similar to: survSplit: further exploration and related topics

Dear All, since years I am struggling with Surv objects in data.frames. The following seems to have to do with it. See below the modified example from the help page of survSplit. The original works, as expected. If, however, a Surv object is added to the data.frame, each record gets doubled. Is there some solution other than avoiding Surv objects in data.frames? Thanks, Heinz

Hi, I am new to time-dependent Cox model to estimate time dependent hazard ratios. Let me use aml dataset from survival package: > aml3<-survSplit(aml2,cut=c(5,10,20),end="time",start="start", event="status",episode="i") If I want to esimate hazard ratio for each of the time intervals 0-5, 5-10, 10-20 and >=20, would the following calculate

Danardono <daodao99 at student.umu.se> wrote: > > While waiting for 2.1, for those who need function[s] for this > survival-splitting business, as I do, this 'survcut' function below > might be helpful. > It is not an elegant nor efficient function but it works, at least for > some examples below. > Ditto the following, for the case where there are multiple

Dear R-users, I use the survsplit function in the survival package to change my data into counting-process format and the transformed format is as follow: (a) start stop event DP age .... 0 5 0 1 20 5 10 0 1 20 10 25 1 1 20 looking at the above three entries that belong to the same person, if an event happen at

I'm trying to convert a dataset from the time-independent analysis form > head(addicts) id clinic status survtime prison meth clinic01 1 1 1 1 428 0 50 1 2 2 1 1 275 1 55 1 3 3 1 1 262 0 55 1 into the "counting data format" necessary to perform extended Cox regression. I got

Dear All, I got a warning message "X matrix deemed to be singular" in Cox model with a time dependent coefficient. In my analysis, the variable "SEX" is a categorical variable which violate the PH assumption in Cox. I first used the survSplit() function to break the data set into different time intervals, and then fit the model. The procedures can be described as follows:

Dear R users, I am doing Cox regression using coxph(Survival) or cph(Design). I have time varying effects (diagnosed with schoenfeld residuals Chi2 test and graph) so I first want to split time into 2 separate intervals : t<6months and t>=6months, to estimate one hazard ratio (hr) for each interval. I am analysing Overall survival according to 3 prognostic factors (age,deep,ldh).

Alternatively, use the concat() function from the gregmisc library: > library(gregmisc) Attaching package `gregmisc': [warnings omitted]] > concat(x[[1]],x[[2]],names=names(x)) age.day state source 1 81 0 772307 2 141 0 772307 3 234 0 772307 4 321 0 772307 5 437 0 772307 6 499 0 772307 7 601 1 772307 8 689

Dear all, I would like to obtain the Brier score prediction error at different times t for an extended Cox model. Previously I have used the 'pec' function (pec{pec}) to obtain prediction error curves for standard Cox PH models but now I have data in the counting process format (I have a covariate with a time-varying effect) and it seems that the pec function does not support the counting

A new version of the survival package has been released. The biggest change is stronger support for multi-state models, which is an outgrowth of their increasing use in my own practice. Interested users are directed to the "time dependent covariates" vignette for discussion of the tmerge and survSplit functions, which are useful tools to build the requisite data sets, and to the

Dear R-help Community, I'm currently struggling with some issues extending the proportional Cox model with time-dependent coefficients and could really need some help. Since I'm not experienced in adding code in an email in a nice way I add the link to my question and code: https://stats.stackexchange.com/questions/297052/time-dependend-coefficients-in-a-cox-model Essentially, I try

I have some data with two categorises plus/minus (p53) and a particular time (Time) and the outcome is a continuous vairable (Result). I set up a maximum model. ancova <- lm(Result~Time*p53) > summary(ancova) .. Coefficients: Estimate Std. Error t value Pr(>|t|) (Intercept) 0.05919 0.55646 0.106 0.916 Time -0.02134 0.01785 -1.195 0.241 p53plus

Fellow R-users, I have a longitudinal data set with missing values in it. I would like to produce a residual plot for each time using panel.xyplot function but I get an error message. Here's a simple example, library(nlme) set.seed(1544) longdata <- data.frame(ID=gl(10,1,50), y=rnorm(50), time = as.numeric(gl(5,10,50)), x = rnorm(50)) longdata$y[5] <- NA longdata$x[35] <- NA

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Dear R list, I have an event history data set that is structured like this: Legislative act Discussion Agreement Time Event Act1 2006-05-30 2006-06-19 20 1 Act2 2004-03-01 2004-06-14 105 1 . . . I have information on the meetings in the legislature between adoption periods in a separate variable (the

Hi, All,   My data collection is from 4 regions (a, b, c, d). Within each region, it has 2 or 3 units. Within each unit, it has measurement from about 25 sample site. I was trying to use lme function to discribe relationship between y and a few covariates. Both y and covariates were measured at the sample site level. My question is when I use exactlly the same model but choose different estimation

Long story short, I have a big iterative procedure that produces a long list of data.frames such as the one called "results" here. Is there an easy way to produce a similar list of data.frames comprised of the mean of each of the columns in results, such that it ends up like the one I've shown in "resultsmean" below? I've tried apply and lapply, still not got the

Hi,I have tumor growth curve data for a bunch of different mice in various groups. I want to compare the growth curves of the different groups to see if timing of drug delivery changed tumor growth.I am trying to run a mixed models with repeated measures over time with each mouse as a random effect with linear and quadratic terms for time.This took me a long time to figure out and I just wanted to

Dear all, The 'make check' step fails for the pacakge mva on IBM AIX. The tail of the Rout log file looks like: > for(factors in 2:4) print(update(Harman23.FA, factors = factors)) Call: factanal(factors = factors, covmat = Harman23.cor) Uniquenesses: height arm.span forearm lower.leg weight 0.170 0.107 0.166

On 27 October 2014 11:23, Li Liu <john.liuli at huawei.com> wrote: > So you mean virtio-mmio will be replaced by PCI/PCIe on ARM at last? That is the plan, yes. I can't make any promises on timescales at the moment, though... -- PMM