search for: kalbfleisch

...to calculate the Cumulative incidence for an event adjusting for competing risks and adjusting for covariates. One way to do this in R is to use the cmprsk package, function crr. This uses the Fine & Gray regression model. However, a simpler and more classical approach would be to implement the Kalbfleisch & Prentice method (1980, p 169), where one fits cause specific cox models for the event of interest and each type of competing risk, and then calculates incidence based on the overall survival. I believe that this is what the cuminc function in the aforementioned package does, but it does not...

...Thanks! > > Jen. Not a direct answer, but you might look at Ronald Pruitt's "BIVSURV S LIBRARY", which runs under R, snd the references in the README. Another way to model such data is using survreg with a frailty term -- see the survival and kinship packages. The most recent Kalbfleisch & Prentice (Kalbfleisch J. D. & Prentice, R. L. (2002) The Statistical Analysis of Failure Time Data Wiley, 2nd Edition) has a chapter on bivariate survival analysis (with our example in it;)). David Duffy.

Hi all, Has any body the exprience to iclude a nonparametric component into the survival analysis using R package? *Can someone recommend *me * some ** references? * Thanks a lot Ashta [[alternative HTML version deleted]]

...t is two Rxp matrices, one with the bootstrap estimates of the regression coefficients, and one with the corresponding standard errors. The analysis is up to the user for now. The 'boot' package? 2. 'mlreg': A discrete time proportional hazards model is fitted along the lines of Kalbfleisch & Prentice (1980, pp. 98--103). See also Brostr?m (2002): "Cox regression; Ties withot tears", Communications in Statistics, Theory & Methods 31, 285--297. This function has two methods; "ML", the purely discrete model with one parameter per observed distinct event ti...

...t is two Rxp matrices, one with the bootstrap estimates of the regression coefficients, and one with the corresponding standard errors. The analysis is up to the user for now. The 'boot' package? 2. 'mlreg': A discrete time proportional hazards model is fitted along the lines of Kalbfleisch & Prentice (1980, pp. 98--103). See also Brostr?m (2002): "Cox regression; Ties withot tears", Communications in Statistics, Theory & Methods 31, 285--297. This function has two methods; "ML", the purely discrete model with one parameter per observed distinct event ti...

Dear all, I am looking for a good reference on "Survival analysis". I am looking for a booking containing both applications and Maths. Explaining different methods in survival analysis .... Many thanks Bernard --------------------------------- [[alternative HTML version deleted]]

Dear all, What is the cumulative distribution (with parameterization) used within survreg with respect to the log-logistic distribution? That is, how are the parameters linked to the survivor function? Best regards, Mario [[alternative HTML version deleted]]

Hi all, I am trying to run Weibull PH model in R. Assume in the data set I have x1 a continuous variable and x2 a categorical variable with two classes (0= sick and 1= healthy). I fit the model in the following way. Test=survreg(Surv(time,cens)~ x1+x2,dist="weibull") My questions are 1. Is it Weibull PH model or Weibull AFT model? Call: survreg(formula = Surv(time, delta) ~ x1

Hello, My question concerns model selection, stepAIC(), add1(), and coxph(). In Venables and Ripley (3rd Ed) pp389-390 there is an example of using stepAIC() for the automated selection of a coxph model for VA lung cancer data. A statistics question: Can partial likelihoods be interpreted in the same manner as likelihoods with respect to information based criterion and likelihood ratio tests?

...gs have neither emerged nor died yet. How best to model this data is a larger and harder question. What you have done with coxph, creating an artificial censoring at the time of the competing event, is a first step in competing risks modeling. You might look at the appropriate chapter in Kalbfleisch and Prentice or a similar survival textbook to get a feeling about how to think about the fitted coefficients. I would guess that there is a lot of work on competing risks outside of the survival (censored) data literature, in financial markets for instance, that is appropriate; but I am not f...

Dear all, I'm asking this question again as I didn't get a reply last time: I'm doing a survival analysis with time-dependent covariates. Until now, I have used a simple Cox model for this, specifically the coxph function from the survival library. Now, I would like to try out an accelerated failure time model with a parametric specification as implemented for example in the survreg

I am trying to use R to do loglogistic hazard estimation. My plan is to generate a loglogistic hazard sample data and then use survreg to estimate it. If everything is correct, survreg should return the parameters I have used to generate the sample data. I have written the following code to do a time invariant hazard estimation. The output of summary(modloglog) shows the factor loading of

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 =

Hello! I have a matrix with data and a column indicating whether it is censored or not. Is there a way to apply weibull and exponential maximum likelihood estimation directly on the censored data, like in the paper: Backtesting Value-at-Risk: A Duration-Based Approach, P Chrisoffersen and D Pelletier (October 2003) page 8? The problem is that if I type out the code as below the likelihood

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

For time scale that are truly discrete Cox proposed the "exact partial likelihood". I call that the "exact" method and SAS calls it the "discrete" method. What we compute is precisely the same, however they use a clever algorithm which is faster. To make things even more confusing, Prentice introduced an "exact marginal likelihood" which is not

Dear Community, I have been using two types of survival programs to analyse a data set. The first one is an R function called aftreg. The second one an STATA function called streg. Both of them include the same analyisis with a weibull distribution. Yet, results are very different. Shouldn't the results be the same? Kind regards, J -- View this message in context: