty/cberry/ La Jolla, San Diego 92093-0901
______________________________________________
R-help@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.
