similar to: Prediction in Cox Proportional-Hazard Regression

My question is, how to compute hazard function(H(t)) after building the coxph model. I even aware of the terminology that differs from hazard function(H(t)) and the hazard rate(h(t)). Here onward I wish to calculate both. Here what I have done in two different methods; ##########################################################################################

Hi, In the following results I interpret exp(coef) as the factor that multiplies the base hazard rate if the corresponding variable is TRUE. For example, when the bucket is ks008 and fidelity <= 3, then the rate, compared to the base rate h_0(t), is h(t) = 0.200 h_0(t). My question is then, to what case does the base hazard rate correspond to? I would expect the reference to be the first

Hi, Here is the summary-output of the Coxph-model I used (the output is based on the best final model i.e. all significant explanatory variables and their interactions are included): coxph(formula = Y ~ LT + Food + Temp2 + LT:Food + LT:Temp2 + Food:Temp2 + LT:Food:Temp2) n= 555 coef exp(coef) se(coef) z

I am using a syntax like coxph(Surv(start, stop, event) ~ X, data) to estimate the effect of X, which may change at each measurement (every 6 months). Is there anyone who knows a way to test the proportional hazard assumption in that case? Thank you in advance Jean-François Boudreau Sherbrooke University [[alternative HTML version deleted]]

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 all, Is there an equivalent to predict(...,type="linear") of a Proportional hazard model for a Cox model instead? For example, the Figure 13.12 in MASS (p384) is produced by: (aids.ps <- survreg(Surv(survtime + 0.9, status) ~ state + T.categ + pspline(age, df=6), data = Aidsp)) zz <- predict(aids.ps, data.frame(state = factor(rep("NSW", 83), levels =

Dear R help list, I am modeling some survival data with coxph and survreg (dist='weibull') using package survival. I have 2 problems: 1) I do not understand how to interpret the regression coefficients in the survreg output and it is not clear, for me, from ?survreg.objects how to. Here is an example of the codes that points out my problem: - data is stc1 - the factor is dichotomous

Dear friends, I have used R for some time now and have a tricky question about the coxph-function: To sum it up, I am not sure whether I can use coxph in conjunction with missing covariate data in a model with time-variant covariates. The point is: I know how "old" every piece that I oberserve is, but do not have fully historical information about the corresponding covariates. Maybe you

Thanks for sharing the questions and responses! Is it possible to appreciate how much the coefficients matter in one or the other model? Say, using Biau's example, using coxph, as.factor(grade2 == "high")TRUE gives hazard ratio 1.27 (rounded). As clinician I can grasp this HR as 27% relative increase. I can relate with other published results. With survreg the Weibull model gives a

1. The weibull is the only distribution that can be written in both a proportional hazazrds for and an accelerated failure time form. Survreg uses the latter. In an ACF model, we model the time to failure. Positive coefficients are good (longer time to death). In a PH model, we model the death rate. Positive coefficients are bad (higher death rate). You are not the first to be confused

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),

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 =

Dear All, I am contemplating centering the covariates in my Cox model to reduce multicollinearity between the predictors and the interaction term and to render a more meaningful interpretation of the regression coefficient. Suppose I have two indicator variables, x1 and x2 which represent age categories (x1 is patients less than 16 while x2 is for patients older than 65). If I use the following

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)

Hi, I'm new to R and using it for Cox survival analysis. Thanks to this great forum I learned how to compute the HR with its confidence interval. My question would be: Is there any way to get the p-value for a hazard ratio in addition to the confidence interval? Thanks, Thierry -- Thierry Panje Visiting Student Researcher Department of Psychology Stanford

What is the best way to report the standard error when publishing Kaplan-Meier plots? In my field (Vascular Surgery), practitioners loosely refer to the "10% error" cutoff as the point at which to stop drawing the KM curve. I am interpreting this as the *standard error of the cumulative hazard*, although I'm having a difficult time finding some guidelines about this (perhaps I am

How can I model coxph() in combination with competing risks i.e. I have two events and for event the object will leave the data set. So : Coxph(Surv(time,event)~....) the event is for all my objects 1. How can I model this? Sharon ----------------------------------------------------------------- ATTENTION: The information in this electronic mail message is private and confidential, and only

Dear All, I am currently working with the coxph function within the package survival. I have the model h_ij = h_0(t) exp(b1x1 + b2x2) where the indicator variables are as follows: x1 x2 VPS 0 0 LTG 1 0 TPM 0 1 [[alternative HTML version deleted]]

Good afternoon, I am currently trying to do some work on survival analysis. - I hope to seek your advice re: 2 questions (1 general and 1 specific) (1) I'm trying to do a stratified Cox analysis and subsequently plot(survfit(object)). It seems to work for some strata, but not for others. I have tumor grade, which is a range of 1 - 4. When I divide this range of 1:4 into 2 groups, it

Hello everyone, I am a new user of R. Does anybody know how hazard ratios are extracted for each factor level in a stratified Cox proportional hazard regression model? I have a cancer data set where the variable ?differentiation? is a factor with three levels: poor, intermediate and good. I would like to extract the hazard ratio for each grade level and relate it to another prognostic factor.