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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Extracting coefficients from frailty survival model with sparse=T [migrated]

I am fitting a survival model in R with time-dependent covariates and using frailty.gaussian() for some of the variables. An example call is ...
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What to do if my -log(-log(S(t|x))) kaplan-meier survival function violates PH assumption?

I've already performed survdiff() on my survival curves to find that there is a difference in time-to-germination between my 3 treatments. As far as I'm aware, I will need to run coxph() to be able to ...
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What's the difference between univariate and multivariate cox regression?

I'm dealing with oncology patients so it would be nice to know whether to use univariate or multivariate cox regression. I have some books on survival analysis but they don't elaborate the academic ...
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If my null is rejected when using survdiff(), it means that there is a difference in survival curves for at least one pair of treatments

I am trying to run a survival analysis on my germination data and have been using (McNair et al. 2012, How to analyse seed germination data using statistical time-to-event analysis: non-parametric ...
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Interpreting coxph output with cumulative time dependent covariate

Hoping for some help in interpreting the coxph output in R using the survival package. I am very new to R, but have successfully ...
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Cox Proportional Hazards Model with time-varying covariates data structure [on hold]

I am currently shifting away from a static hazards model to a one with time-varying covariates while using the same original data source. I have some difficulty understanding the data structure needed ...
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Analyzing event occurrences in panel data

I have a dataset with over around 50 firms over 10 years, 8 time varying firm level characteristics as predictor and as response, technologies that they adopted each year. For the response variable, ...
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1answer
24 views

What imputation methods can be used for missing not at random covariate values in a survival analysis?

I'm new to survival analysis and trying to understand how to use it properly. My dataset is a time series dataset where most dependent variable values are available, 2 dependent variable values are ...
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Hazard model for three-tiered-response dependent variable

My time-series dataset is composed of measurements for a number of independent variables and one dependent variable. The dependent variable accepts 3 responses: weak reaction, strong reaction, or no ...
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Interpretation of this Cox Model

I am working on an analysis that model duration of stay (DurStay) in an aged house (HseAge) to having an elevated lead level (Lead_ind).The lead level is measured when the respondent reports to a ...
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Method for analyzing interactions of periodical exposures (drug x drug interaction and likelihood of adverse events)

I am interested in studying drug usage interactions (drug x drug) and its relation to adverse events. I would appreciate help with choosing the right model/method for this type of analysis. The data ...
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Deviance residuals in Cox Model

Based on this article: Click here, the martingale residuals is a sum of each martingale residual per subject from counting process data. So, if deviance residuals in Cox regression are defined by: $$...
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What do the parameters of “frailty” function of R package “survival” mean

What do the methods "aic" and "df" mean in R package survival's function "frailty"? I can guess that aic somehow uses Akaike's information criterion (thus the name, duh), but how does it differ from ...
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1answer
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Cox model for recurrent events (with estimation of residuals / of an individual effect)

Consider some individuals that are followed during a period $T = 1$ (the same for all individuals). The individual indexed by $i$ have $n_i \ge 0$ events, at times $t_{i1}, t_{i2}, \dots, t_{in_i}$. ...
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Intuition behind Hazard Rate

I am confused about the equation definition of hazard rate. I get the idea of what the hazard rate is, but I just don't see how the equation expresses that intuition. If $x$ is a random variable ...
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key prognostic factors are statistical different among cohorts in pooled data

I am building a prognostic model for survival outcome based on data from pooling several clinical trials database. Patients who were recruited in the trials have the same disease and were under the ...
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1answer
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What is the relationship between median disease free survival and median follow-up distribution?

I created a Kaplain Meier disease free survival curve with the following statistics (in months): ...
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1answer
90 views

Lagging/Leading Indicator Length Time

I tried looking this question up on google and didn't find material that answered my question. But my questions are: (1) Is there a method to determine how long it takes a leading indicator to ...
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1answer
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Graphical verification of Weibull distribution

I want to verify whether Weibull is a good candidate for my distribution in survival analysis. So I plot log(t) vs log(-log(Kaplan-Meier). But instead of two lines, I get plot where the lines are ...
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Proportional Hazards Model With Multiple Treatments?

Is there some way to do a proportional hazards model where the "treatment" occurs multiple times? Here's an example: Let's say you are running a Ford dealership where you sell only one car... The ...
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Modeling times-to-event when units may be nested within units

I have a modeling problem and would be grateful if anyone has ideas on its solution. The dataset I am using consists of time-to-event observations for organizational units within government ...
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1answer
58 views

R Survival Analysis - Cox Regression and Cumulative Time Dependent Covariate

Hoping for some help related to a survival analysis using R and the survival package. I've been relying heavily on a series of blog posts done by Dayne Batten, ...
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Applicability of survival analysis (using Cox proportional hazards) to question of interest

I have started down the path of using cox PH models to try to understand which variables are driving time-to-migration (event) in my study system. My system has two groups (A,B) of animals that ...
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Interaction of risk factor with age in survival model with age as time scale

I'm currently looking into survival analysis regression models and can't quite wrap my head around the following: Say I want to model time to occurrence of a cardiovascular event and use age (as ...
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How to simulate time of event from hazard

I am trying to simulate event data based on the results of paper. The paper published the hazard ratios (using a Cox hazard function) of unemployment of different groups. To get the hazard function I ...
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In survival analysis, what is a survival object?

Relating to the Surv function in R. Could you please explain this to someone without an extensive statistics background? I need to know this for my research.
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When using counting process in Cox PH survival analysis in R (survival::coxph), must I use cluster term in the model formula?

I am running Cox Proportional Hazard Model in R, package survival, function coxph(). As I have time-varying covariates, my data is defined as counting process, that is there is one separate data ...
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Using survfit function for Byar Dataset

I have to plot distribution function for 2 causes of deaths of Byar Dataset. In Byar dataset of clustMD, events range from 1 to 8, for several causes of death.I am using the following code: ...
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Computing time dependent variables in Cox regression

For a paper I’m running analyses on factors influencing the rate of adoption of a reporting practice, i.e., the dependent variable is the rate of adopting a specific sustainability reporting standard. ...
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sensitivity/specificity in survival analysis

How do you perform a sensitivity/specificity analysis for survival curves in SPSS or Stata? Is using a standard ROC analysis inappropriate given the presence of time data?
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Tradeoff between Survival probability and detection probability: Mark-recapture

When scientists are using mark-recapture models on an open population model to estimate the survival probability and the recapture probability (also known as "detection"), how can we be sure that the ...
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1answer
30 views

Left truncated survival data?

I am attempting Attrition Analysis in R using the Survival & KMsurv Package. My question is more related to how to use the R package / functionality for my situation. Let us say the analysis is ...
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How to split a survival data such that the proportion of events and censoring are equal in both groups

I need to develop a prognostic model, i have the survival data, and i need to split into validation set and training set. However, I want the Ratio of event to censoring in both sets to be equal. so ...
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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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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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1answer
36 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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1answer
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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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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 ...