search for: biomedicin

...als for Continuous Exposures", American Journal of Epidemiology, 154(3), 264-275. [2] Saez, M., Cadarso-Su?rez C. & Figueiras, A. (2003) "np.OR: an S-Plus function for pointwise nonparametric estimation of odds-ratios of continuous predictors", Computer Methods and Programs in Biomedicine, 71, 175-179. 4. For each purely parametric covariate a t-test is produced; I'd like to have something like S-plus' anova.gam() to get an overall test. (Perhaps with the addition of a choice between Type I and Type III tests, but I guess that may be controversial). Is it possible? //...

...railty models using maximum penalized likelihood estimation. Statistics in Medicine. 2006 ;25(23) : 4036-4052. [4] Rondeau V, Gonzalez JR. FRAILTYPACK : a computer program for the analysis of correlated failure time data using penalized likelihood estimation. Computer Methods and Statistics in Biomedicine, 2005 ;80,154-164. [5] Rondeau V, Joly P, Commenges D. Maximum penalized likelihood estimation in frailty models. Lifetime Data Analysis, 2003 ;9 :139-153. -- _____________________________________________________________________ Virginie RONDEAU, Ph.D. - Chercheur INSERM (CR1) INSERM U897 (Bi...

...he surev package of Lusa Lara; Miceli Rosalba; Mariani LuigiEstimation of predictive accuracy in survival analysis using R and S-PLUS.<http://www.biomedexperts.com/Abstract.bme/17601627/Estimation_of_predictive_accuracy_in_survival_analysis_using_R_and_S-PLUS> Computer methods and programs in biomedicine 2007;87(2):132-7. And my code was library(surev) pred.accuracy<-f.surev(f) pred.accuracy sorry if my question isn't clear - should I have included my sessionInfo for a methodological question ? (I'm a newbie) many thanks for any advice [[alternative HTML version deleted]]

...railty models using maximum penalized likelihood estimation. Statistics in Medicine. 2006 ;25(23) : 4036-4052. [4] Rondeau V, Gonzalez JR. FRAILTYPACK : a computer program for the analysis of correlated failure time data using penalized likelihood estimation. Computer Methods and Statistics in Biomedicine, 2005 ;80,154-164. [5] Rondeau V, Joly P, Commenges D. Maximum penalized likelihood estimation in frailty models. Lifetime Data Analysis, 2003 ;9 :139-153. -- _____________________________________________________________________ Virginie RONDEAU, Ph.D. - Chercheur INSERM (CR1) INSERM U897 (Bi...

...s an annual forum that will bring together ideas, projects, lessons, etc. associated with distributed computing, artificial intelligence and its applications in different themes. This meeting will be held at the University of Salamanca in June 10-12th, 2009. This symposium will be organized by the Biomedicine, Intelligent System and Educational Technology Research Group (http://bisite.usal.es/) of the University of Salamanca. The technology transfer in this field is still a challenge and for that reason this type of contributions will be specially considered in this symposium. This conference is the fo...

Dear friends, two years ago (as I found on the web) Paul sent the following message but I was not able to find if he got an answer. Today I have the same question and it would be great if I could find out that this test has been implemented (somehow) in R. Please do not confuse it with the Brown-Forsythe test of equality of variances. Thank you: I've been searching around for a function for

Dear R Fellow-Travellers: What is your recommended way of dealing with a left-censored response (non-detects) in (linear Gaussian) mixed effects models? Specifics: Response is a numeric positive measurement (of volume, actually); but when it falls below some unknown and slightly random value (depending on how the sample is prepared and measured), it cannot be measured and is recorded as 0.

I read somewhere that t.test is equivalent to a hypothesis testing for one way ANOVA. But I'm wondering how they are equivalent. In the following code, the p-value by t.test() is not the same from the value in the last command. Could somebody let me know where I am wrong? > set.seed(0) > N1=10 > N2=10 > x=rnorm(N1) > y=rnorm(N2) > t.test(x,y) Welch Two Sample t-test data: