Questions tagged [survival]

Survival analysis models time to event data, typically time to death or failure time. Censored data are a common problem for survival analyses.

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Right-censored survival fit with JAGS

Update: I got the JAGS model running and this eliminates the distracting part of my question. It's really about the proper preparation of data for dinterval() and inits. I can't find a concrete ...
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Which algorithm to compute p-value of logrank test with three or more groups is best?

There seem to be two different algorithms for comparing three or more survival curves using the logrank test. Algorithm A. Found in books by Altman and Machin. Computed by GraphPad Prism. This ...
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What does muhaz return?

A pretty basic question. I have read somewhere that the muhaz function in muhaz package will return the baseline hazard rate for COX model. The muhaz document states that it "Estimates the hazard ...
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log-rank test in R

I need to use the survdiff function to statistically compare (using log-rank test) the following survival functions: (1) Male (Sex=1) and Female (Sex=2) (2) ...
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Cox regression when reference group had zero events

I would appreciate some advise on an a problem I ran into. I use SPSS for statistical analysis of a study. The study look into how a blood test predicts mortality with patients followup of 1 year. ...
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Performing contrasts among treatment levels in survival analysis

I'm reviewing a paper where the authors compare the survival of an insect fed on three different diets. Following the survival analysis (using Surv) they perform ...
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Assumptions and pitfalls in competing risks model

Question: What are the major pitfalls and assumptions for competing risk analysis? Background I'm trying to learn competing risks and I use the cmprsk package in ...
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I’m reviewing an article, and can’t give details but here is the situation, and it’s got me puzzled Patients were divided into 4 categories (call them A B C and D), which were exhaustive and ...
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EM algorithm R code on Cox PH model with frailty

Let say I have a 'kidney catheter' data set. Data are about the recurrence times to infection, at the point of insertion of the catheter, for kidney patients using portable dialysis equipment. ...
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Recurrent event analysis

I want to model patient visits. My assumptions are: Patients visit the hospital until they stop visiting at all. I don't know if their last visit was the last one. Patients visit at certain intervals....
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Relative importance of variables in Cox regression

I've understood that relative importance of predictors is a tricky question. Suggested methods range from very complex models to very simple variable transformations. I've understood that the ...
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What’s wrong with this way of fitting time-dependent coefficients in a Cox regression?

I have a Cox proportional hazards model. Judging by Schoenfeld residual vs. time plots and corresponding tests for zero slope, there is clear violation of the PH assumption for several of the ...
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Is this a problem for Survival analysis?

I have a dataset of individuals. Each individual has the same start time at which we begin observing them. There is also an end time for all individuals. Some individuals fail before they reach the ...
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Prediction on individual cases in survival analysis

It seems that survival models are used mostly to describe (not predict) the change in survival probability over time for all cases or each class (e.g. men vs. women). What I'm interested in, however, ...
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How to determine the cut-point of continuous predictor in survival analysis, optimal or median cut-point?

everyone! I want to do overall Recurrence-free survial analysis for one continuous predictor with Kaplan-Meier method, but the determination of the cut-point for groups really confused me. Most ...
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When (and why) is a conditional logistic regression equivalent to a Cox proportional hazards model?

In the the help for the clogit function in the survival package in R, the details section ...
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Why is coxph() so fast for survival analysis on big data?

I frequently do survival analysis on large data sets. One million samples or more is typical, and this seems to be much more than typical research usage. Many algorithms I've used are prohibitively ...
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Measuring length of intervention effect

I ran a study in which participants were randomized to either a control or an intervention, with outcomes in the form of time-to-event data. While overall time-to-event is shorter in the intervention ...
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Confidence intervals of fitted Weibull survival function?

I'm implementing a Weibull survival analysis fitter, and have successfully estimated the parameters and their standard errors. I can also produce the fitted survival curve. My question is how can I ...
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Expected survival time from log-logistic survival model in R from survreg

I am currently estimating a survival model (specifically, accelerated failure time model) with a log-logistic distribution using the survreg function in the ...
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How would you visualize the difference between Cox/Weibull regression?

I'm trying to figure a way of properly displaying the difference\resemblance between various regression values on the same data set, using cox ph, weibull regression and log-normal regression. ...
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Survival analysis with categorical variable

I have event time data for subjects with different categories (A, B, C etc.) yearly observed. To my understanding my data is both right and interval censored (?). Subjects' category can change from ...
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Are survivor functions meaningful with proportional hazards models?

Does the survivor function estimated after running a Proportional Hazards model give valid predicted probabilities of an event happening after a number of time periods? The only source I've found on ...
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How to compare Harrell C-index from different models in survival analysis?

In a dataset with survival event, I calculated Harrell C-index from three different models. Furthermore, I calculated the 95% C.I. for the three different models. So the next question is to compare ...
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time varying coefficients in cox proportional hazard model

I am trying to fit a coxph model in R. The study can be described as follows: I have a very large dataset, in counting process form, containing whether or not someone responded to a survey or not. ...