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@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: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 > > -- 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 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.
