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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Survival model output not consistent with actual data

I am trying to learn Survival modelling using a dummy data. The code is as follows ...
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Comparing between group HR for different age strata

I was asked by a colleague if i knew how to test if between group (cases and controls) HR were higher in one age strata than in another. The design: Matched cohorts of cases (with info on debut age) ...
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Extended Cox regression time dependent variables

I have registry data for treatment with a certain drug for a large number of patients from the years 2005-2012. The main research question is whether the treatment is associated with higher mortality ...
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23 views

Which analysis to be used in case of time dependencies?

So i will give you a background of the study im trying to do. Im trying to model status of the customer booking a particular vacation. They would either get cancelled or they would get a confirmed ...
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14 views

Understanding output from Survival Analysis(Cox's Regression) [on hold]

I am using SAS to run Cox's regression.There is a keyword "survival=" which supposedly gives the estimate of survival function. As far as I understand, the survival function is dependent on time and ...
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25 views

the graph of log(-log) for Cox model on survival analysis

I'm studying Cox Regression model on Survival Analysis. While testing validity of Proportional Hazard model, I will use log(-log) graph method in SPSS. First of all, I mention which procedure I'm ...
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Sign of coefficients in survreg (survival analysis) -part 2

Based on the explanation of interpreting the coefficient of a survreg model here, if a variable (with two levels) has exp{−βTX}=0.496. Is it interpret as a) 0.496-1= -0.504 or 50.4% lower hazard than ...
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45 views

What is the minimum sample size for kaplan meier

I used the "survival" package in R to calculate a Kaplan Meier estimate for survival. An example of my output is like this: ...
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Ranking based on covariates and outcomes

I have a dataset of a high school at-risk program at an private educational institute. I need to rank the efficiency of the tutors here to reward performance and for promotions. How do I go about ...
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Simulating qualitative interaction in survival analysis

I am trying to simulate the survival data that can fit the model: $$h(x) = ho(x) exp (a_0*Treatment + a_1*Treatment*x_1 + a_2*Treatment*x_2)$$ Whereas treatment is an binary variable (0 = control ...
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hazard rate ratio and confidence intervals with zero events

Apologies if this has been asked; I haven't been able to easily find a related thread. I'm doing a survival analysis; how do you calculate hazard rate ratios and CIs when there are 0 events in one ...
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how to chose between using text(_t) and text(log(_t)) when evaluating the significance of time varying covariates

I am running competing risk regression models in Stata and want to test if they fulfill the proportionality assumptions for the models. According to the Cleves et al. book out on Stata press: an ...
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calculate p-value for AFT survival model

I am using Spark ml library to do some survival analysis. Here is the documentation. After training an AFT survival model, I cannot get the p-value directly as in R. What is available for the model ...
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Proportional hazard model, why exp(X*beta)

With covariates $X$ and parameters $\beta$, the Cox proportional hazard model assumption is that the hazard function is $\lambda(t|X) = \lambda_0(t)\exp(X\beta)$, where $\lambda_0(t)$ is baseline ...
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AFT model with Weibull - covariance influence

I am trying to learn Accelerated failure time model (AFT). I am using pbc dataset. When I fit AFT with Weibull distribution with following code: ...
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13 views

Regularized cox survival model with time varying covariates and sparce matrix in R

I was wondering if there is a survival framework in R (or any other language for that matter) for doing the following: Fitting an extended (i.e., time-varying covariates) cox survival model ...
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Textbook approach to modeling non-proportional hazards in the Cox model

In Cox models with time varying coefficients, the effect of covariates on the hazard is allowed to change through time. In cases where a coefficient has a linear relationship with time, I am aware of ...
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Cox-Snell residuals for Cox model with time varying coefficient

I am using the time transform feature of the coxph function in the survival package to model the effect of a time varying ...
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26 views

Hazard ratio with confindence interval for dummy variable

I have covariate Length which is 1 or 0, I'd like to get an estimate with confidence interval for hazard ratio of having an event when ...
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25 views

Can mediation analysis be done with Cox proportional hazards models?

I am trying to understand what types of open source software projects succeed and stay active for a long time and what types die. I want to use survival analysis because some of the projects in my ...
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40 views

Survival analysis with multiple factors

I want to do survival analysis in a situation where I expect the survival time depends on two factors: Environment. Each person is in one of three environments, $E_1,E_2,E_3$. I expect that the ...
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Survival analysis to model waiting times for buses

I need some way to model what is the expected waiting times when someone can take one of a set of buses, with different time/frequency characteristics. Bus 1 - There are 10 buses an hour, but ...
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30 views

Kaplan-Meier vs Cox proportional hazards survival estimates

I am conducting a 20 year longitudinal study on firm survival using a number of variable such as size, profitability, cash resource etc. What is the difference between the Kaplan Meier and Cox ...
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27 views

Modelling Length of Hospital Stay, Poisson Regression or Cox Regression Analysis

I would like to find out what people thought about the better or more widely accepted way to model hospital Length of Stay (LOS) is? LOS can be thought of as count data (number of days) with a ...
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Recurrent time-to-event?

I am working on a research paper looking at hospital readmissions after surgery and I have data for about 200 patients. I have the readmission dates for the first year after initial hospital discharge ...
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Constrained regression for binary dependent variable

I would like to discuss the methodology for the following case: I have a data for several patients over several years for 5 factors describing the health of a particular patient. Every factor ...
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16 views

How to plot adjusted Kaplan-Meier Curves?

I am trying to plot adjusted Kaplan-Meier curves. I know publications like to see something graphical. But using R, I don't know how to go about adjusting for something like age, gender, income when ...
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Variance-Covariance matrix of Weibull Distribution for right-censored data

The probability distribution function, cumulative distribution function and survival function of Weibull distribution are given by respectively, \begin{equation} f(t;\alpha, \beta)= ...
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Weibull Distribution v/s Beta Distribution

I've recently fallen in love with the Weibull Distribution and have gotten a reason to see if there's a mapping of this distribution to an interval (0,1). After plotting the Beta Distribution against ...
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Survival time tie-handling using aareg in R's survival package

I have noticed in the CRAN documentation for the survival package that survival time tie-handling is discussed extensively for Cox-PH regression (allowing for ...
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42 views

Optimal Cutpoint for Predicted Results from Kaplan Meier and Cox Regression

Is there anyway to get the optimal cutpoint for predicted survival probabilities of the aforementioned survival analysis approaches? Something like the optimal cutpoint from ...
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31 views

Can coxph be used for categorical data?

I'm doing some survival analysis and I got the idea to change a binary variable into three-category variable by sub-dividing one category of the binary variable into two new categories. Here's a ...
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What extend does in summary.survfit

I want to extract survival probability from summary.survfit at a specified time for different patients groups. However, for some groups, there is no subject left at the end of the specified time. I ...
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62 views

predict from a Cox model with beta coefficients

I fitted a Cox PH model in R with the survival package and the coxph function. I get the beta estimates from this model. How can I use these coefficients to ...
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40 views

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 ...
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Equal Precisision confidence band not getting sufficient coverage [closed]

I'm looking at confidence bands for the survival function of right-censored data. In particular the Hall-Wellner band and the equal precision band (by Nair). Using the km.ci R package I did a ...
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C-index of a fitted cox model in validation data set

I know that c-index of a cox model can be obtained using survival::survConcordance. Below is an example, ...
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How to get Cox p-value for 20,000 genes?

If you run the following code, you will have a data frame real.dat which has 1063 samples for 20531 genes. There are 2 extra columns named ...
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How to structure data for repeated measures Cox model in R?

I am looking the relationship between a patient's lab values (measured at baseline and post-treatment) and their survival. The lab values are measuring the concentration of the same protein, they are ...
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29 views

Forecasting survival probability using Cox Regression

I'm able to obtain predicted survival probabilities of cox regression using either survfit.coxph or predictSurvProb from ...
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How to generate survival data using R [duplicate]

I want to simulate a sample of size 200, using right-censoring. T follows exponentioal(1) and c follows uniform(0.5,b). choose b so we have 20% of data are right-censored. Thank you
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The effect of prognostic factors on the power in survival analysis

I was curious to know if there exists closed form equations for calculating the power in survival analysis, using either Cox-regression or the log-rank test, when their is an important prognostic ...
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Version of Mann-Whitney U test, for right-censored data

Is there an equivalent of the Mann-Whitney U test, but for right-censored data? I have right-censored numerical data from two different groups. The mean for one group is higher than the other. I'd ...
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Multivariable survival analysis: adding another variable lowers the p value?

When I was performing the Cox survival analysis on my data, I tried to look at the predictive value of different variables to survival. For example, here I have two variables: 'size' and 'surface'. ...
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22 views

Unobserved heterogeneity in Cox model

I have some questions about the Cox model and unobserved heterogeneity. I work on a sample of firms divided in two categories: cooperatives and corporations. I want to test the influence of some ...
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47 views

What kind of model should I use for churn risk prediction?

I have a data set containing many client's id, and its behavior characteristics measured each month before churn or censored. Data looks like: id || lifetime period || folow-up time before churn of ...
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48 views

R: How can I use global average as baseline?

Using Cox regression I'm trying to find the difference in churnrate for different demographic properties for a dataset with millions of records. The data is similar to below: user zip time ...
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134 views

How to make calibration plot for survival data without binning data?

To make a calibration plot for survival probabilities estimated from a Cox model, one can divide the estimated risk into groups, calculate the average risk within a group, and then compare this to the ...
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1answer
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Validity of PH test using Schoenfeld residuals from Cox model on parametric models

I need to use parametric models to model survival data. If the PH assumption holds, I would fit the data for my two intervention groups using a single covariate for treatment effect. I fit a Cox PH ...
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31 views

Error in R code. (buckley james estimator)

I am performing survival analysis (Buckley James estimator), using restricted cubic splines, but in R I get a blank error message and multiple warnings such as this ...