similar to: coxph() command design and data setup

Justin, You have an interesting problem, and a serious (reliable) consultation would take more time than I have to give at the moment. Which is to say that you should take these comments with a grain of salt. First, I don't think that you have censored data. You have 2 subdistribution functions F1(t) and F2(t), F1(t) + F2(t) = F(t) = the "time to endpoint" distribution.

Dear R-users, I computed a simple coxph model and plotted survival over time with associated confidence intervals for 2 covariate levels (males and females). M1 <- coxph(survobject~sex, data=surv) M1 survsex <- survfit(survobject~sex,data=surv) summary(survsex) plot(survsex, conf.int=T, col=c("black","red"), lty = c(1,2), lwd=c(1,2), xlab="Time",

Hello all R-helpers, I've performed an experiment to test for differential effects of elevated temperatures on three different groups of corals. I'm currently performing a cox proportional hazards regression with censoring on the survivorship (days to mortality) of each individual in the experiment with two factors: Temperature Treatment (2 levels: ambient and elevated) and

Terry, I recently noticed the censor argument of survfit. For some analyses it greatly reduces the size of the resulting object, which is a nice feature. However, when combined with the id argument, only 1 prediction is made. Predictions can be made individually but I'd prefer to do them all at once if that change can be made. Chris ##################################### # CODE # create

Hello, all R-users! I am working on fitting a survival analysis model using the coxph function for Cox proportional hazards regression model. Data look like usual: ========================== group block death censor Group1 1 4 1 Group1 1 12 1 ... Group2 30 4 1 Group2 30 4 1 ... Group3 57 16

Hi. I am using Windows version of R 1.8.1. Being somewhat new to survival analysis, I am trying to compare cph (Design) with coxph (survival) for use with a survival data set. I was wondering why cph and coxph provide me with different confidence intervals for the hazard ratios for one of the variables. I was wondering if I am doing something wrong? Or if the two functions are calculating hazard

Hi all: I'm attempting to create a faceted plot with ggplot2 and I'm having issues with a factor's order that is used to define the facet_grid(). The factor (named total.density) has three levels - 8, 16, and 32 - and I would like them presented in that order. Running order(levels(total.density)) yields the incorrect order of the facet grid - 2 3 1, corresponding with 16, 32, and 8.

Dear R members, I am new in using frailty models in survival analyses and am getting some contrasting results when I compare the Wald and likelihood ratio tests provided by the r output. I am testing the survivorship of different sunflower interspecific crosses using cytoplasm (Cyt), Pollen and the interaction Cyt*Pollen as fixed effects, and sub-block as a random effect. I stratified

Hi all: There was a concern raised by reviewers of a manuscript of mine over the proper execution of a Pearson's correlation. In brief, this was undertaken in order to determine the relationship between the extent of wheel running (y axis) and ethanol intake (x axis) across three, separate 10 day periods in 7 animals. In the paper, the correlational plots for each 10 day-period had 70 data

Dear all, I would have some questions on the coxph function for survival analysis, which I use with frailty terms. My model is: mdcox<-coxph(Surv(time,censor)~ gender + age + frailty(area, dist='gauss'), data) I have a very large proportion of censored observations. - If I understand correctly, the function mdcox$frail will return the random effect estimated for each group on the

Dear list: I am attempting to use what I thought would be a pretty straightforward practical application of Cox regression. I figure users of the survival package must have come across this problem before, so I would like to ask you how you dealt with it. I have set up an illustrative example and included it at the end of this post. I took a sample of 100 data points from each of two populations

Hi, I have a coxph model like coxph(Surv(start, stop, censor) ~ x + y, mydata) I would like to calculate the Schoenfeld residuals for the null, i.e the same model where the beta hat vector (in practical terms, the coeff vector spat out by summary()) is constrained to be all 0s --all lese stays the same. I could calculate it by hand, but I was wondering if there is a way of doing it with

Dear List: I have a data frame prepared in the couting process style for including a binary time-dependent covariate. The first few rows look like this. PtNo Start End Status Imp 1 1 0 608.0 0 0 2 2 0 513.0 0 0 3 2 513 887.0 0 1 4 3 0 57.0 0 0 5 3 57 604.0 0 1 6 4 0 150.0 1 0 The outcome

Dear all, I'm trying reproduce an example of bioassays Yielding Concentration-Mortality Data particularly control - adjustment model from book Bioassay of Entomopathogenic Microbes and Nematodes chapter 7 with R. I used glm with family=binomial and link=probit, but I do not know how to implement parameter gamma (control mortality - mortality of the untreated control insect in this exaple)

Hello all: I am confused about the output from a lm() model with an incomplete design/missing level. I have two categorical predictors and a continuous covariate (day) that I am using to model larval mass (l.mass): leaf.species has three levels - map, syc, and oak cond.time has two levels - 30 and 150. There are no response values for Map-150, so that entire, two-way, level is missing.

Hi, I need a help in a survival analysis using survreg function with weibull distribution from survival package. Look the data sample: ########## Start of script dados <- structure(list(TFD = c(20L, 34L, 1L, 2L, 3L, 3L, 50L, 26L, 1L, 50L, 21L, 3L, 13L, 11L, 22L, 50L, 50L, 1L, 50L, 9L, 50L, 1L, 13L, 50L, 50L, 1L, 6L, 50L, 50L, 50L, 36L, 3L, 46L, 10L, 50L, 1L, 18L, 3L, 36L, 37L, 50L, 7L, 1L,

Dear All, I hope to run some simple survival analysis using the cox-proportional hazard models in R, my command will look like below: cox <- summary( coxph( Surv( mortality , TIME ) ~ Independent variables ) ) My query is about specifying a range of independnt variables in R, such that each independent variable is included as the main defining variable independently of other variables in the

Hello: I would like to run a Cox proportional hazards regression on crayfish dislodgement at different water velocities by crayfish size class and substrate (rock) type. Additionally, there is a covariate variable, rock movement that may be influencing crayfish dislodgment. So... I have crayfish size class (CFSZCL) and substrate type (SUBSZ) as fixed factors influencing the dislodgment of

Dear R users, I tried to analysis the hazard function of some data by coxph function in survival package. The type of the data include "left-censored", "right-censored", "both right-censored and left-censored" (btw, does this has a technical term?), and "complete" ones. I noticed that event (one parameter in "Surv()") might be an indicator for the

I am writing to get a better handle on a warning I am getting from a coxph analysis I am doing. I am analysing age of onset of dementia *after* the onset of parkinson disease. My data looks like: age.park age.dem age.death censor x1 x2 x3 x4 1 76 87 88 0 16 33 E3 E3 2 75 84 84 0 33 36 E3 E3 3 77 81 81 1 NA NA