similar to: survival analysis sampling question

I have a study best described as a retrospective case-cohort design: the cases were all the events in a given time span surveyed, and the controls (event-free during the follow-up period) were selected in 2:1 ratio (2 controls per case). The sampling frequency for the controls was about 0.27, so I used a weight vector consisting of 1 for cases and 1/0.27 for controls for coxph to adjust

Dear all, I have data from 1970 to 1990 for people above age 50. Now I want to calculate survival curves by age starting at age 50 using the Kaplan Meier Estimator. The problem I have is that there are already people in 1970 who are older than 50 years. I guess this is called delayed entry or left truncation (?). I thought the code would be: roland <- survfit(Surv(time=age.enter,

Hi, I am wondering if anyone can explain to me if cumulative incidence (CI) is just "1 minus kaplan-Meier survival"? Under what circumstance, you should use cumulative incidence vs KM survival? If the relationship is just CI = 1-survival, then what difference it makes to use one vs. the other? And in R how I can draw a cumulative incidence plot. I know I can make a Kaplan-Meier

Hi everyone, I am fitting a cox proportional hazard model with a continuous variable "x" as the covariate: fit<-coxph(Surv(time, status)~x) Now I wanted to make a plot of survival probability vs. the covariate, and the 95% confidence interval for the survival probability. It's just like a Kaplan-Meier Survival curve, except now the x axis represents the value of covariate, not

Dear R users, i try to calculate the probabilty to survive a given time by using the estimated survival curve by kaplan meier. What is the right way to do that? as far as is see i cannot use the predict-methods from the survival package? library(survival) set.seed(1) time <- cumsum(rexp(1000)/10) status <- rbinom(1000, 1, 0.5) ## kaplan meier estimates fit <- survfit(Surv(time,

Hello R users I have a question with Kaplan-Meier Curve with respect to my research. We have done a retrospective study on fillings in the tooth and their survival in relation to the many influencing factors. We had a long follow-up time (upto 8yrs for some variables). However, we decided to stop the analysis at the 6year follow up time, so that we can have uniform follow-up time for all the

Hello, Excuse me for a more methodological than technical question. I'm developing a Cox model with 10 covariates. One of them is age (named "eta"). I've checked proportionality with cox.zph with age continuous: > cox.zph(coxph(Surv(TTP,CENSOTTP)~eta)) rho chisq p eta -0.0154 0.0225 0.88 and categorical (eta<60): >

useR's, I am trying to do a power calculation for a survival analysis using a logrank test and I need some help properly doing this in R. Here is the information that I know: - I have 2 groups, namely HG and LG - Retrospective analysis with subjects gathered from archival data over 20 years. No new recruitment of subjects and no estimated time to target accrual and accrual rate. - Survival

First a statistical issue: The survfit routine will produce predicted survival curves for any requested combination of the covariates in the original model. This is not the same thing as an "adjusted" survival curve. Confusion on this is prevalent, however. True adjustment requires a population average over the confounding factors and is closely related to the standardized

Did you not notice the conf.type = "none" argument to your survfit call and the associated documentation in the survfit help? -- Bert Bert Gunter "The trouble with having an open mind is that people keep coming along and sticking things into it." -- Opus (aka Berkeley Breathed in his "Bloom County" comic strip ) On Sat, Aug 26, 2017 at 5:18 PM, Adrian Johnson

Hi I have been asked to provide Weibull parameters from a paper using Kaplan Meir survival analysis. This is something I am not familiar with. The survival analysis in R works nicely and is the same as commercial software (only the graphs are superior in R). The Weibull does not and produces an error (see below). Any ideas why this error should occur? My approach may be spurious.

Dear Bert, thank you for suggestion. I am aware of R-help function. I must apologize, my earlier question could lead to assumptions otherwise. As you can see below, I only get Std. error but not lower and Upper CIs. I was wondering if there is another argument or method, could give CIs which I cannot find anywhere. Thanks Adrian > Nsurv <-

Dear list, does anyone know of a R-package that has implemented the increasingly popular inclusion of the number of patients at risk below Kaplan-Meier curves like in http://bloodjournal.hematologylibrary.org/content/vol116/issue19/images/large/zh89991058760001.jpeg any hint (or negative answer) is much appreciated. Thanks Thorsten -- Thorsten Raff 2nd Medical Department, University

Hi - Below is my code and then the error when I run the last line. time_np <- train1_na$tte event_np <- train1_na$censored X_np <- cbind( train1_na$AMT, train1_na$DISCOUNT_AMT, train1_na$high_price_pcnt, train1_na$EM_RECEIVED, train1_na$DM_RECEIVED, train1_na$TXN_WITH_RINGCODE, train1_na$WEB, train1_na$clearance_pcnt, train1_na$bts_pcnt, train1_na$sales_pcnt,

You should stop trying to use matrices on the RHS and using separate vectors to Surv. Instead use a data argument and have the names in your formula refer to column names. ? David Sent from my iPhone > On Nov 5, 2017, at 7:21 PM, Meghna Govil via R-help <r-help at r-project.org> wrote: > > > Hi - Below is my code and then the error when I run the last line. > >

Good morning everyone (or maybe good evening) Is there a counterpart to the s-plus function "probplot" (which provides a qq-plot for "survreg"-objects)? Or do exist other (rather simple) possibilities to check the assumptions of the distribution? I have another question to the author(s) of summary.survreg: Why does summary(...,times=sort(x)) not give the same result as

Hello everybody, does anybody know how the function plot.survfit exactly works? I'd like to plot the log of the cummulative hazard against the log time by using plot.survfit(...fun="cloglog") which does not work correctly. The scales are wrong and there is an error message about infinit numbers. It must have something to do with the censored data, doesn't it? #Example:

Hi R users I need to estimate, with kaplan Meier methodology, a Quality adjusted survival analysis. It is possible doing this at R? Thanks in advance. Best Regards Pedro Mota Veiga -- View this message in context: http://r.789695.n4.nabble.com/How-can-I-doing-Quality-adjusted-survival-analysis-in-R-tp4295868p4295868.html Sent from the R help mailing list archive at Nabble.com.

OK this is bound to be something silly as I'm completely new to R - having started using it yesterday. However I am already warming to its lack of 'proper' GUI... I like being able to rerun a command by editing one parameter easily... try and do that in a Excel Chart Wizzard! I eventually want to use it to analyse some chemotherapy response / survival data. That data will not be

Hi, I forgot to add to my initial message that I am using Windows 98 (if it's relevant). I need to perform survival analysis and could not find the S+ functions: - survfit (fits a Kaplan-Meier survival curve), or - coxph (fits a Cox proportional hazards model) in R. Are there any alternative functions available? Thanks, Alvaro -- -- -- -- -- -- -- -- Alvaro A. Novo