similar to: retrieve p-value from a cox.obj

He, I used the "coxph" function, with four covariates. Let's say something like that > model.1 <- coxph(Surv(Time,Event)~X1+X2+X3+X4,data=DATA) So I obtain the 4 coefficients B1,B2,B3,B4 such that h(t) = h0(t) exp(B1*X1+ B2*X2 + B3*X3 + B4*X4). When I use the function on the same data > predict.coxph(model.1,type="lp") how it works in making the prediction?

Hi all: I finished cox analysis like this: fit_cox<-coxph(Surv(dat$Time, dat$death) ~ dat$CD4 + strata(dat$gender),data=dat); > fit_cox Call: coxph(formula = Surv(data_ori$Time, data_ori$death) ~ data_ori$drug + strata(data_ori$gender), data = data_ori) coef exp(coef) se(coef) z p data_ori$drugddI 0.216 1.24 0.146 1.47 0.14 Likelihood ratio test=2.17 on

I am calculating cox propotional hazards models with the coxph function from the survival package. My data relates to failure of various types of endovascular interventions. I can successfully obtain the LR, Wald, and Score test p-values from the coxph.object, as well as the hazard ratio as follows: formula.obj = Surv(days, status) ~ type coxph.model = coxph(formula.obj, df) fit =

Hi, I wanted to do the cox model using a matrix. The following lines illustrate what I want to do: -------------------------------- dat <- matrix(rnorm(30), ncol=3,dimnames = list(1:10,letters[1:3])) Survival <- rexp(10) Status <- ifelse(runif(10) < .7, 1, 0) mat <- as.data.frame(cbind(dat,Survival,Status)) cmod <- coxph(Surv(Survival, Status) ~ a+b+c, mat)

Dear R-users, I would like to determine the probability of event at specific time using cox model fit. On the development sample data I am able to get the probability of a event at time point(t). I need probability score of a event at specific time, using scoring scoring dataset which will have only covariates and not the response variables. Here is the sample code: n = 1000 beta1 = 2; beta2 =

Hi, I am just wondering if there is a test available for testing if a linear fit of an independent variable in a Cox regression is enough? Thanks for any suggestions. John Zhang ____________________________________________________________________________________ [[elided Yahoo spam]]

The combination of survfit, coxph, and factors is getting confused. It is not smart enough to match a new data frame that contains a numeric for sitenew to a fit that contained that variable as a factor. (Perhaps it should be smart enough to at least die gracefully -- but it's not). The simple solution is to not use factors. site1 <- 1*(coxsnps$sitenew==1) site2 <-

Hi, I'm using a Cox-Regression to estimate hazard rates on prepayments. I'm using the "pspline" function to face non-linearity, but I have no clue how to interpret the result. Unfortunately I did not find enough information on the "pspline" function wether in the survival package nor using google.. I got following output: * library(survival)* > > > >

Hi, May I know how to obtain the confidence interval of the survival curve of weighted Cox regression model? I tried coxph, cph, and coxphw, but they did not work. Any help would be much appreciated. Thanks, Jeanne -- View this message in context: http://r.789695.n4.nabble.com/Confidence-interval-of-Survival-Curve-of-Weighted-Cox-Regression-tp3308085p3308085.html Sent from the R help mailing

I'm getting unexpected results from the coxph function when using weights from counter-matching. For example, the following code produces a parameter estimate of -1.59 where I expect 0.63: d2 = structure(list(x = c(1, 0, 0, 1, 0, 1, 0, 1, 0, 1, 0, 1, 0, 1, 1, 0, 0, 1, 0, 1, 0, 1, 0, 1), wt = c(5, 42, 40, 4, 43, 4, 42, 4, 44, 5, 38, 4, 39, 4, 4, 37, 40, 4, 44, 5, 45, 5, 44, 5), riskset =

Suppose I have a dataset contain three variants, looks like > head(dta) Sex tumorsize Histology time status 0 1.5 2 12.1000 0 1 1.8 1 38.4000 0 ..................... Sex: 1 for male; 0 for female., two levels Histology: 1 for SqCC; 2 for High risk AC; 3 for low risk AC,

R-helpers: I have a dataset that has 168 subjects and 12 variables. Some of the variables have missing data and I want to use the multiple imputation capabilities of the "mice" package to address the missing data. Given that mice only supports linear models and generalized linear models (via the lm.mids and glm.mids functions) and that I need to fit Cox models, I followed the previous

Hello All, Am having some trouble computing Type III SS in a Cox Regression using either drop1 or Anova from the car package. Am hoping that people will take a look to see if they can tell what's going on. Here is my R code: cox3grp <- subset(survData, Treatment %in% c("DC", "DA", "DO"), c("PTNO", "Treatment", "PFS_CENSORED",

Hi, Can anybody tell me what the message below means and how to overcome it. Thanks, Neil Warning message: X matrix deemed to be singular; variable 2 in: coxph(Surv(age_at_death, death) ~ project$pluralgp + project$yrborn + ......... >

Hello, I might be barking up the wrong tree here, but I want to make sure I have a full understanding of this. What I would like to know is what tests are performed to give the p-values for each variable in the table that is the result of coxph regression when the variables are categorical only. More specifically, when expected counts are less than 5 is the Fisher's exact test used instead

Hello R-community, It's been a week now that I am struggling with the implementation of a cox model in R. I have 80 cancer patients, so 80 time measurements and 80 relapse or no measurements (respective to censor, 1 if relapsed over the examined period, 0 if not). My microarray data contain around 18000 genes. So I have the expressions of 18000 genes in each of the 80 tumors (matrix

Hi, When I run the following Cox proportional hazards model on the Mayo clinic's PBC data set (given in the "survival" package), the regression coefficients do not agree with the results presented in Table 4.6.3 (p. 195) of Fleming & Harrington's book. library(survival) data(pbc) ans.cox <- coxph(Surv(time, status) ~ log(bili) + log(alb) + age + log(protime) +

A.S.: I am sorry for sending my first mail to <r-help at R- project.org>. --------------------------------------------------------- Hallo, computing a Cox proportional hazards model in SPSS 9.0 and R 1.2.2 produces different results for beta-coefficient. I use the follwing data set (source: example in help(coxph), somewhat modified) Time Status Covariate (x)

Hi, I'm reading van Belle et al "Biostatistics" and trying to run a cox test using a dataset from: http://faculty.washington.edu/~heagerty/Books/Biostatistics/chapter16.html (Primary Biliary Cirrhosis data link at top of the page), I'm using the following code: --------------- start of code library(survival) liver <-

Dear r-users, I want to use the Cox's ph model to analyze survival data set. How can I extract the model coefs. and Wald test p-value or Score? For example: I use the data set melanom in iSwR package. > library(survival) > data(melanom) > attach(melanom) > cox.model <- coxph(Surv(days,status==1)~sex) > summary(cox.model) Call: coxph(formula = Surv(days, status == 1) ~