similar to: course on Analysis of Repeated Measurements by Ronald Geskus

Pharmacokinetic and pharmacodynamic modeling and simulation By Dr. Jan Freijer September 24, 2009 Amsterdam, The Netherlands http://www.can.nl/events/details.php?id=57 This course is aimed at users of R or S-PLUS in the bio-pharmaceutical sciences who would like to use R for clinical trial simulations. topics include Working with packages in R - MASS -

Dear Geskus, I want to develop a prediction model. I followed your paper and analysed thro' weighted coxph approach. I can develop nomogram based on the final model also. But I do not know how to do internal validation of the model and subsequently obtain calibration plot. Is it possible to use Wolbers et al Epid 2009 approach 9 (R code for internal validation and calibration) . It is

Hi List, I have a problem with auth for just one user: dovecot: Sep 30 09:56:16 Info: auth(default): new auth connection: pid=3809 dovecot: Sep 30 09:56:17 Info: auth(default): client in: AUTH 1 PLAIN service=IMAP secured lip=127.0.0.1 rip=127.0.0.1 resp=AGoucnVpc0BtZXJreC12ZXJrZXJrLm5sAGJsb2VtMDEh dovecot: Sep 30 09:56:17 Info: auth(default): pam(j.ruis at

Dear Raja, A Fine and Gray model can be fitted using the standard coxph function with weights that correct for right censoring and left truncation. Hence I guess any function that allows to perform stepwise regression with coxph should work. See e.g. my article in Biometrics https://doi.org/10.1111/j.1541-0420.2010.01420.x, or the vignette "Multi-state models and competing risks" in the

Dear R-help subscribers, I have a quite stupid question about using lapply. I have the following function: create.gradient <- function(i){ colorgrad01<-color.scale(seq(0,1,by=0.01), extremes=c("red","blue")) tree1$edge[i,1] -> x tree1$edge[i,2] -> y print(x) print(y) all2[x] -> z all2[y] -> z2 round(z, digits = 2) -> z round(z2, digits = 2) -> z2

Dear R-help subscribers; I have a question regarding making gradients in R. I've searched on the web, but was only able to find functions that make a gradient between color X and Y, which is not what I want. I want to 'pick out' individual, smaller pieces of a gradient based on a range of numbers. Say that I have a range of numbers, leading from 0 to 1. Then I want 0 to refer to

Dear people from the R help list, I have a question that I can't get my head around to start answering, that is why I am writing to the list. I have data in a format like this (tabs might look weird): John A1 1 0 1 John A2 1 1 1 John A3 1 0 0 Mary A1 1 0 1 Mary A2 0 0 1 Mary A3 1 1 0 Peter A1 1

Hello, I want to perform a permanova where the first factor called Treatment has four levels. The second factor involves sampling the same research plots for four consecutive years, hence the repeated measurements. I have been able to use the adonis function from the package vegan to run this analysis. code below: TC.perMANOVA.adonis<-adonis(TC.PerMANOVA ~ Treatment*Year,

Dear R-users, I have some human serum lipid concentrations (cholesterol, apoB ...), each lipid was measured (in the same person) for 6 times in different time points (start, 3 months, 6 months, 12 months, 18 months, 24 months). There were 2 groups of participants: one with a nutritional intervention and the other without it. I would like to know, whether lipid concentrations differ among different

Hello, I have a question regarding performing manova. I have an experiment where I want to measure 10 output variables for 3 different measurement methods. Since each of the methods requires some user interaction, I would also like to include repeated measures for each of the output variables to include intraobserver variability in the design. How can I perform such a repeated-measures

I have repeated measures data (over time) for each of 40 individuals with the number of observations per person varying greatly from 4 to 51. There are 578 measurements in total. There are no grouping variables or other covariates. I have been using the function carma in the library called growth to fit a cubic polynomial in time with AR(1) serial correlation plus measurement error. However, I

Hi list I am just beginning to understand the complexities of linear mixed effects models. Maybe someone can give advise concerning the following problem: I have two groups of surgical patients in which repeated laboratory measurements were taken over time after surgery. I decided that lme would be the best model to fit the data. I already fitted the model lme(logratio ~ gr*I(pod-10) +

Dear all, Does anybody know how to get more evaluation points in performing Nonparametric analysis of repeated measurements data with "sm" library. The following command gives the estimation for 50 points, by I would like to increase to 100 points But I do not know how to do that. library(sm) provide.data(citrate, options=list(describe=FALSE)) provide.data(dogs,

Hello, I am using the package gamlss in R to plot repeated measurements. The command I am using is par.plot(). It works great except one thing about the label of the axises. I tried to label both x and y axises using ylab and xlab options. But the plot only gives variable variables. The labels did not show up. Below is the code I used. Any comments are appreciated! Thanks. library(gamlss)

Dear R Users, I am using the LLTM and the LRSM functions in the eRm package to do repeated measurements where there are 2 measurement points for a list of 10 items. I am trying to get ability estimates but am having trouble. I don't think that it is appropriate to use the pmat function since the person parameters are based on all 20 items. Rather, I think it would be more appropriate to

I am trying to do a repeated measurement anova using an lme. I have the following variables: -ID, the identification of the individual -trail, with 2 levels -treatment, with 3 -time, measure 5 times the same individual -VCL, the response variable I tried the following in R, within.gr<-groupedData(VCL~time|ID/treatment/time,data=within)

Dear all, I'm working on a design with rotating panels. Each site is sampled every X year. Have a look at the code below the get an idea of the design. In reality the number of sites will be (much) higher. However I'm not sure if I coded the design correctly in svydesign(). Can someone confirm that it is either correct or wrong? I've added the lmer() equivalent that I want to

Dear list, i am setting up a GLM for a repeated measurement ANOVA using the lm and ANOVA function. my design contains four factors with 5, 5, 2 and 2 (= 14) levels, respectively. the data are stored in a data.frame with six columns, one for the data themselves and the remainings for the factors where strings indicate the factor levels in each row. now i would like to restructure this

Dear, I want to analyze an outcome in an RCT using lme but I am not sure that I have chosen the right way for the model. We measured the outcome three times repeatedly in the same patient. One time before intervention and two times after intervention. I wanted to adjust for the correlated data in the repeated measurement and baseline differences in the variable in order to get the treatment

I am using lme to handle repeated measurements. So far i can follow the examples from the book from pinheiro and bates. Now i get the problem , that i have "nested" repeated measuremnts, and i cant find out how to do the grouping part of the lme formula. 1.The simple problem ist that i have different Samples , from which i make repeated measurements (each sample is measured 6 times) and