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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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What is the link between regression Poisson estimates and piecewise exponential model?

Survival estimates with a piecewise exponential model can be obtained with survival Poisson regression. How can we check this equivalence? Below is a reproducible example : ...
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R package `survival`: the estimated parameters are off

I simulated some survival data and used the R package survival's function coxph to estimate the parameters, but they are ...
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Poisson Regression vs. Exponential_Weibull vs. Cox Regression vs. Negative Binomial

Problem: I would like to predict the number of days a student continues with school using student and school level covariates (no censor data). Data includes the number of days that the student ...
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Interpreting this KM Curve

I would like someone experienced to look at my KM curve. I have categorized a continuous clinical variable into 2 groups based on its median. The Log rank test is significant (p=0.0052). I have then ...
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Using multiple linear regression instead of survival analysis to estimate time until event

I have just started to read about survival analysis, I have read in this interesting article about survival analysis, and why use it rather than the famous multiple linear regression to estimate the ...
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Hazard function equality $T = \min(T_1, T_2, …, T_n)$ [on hold]

Let $T_i, i = 1, ..., n$ be independent continuous random variables. Denote by $h_i(t)$ the corresponding hazard function of $T_i$. Let $T = \min(T_1, T_2, ..., T_n)$. Denote by $h_T(t)$ the ...
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Comparing coefficients of survival analysis with Gompertz distribution in R

I am performing Gompertz analysis of survival to calculate initial mortality rate (IMR) and Gompertz coefficient (rate of ageing) of different strains of worm. I am doing this by using the package ...
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Time ratio of AFT generalized gamma parametric regression in survival analysis

In Applied Survival Analysis: Regression Modeling of Time-to-Event Data, 2nd edition, Ch8, page262. Hosmer, Lemeshow and May obtained the time ratio of Weibull regression model, according to the ...
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Log-rank test chi-square and z values

I start saying that I am not still skilled enough at statistics and I am just studying how to apply particular methods to my data. I run a survival analysis using by a statistical software. Looking at ...
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Compare samples with noisy data and maximums

Summary: I collected psychophysical data (i.e. yes/no responses to physical stimuli) testing the ability to feel a touch stimuli. I used a Bayesian algorithm to select the stimuli (30 trials per ...
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25 views

Cox regression - unknowns within covariates

I have run a model for duration in treatment. Records have either exited treatment or are still in treatment. If exited, the number of days is known. If still in treatment, the number of days in ...
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How to setup Time series model to predict time left to a binary event?

I have seen questions like this before on stack exchange, such as here and here, but most are either left unanswered, or the ones with answers (like the second link) are rather vague. Is there a ...
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Modelling Inverse Gaussian Distribution for Survival Data in R

I am fitting survival data with different distributions in order to determine what best characterises my data. I believe that the inverse gaussian distribution may be a good fit for my data but I have ...
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Real 1 dimensional non-binary data where Cox Proportional hazards model fails

I am learning about the Cox Proportional Hazards model and understood that it is very flexible, even when the assumption of proportional hazards is not met, e.g. for additive hazards. But say we have ...
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30 views

multivariate truncated survival analysis

I have many short time series (1-5 data points) that document the development of morphological traits (length and pigmentation) of some lab critters in response to different dietary supplement. I ...
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Cox Regression Specifics: Standardizing, Log Transforming

In the statistical analysis of this paper I have some questions regarding their approach. https://academic.oup.com/ndt/article/33/6/1001/3978817 “Variables with non-normal distributions were either ...
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Generating Failure / Suspension data from Weibull Distribution

I've been trying to make some synthetic failure data in MATLAB and I'm coming across an issue when I try to refit the Weibull distribution from my synthetic data. For example, assuming a Weibull ...
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Is there a standard way to treat events with unknown times (missing survival time data)?

Suppose we are studying some event and the observations are the pairs: time and indicator whether the event has already happened at this time. We have one observation per subject. No events happen ...
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How to statistically split a set of numbers into two groups for Kaplan-Meier plot?

I want to perform a survival analysis for the cohort of breast cancer patients. For each patient, I know whether he was right-censored or not and what was his survival time (or the end-of-study time ...
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Using R to maximize a two parameter Weibull model via multivariate extension of Newton-Raphson method

I am just getting back into using R for the first time in a while, and wrote some code to perform the aforementioned task in the title. I was wondering if anyone could take a look at it and see if ...
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Predict hazard relative to a specific sample

I want to predict the hazard ratio and 95% confidence intervals relative to a specific sample rather than the population mean: ...
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Question regarding the Supervised Principal Components method

I'm going over Bair's and Tibshirani's Supervised PC method and looking at their R package tutorial here. In their paper, in the section titled "A Breast Cancer Example," it seems they find the most ...
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using survival censoring indicator as a binary outcome for ROC curves and logistic regression

I wonder how acceptable it is, pros and cons, of using the censoring indicator with survival data as a binary outcome for ROC curves and logistic regression. One issue is if we have early dropout / ...
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Is it possible to use Survival Analyis on a time series to predict events?

I have a time-series of noisy data which occasionally triggers an event, and once that happens, the noise calms down and the cycle repeats itself (until the event is triggered once again). What I want ...
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1answer
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Cox time-dependent coefficient continues to violate the PH assumption

After determining that my Cox model with a particular covariate (drug) was violating the proportional hazard assumption, I took steps to incorporate a time-dependent continuous coefficient given how I ...
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1answer
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Can the hazard function be defined on a continuous state

The hazard function is defined as the instantaneous failure rate or instantaneous hazard rate as $\Delta t$ tends to zero $$ h(t)= \lim_{\Delta t \to 0} \frac{R(t)-R(t+\Delta t)}{\Delta t * R(t)} $$ ...
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LASSO Cox Model after multiple imputation

I want to develop a predictive survival model on a data set with about 8000 subjects and 38 covariates. About 4% of subjects had the event of interest. There are 21 variables with missing values, ...
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Survival Analysis: Number of Censored Events in Life Table [closed]

I want to construct a life table for some event of interest. See below for an example: I'm trying to do this with survival R package: https://cran.r-project.org/...
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71 views

Clarification for LASSO based Cox model using glmnet

I am trying to find a variable signature associated with a characteristic. Particularly I am looking to get a prognostic model from multi-variable data for gene expression. I have the "Time (survival ...
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1answer
28 views

Continuous vs Categorical covariate of interest in Cox Regression

I will use aliases throughout to explain my results in brief and a few questions that have propped up in the process. Suppose I'm interested in the association of baseline measurement of blood ...
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24 views

Survival prediction

I want to estimate the hazard ratio for a case-control study, and I want to test how the value of a continuous covariate affects survival. Does the covariate have to be normally distributed?, that is ...
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1answer
20 views

Percent Censoring in A Cox Regression

How much right-censoring in a PH model is acceptable? For example, my summary of the number of event and censored values is Total: 450 Events: 200 Censored: 250 Percent Censored: 55.6% PH ...
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28 views

Cox proportional hazards model with interaction terms

I am looking at a survival analysis using Cox PH models. I am interested in the effect of a continuous variable V1 on survival. I want to look at interaction terms with my model. I believe variable ...
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2answers
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What survival model is suitable for not comparing groups?

I initially used Cox PH model for my data for different subgroups of my population, however I do not want to make any comparisons between groups, rather i need to describe the time to event for each ...
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Fitting Probability Distribution to Failure Data with discrete, right censored stress

I'm a bit unfamiliar with survival analysis and I'm struggling to find examples of the particular problem I wish to tackle (which I don't think is particularly unique actually). Imagine I'm doing a ...
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How can we analyze a dataset with a pseudoreplication problem?

I am trying to analyse a data set with a pseudoreplication problem and would be grateful if someone could give me some guidelines on the best way to deal with it. We have a set of subjects exposed to ...
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1answer
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Are cancer studies cases always left-censored?

I'm used to traditionnal studies with plain old Cox PH models and right censoring. But lately I was wondering about the outcome date in cancer studies. Indeed, the date we usually use as outcome ...
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1answer
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Confidence interval of Kaplan-Meier and Cox analysis

I have a question about the 95% confidence interval of KM and Cox. Excuse me that I am not familiar with their deep statistic theory but I thought the p-value, HR and 95%CI should be close to each ...
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1answer
27 views

5-year EFS > 5-year OS

I had a data of 24 cancer patients; their 5-year overall survival (i.e. event as death) was around 57%, however, when I calculated 5-year event-free survival for the same cohort, defining the event as ...
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how to interpret output of non-mixture cure model in R?

I focused on cure models with log logistic distribution and logistic link therefore I utilized flexsurvcure package. I have 3 questions. 1-When I run the following codes to fit cure model, values of ...
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1answer
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Accounting Student taking Master in Stat, need your help regarding taking subjects

I had a Bachelor in Accounting and now I'm doing a Masters in Statistics. This is my first semester and I have to Choose at least 3 out of 6 offered subjects for this semester. These subjects Are: ...
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Jags: Survival analysis using CJS and multiple covariates

I want to investigate three fixed group effects as covariates in a Cormack-Jolly-Seber style survival analysis using Jags in R. I have successfully developed a separate model for each covariate; ...
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1answer
38 views

Combining probabilities to find most probable window

I have a series of observations, with an associated probability that an event is occuring at timestep t, something like: [0.8, 0.8, 0.3, 0.9, 0.2] Events can ...
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aregImpute or mice for imputation of survival data

I would like to use multiple imputation to analyse associations between an exposure variable (exp) and different disease risks in a dataset with some missing data (...
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Error for Validation of Cox model with extern dataset using rms val.surv by Frank Harrell [closed]

i developed a cox model for cancer overall survival with the rms-package. I use a with 765 observations (194 intern, 571 extern). So I divided in two datasets: Training and Validation I get a correct ...
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1answer
32 views

Interpreting glmnet cox coefficients

There have been similar questions regarding interpretation of glmnet results. However this is more specific to the cox part of the package. I am trying to create a prognostic score for cancer ...
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Survival model building with time dependent covariates

I am looking to build a Cox proportional survival hazard model in SAS on time dependent covariates. Ultimately, I will put this model in production and score on recurring basis to target the right ...
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1answer
64 views

Connection between Hazard Ratios, Survival, and Probability

According to this wiki link, the hazard ratio relates to survival according to the following equation $$ S_1(t)=S_0(t)^r \quad (1) $$ where $S$ is the survival and $r$ is the hazard ratio. So from ...
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Equivalence of Kaplan-Meier estimator and EM algorithm

For right-censored failure time data, in a non-parametric setting, is there an equivalence between using the EM algorithm (i.e. calculating the expected log-likelihood and maximizing) and the Kaplan-...
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Large discrepancy between complete-cases and imputed data

I would like to conduct a survival analysis using a dataset with approximately 12,000 participants (1100 events). However, complete data are available for only 9500 participants (820 events). I have ...