search for: baseline

...So would like to resend the message, hope to get any fresh attention. Since this is not purely lme technical question, so I also cc-ed R general mailing list, hope to get some suggestions from there as well. I asked some questions on how to analyze longitudinal study with only 2 time points (baseline and a follow-up) previously. I appreciate many useful comments from some members, especially Dennis Murphy and Marc Schwartz who refered the following paper addressing specifically this type of study with only 2 time points: https://www.ncbi.nlm.nih.gov/pmc/articles/PMC1121605/ Basically, wit...

...h keep.source = TRUE Symptom: SInput is taken from too far down the input file (the shift is the number of lines of the included file). Is that known? Searching didn't turn up anything, yet I think there are more people than just me using keep.source. Example: $ ~/tmp/R-beta/bin/R CMD Sweave baseline.Rnw && mv baseline.tex baseline.devel.tex && R CMD Sweave baseline.Rnw && mv baseline.tex baseline.2.11.tex && diff -u baseline.2.11.tex baseline.devel.tex gives: --- baseline.2.11.tex 2010-10-05 11:33:49.000000000 +0200 +++ baseline.devel.tex 2010-10-05 11:33:49.00...

Hello all, I'm almost embarrassed to post this , it seems so easy. Suppose I have a baseline and follow up survey but some people are missing in the follow up: > baseline<-data.frame(id=c(3,5,7,9,12), data= runif(5)) > follow.up<-data.frame(id=c(3,7,9,12), data= runif(4)) > baseline id data 1 3 0.66771988 2 5 0.28794744 3 7 0.01892821 4 9 0.64863175 5 12 0.86485...

...s) model using the lme function (package nlme) in R 2.4.0. As a mixed-effects newbie I'm neither sure about the modeling nor the correct R syntax. My data is structured as follows: For each subject, a quantity Y is measured at a number (>= 2) of time points. Moreover, at time point 0 ("baseline"), a quantity X is measured for each subject (I am interested to see whether X, or log(X), is a predictor of Y). I saw in some examples that time-invariant predictors should be repeated for all rows of a subject, which is why I copied the baseline value of X also to time points > 0. The res...

A subset of my raw data looks like this: -------------- "Grip" "Technique" "Baseline.integrated" "Task" "Stroke..direction." "Engag" "Disen" "PenDG" "PenUG" "PenDS" "PenUS" "Duration" ------------- "Tripod","Barrel&...

...0 179 2 I need to find, the single observation, which has the closest date of 'OBS_DATE' to 'IDX_DT'. For example, for 'PT_ID' of 4549, I need row 13, of which the OBS_DATE is just one day away from IDX_DT. I was thinking about using abs(), and I got this: baseline<- function(x){ + + #remove all uncessary variables + baseline<- x[,c("PT_ID","DAYS_DIFF")] + + #get a list of every unique ID + uniqueID <- unique(baseline$PT_ID) + + #make a vector that will contain the smallest DAYS_DIFF + first <- rep(-99,length(unique...

Dear All, I'm having just a little terminology problem, relating the language used in the Hosmer and Lemeshow text on Applied Survival Analysis to that of the help that comes with the survival package. I am trying to back out the values for the baseline hazard, h_o(t_i), for each event time or observation time. Now survfit(fit)$surv gives me the value of the survival function, S(t_i|X_i,B), using mean values of the covariates and the coxph() object provides me with the estimate of the linear predictors, exp(X'B). If S(t_i|X_i,B)=S_o(t_i)^exp(...

Dear R-help and Prof. Harrell: My question concerns the baseline state for continuous variable in lrm() within the RMS package. I have a model which can be reduced to: lrm(FT ~ rcs(V1, c(0, 1,5)) The model makes perfect sense if the baseline state is where V1>=5 but the model makes no sense if the baseline category is 0 (which I had expected). Can someo...

...this problem:   I have a dataset that is in the format below, ID  time  Y  Age 1  0     195  23.1 1  2    204  23.3 1  4   202    23.5 2  0  170    22.0 2  3   234   22.2 3  0  208   24.4 3  2  194  24 .7 3  3   204  24.9   I wish to remove all the measurements at time point 0 and convert them to a baseline variable as follows;    ID  time  Y  Age  baseline 1  2    204  23.3   195 1  4   202    23.5   195 2  3   234   22.2    170 3  2  194  24 .7     208 3  3   204  24.9     208   Thanks.   regards John      [[alternative HTML version deleted]]

...happen? I need to be able to specify an array of rgb values for the 'col' parameter. colnames.col <- c("black", "red", "blue", "green") colnames.rgb <- apply(as.matrix(colnames.col), 1, col2rgb) dimnames(colnames.rgb)[[2]] <- colnames.col baseline <- 1:32 offset2 <- 2*baseline offset3 <- 3*baseline offset4 <- 4*baseline offsets <- cbind(offset2, offset3, offset4) # Produces expected result X11() matplot(baseline, col = colnames.col[1], type = "l") matlines(offsets, col = colnames.col[-1]) # Displays a ??yellow?? li...

Dear R-help, I am trying to obtain the baseline survival estimate of a fitted Cox model (S_0 (t)). I know that previous posts have said use 'basehaz' but this gives the baseline hazard function and not the baseline survival estimate. Is there a way to obtain the baseline survival estimate or do I have to use the formula which does somet...

...catch up). I've discovered R recently while trying to find a statistical program in order to avoid rebooting my computer under windows when I need to do some statistical work. And here is my first question. I'm actually trying to model a survival curve using a cox model. But I need the baseline cumulative hazard. I've been looking around on the web and the archives, and I found that it as to do with the nelson-aalen estimation. But I must not understand all the doc very well, because I can't get the baseline hazard. Could you help me, please. Best regards. PS : I hope you'...

Hi, assume that I have a repeated measure dataset with 3 time points: baseline, day 5 and day 10. There are 4 treatment groups (vehicle, treatment 1, treatment 2 and treatment 3). 20 subjects per treatment group. A simple straight-forward way to analyze the data is to use mixed model: model 1: obj <- lmer(y ~ treatment * time +(time|subject)) where time is numeric with...

...odule/INFO/issue ./files/module/NIS ./files/module/NIS/nsswitch.conf ./fileserver.conf ./manifests ./manifests/nodes.pp ./manifests/site.pp ./manifests/templates.pp ./puppet.conf ./modules ./modules/ssh ./modules/ssh/manifests ./modules/ssh/manifests/ssh.pp ./modules/ssh/manifests/init.pp ./modules/baseline ./modules/baseline/plugins ./modules/baseline/plugins/puppet ./modules/baseline/plugins/puppet/type ./modules/baseline/plugins/puppet/type/lines.pp ./modules/baseline/manifests ./modules/baseline/manifests/baseline.pp ./modules/baseline/manifests/init.pp ./modules/openvz ./modules/openvz/manifests...

Hello, I am looking for a sample size function for samples sizes, to test proportions that are not binomial proportions. The proportions represent a ratio of (final measure) / (baseline measure) on the same experimental unit. Searches using RSeek and such bring multiple hits for binomial proportions, but that doesn't seem to fit my situation. Perhaps there's some standard terminology from a different field that would provide better hits than deeming this a 'rate'...

I am pretty pleased with baselines I fit to chromatograms using the runquantile() function in caTools(v1.6) when its probs parameter is set to 0.2 and its k parameter to ~1/20th of n (e.g., k ~ 225 for n ~ 4500, where n is time series length). This ignores occasional low- side outliers, and, after baseline subtraction, I can re-...

Dear all, I am confused with the output of survfit.coxph. Someone said that the survival given by summary(survfit.coxph) is the baseline survival S_0, but some said that is the survival S=S_0^exp{beta*x}. Which one is correct? By the way, if I use "newdata=" in the survfit, does that mean the survival is estimated by the value of covariates in the new data frame? Thank you very much! Koshihaku -- View this message in...

...: subscript out of bounds ########################################################## set.seed(500) n.timepoints <- 4 n.subj.per.tx <- 20 sd.d <- 5; sd.p <- 2; sd.res <- 1.3 drug <- factor(rep(c("D", "P"), each = n.timepoints, times = n.subj.per.tx)) drug.baseline <- rep( c(0,5), each=n.timepoints, times=n.subj.per.tx ) #Patient <- rep(1:(n.subj.per.tx*2), each = n.timepoints) Patient.baseline <- rep( rnorm( n.subj.per.tx*2, sd=c(sd.d, sd.p) ), each=n.timepoints ) time.baseline <- rep(1:n.timepoints,n.subj.per.tx*2)*as.numeric(drug=="D"...

Dear, I want to fit an inverse gaussion distribution to a data set. The predictor variables are gender, area and agecategory. For each of these variables I've defined a baseline e.g. #agecat: baseline is 3 data<-transform(data, agecat=C(factor(agecat,ordered=TRUE), contr.treatment(n=6,base=3))) The variable 'area' goes from A to F (6 areas: A,B,C,D,E,F) How can i manipulate the data to set the baseline of area to C? R is producing errors when I'm tryi...

...ce frontier to incrementally identify control dependence sources for live operations to determine when branch operations are live. It adds a new fix-up phase to correct update phi-nodeswhen control flow changes. Below are compile time and performance impacts based on Facebook internal code sets. Baseline ADCE compared to new ADCE compile time, -O3 removing loops Comparing compile times for ~400 source files from folly code base "baseline" is total time in seconds for all files "net_difference" is increase due to changes "percent" is increase expressed aa persent of ba...