Survival analysis is concerned with modelling the time before subjects change state, typically time until death or failure. One key feature of such data is that they can be censored, that is, some subjects will not have changed state before the study ends.

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Handling Informative Censoring in Survival Analysis

In a survival study with informative censoring (for example, studying the effects of cigarettes on mortality and smokers are more likely to be Lost to Follow Up). This causes the censored data to be ...
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22 views

Time-dependent variable in survival analysis using R

I am conducting a retrospective study where I have a cohort of cases who underwent the same surgical procedure. The primary outcome of the study is the recurrence incidence rate during a follow up ...
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52 views

Difference Between Discrete Time Proportional Hazards and Logistic Regression

My data consists of one row per person, per month that person was "exposed" to an event. So the month is the discrete time and the row corresponds to one "person-month". There are a few independent ...
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20 views

Computing hazard ratio between groups adjusting for other confounding variables

I have groups formed based on BMI; consider the standard Underweight, Normal, Overweight, etc. groupings for BMI as you might see for classification. I'd like to form a hazard ratio for comparing any ...
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1answer
38 views

Consequences of violating proportional hazards assumption in Cox model

What are the consequences of violating the Proportional Hazards assumption in a Cox Model? I've got a Model where two factors are highly significative, but all the estimated betas associated to the ...
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23 views

Checking Cox model assumptions with multiple imputation

I have run multiple imputation using MICE. I would now like to run a Cox model on it (using with,pool), and make sure that is justified. That is, I need to make sure that the proportional hazards ...
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1answer
108 views

Fluctuations in hazard function at high (x) values

Using a best-fit algorithm, i've obtained gamma-distribution parameter MLEs for my data (scale and shape). When evaluating the hazard function, calculated as the PDF divided by the reciprocal CDF, ...
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14 views

Best way to represent survival data

I have a question regarding the best way to represent survival data to predict the reliability of bike parts. I have the manufacturing date of each bike. At the manufacturing data the bike is ...
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5 views

Fitting a parametric model to a survival curve? [closed]

I'm trying to fit a Weibull model to the following data, but neither Stata nor R accept survival data not in count format to fit a model. What should I do? ...
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52 views

New rare-event regression method somewhere between logistic and survival

I keep running into situations (in my job) where I need to predict relatively rare events that occur at most once per entity, across many entities, and over time (e.g. predicting mortality of cancer ...
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16 views

Determining number at risk: Three possible variants with different results

I try to figure out what the best way of determining the number at risk is. I came up with three options (see below). However, the numbers at risks vary between the options (#1 and #2 versus #3) and I ...
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15 views

Survival Analysis for Marketing Spend Retention

I have yearly cohorted data of customer registrations. The cohorts are 2013, 2014, etc. Each month, a certain number of customers who registered will make their first or nth purchase. I am trying to ...
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1answer
25 views

Vectorized Implementation of Cox PH Model Scoring Process

I created a Cox Survival model and for my purpose I need to use the mean instead of the median. In order to test the results of the model, I need to calculate a mean prediction for every record in my ...
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7 views

How to estimate the occurrence time of an event for scheduling purposes precisely?

I working on a system in which a number of requests arrive in random time. Consider the following timeline: In the above example, we have a certain number of components: ...
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5 views

How to determine covariates that predict presence of a disease at a certain age?

I'm familiar with survival analysis. I was asked to find variables that predict being free from hypertension (ie having normal blood pressure) at age 50. Data is longitudinal with repeated ...
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2answers
28 views

Reasonable approach for modelling churn (survival) and choice of intervention campaign (multinomial regression)?

I've only recently moved into customer analytics, and would love to get some advice around designing a reasonable approach to modelling my data. I want to be able to predict customer churn (that is, ...
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1answer
18 views

Question about Dxy in cox regression model validation

I am using RMS package of R to validate cox regression model with bootstrap. Please see the R code below. I am predicting 1, 2, 3, 4, 5 years survival. Is the optimism corrected discrimination index ...
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6 views

Censored data without the indicator

Can we talk about censored data without observing the indicator? Normally we observe $Z_i = T_i\wedge C_i,$ where $C_i$ is the time of censoring, and $\delta_i = \mathbb{1}(T_i \leq C_i),$ the ...
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1answer
26 views

Survival analysis: log rank or ANOVA to compare groups?

I have a survival analysis model where I want to know what are the survival probabilities of cancer patients with different types of mutations. I am using the Kaplan Meier estimate, and below is a ...
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38 views

Do Oscar winners live longer? How account for length and time-dependent bias

I read a bunch of references on the bias that one should beware of when analyzing survival times. One classic example is the one of Oscar winners: a miss-specification of the model leads to two kind ...
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30 views

Confirm correct process to use Weibull regression for reliability analysis

I am new to the field of survival analysis. I am trying to fit a Weibull distribution to predict the reliability of a part. I have information from machines that have that part. On some machines the ...
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67 views

Resampling to subset (jackknife) or bootstrap for survival analysis

This is a question regarding survival analysis and bootstrap: Suppose I have a time-to-event dataset with 1000 subjects, and based on this dataset I can obtain some measures or plots (not standard, ...
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3answers
47 views

Combining adjusted survival estimates with multiple imputation

I've constructed a Cox PH model using multiple imputed datasets in SAS. Now I would like to estimate adjusted survival curves for each treatment group (main variable in the model). Is there a ...
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How is the hazard ratio in Cox proportional hazard models affected by the case sampling?

I'm trying to conduct survival analysis using Cox proportional hazard models, looking at a biomarker for heart disease. Age is a major risk factor, so I'm modeling the age as the time scale (counting ...
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156 views

Survival rate trends in case-control studies

I submitted an article which was rejected due to the improper way of performing the survival analysis. The referee left no other details or explanations other than: "survival analysis on time trends ...
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1answer
32 views

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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28 views

Survival analysis with Frailty on large dataset

I am trying to fit a survival analysis in R with non-recurrent events and time-varying coefficients. The baseline distribution is exponential or Weibull and the ...
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35 views

How to create a Kaplan-Meier plot using ggplot2 with the number at risk table beneath [duplicate]

I want to create a Kaplan-Meier plot using ggplot2 with a number at risk table beneath. The number at risk table should be aligned to the x-axis ticks of the Kaplan-Meier plot. Here I found how to ...
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16 views

Replicate survival analysis created originally in SAS JMP in R

I need to replicate a survival analysis that was created in SAS JMP in R. Does anyone have experience with SAS JMP and could point me to the best package that would yield similar results in R?
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44 views

Oncology: how to estimate the survival curve?

I have the age at diagnosis for 58 cancer patients and I want to build a survival model. I want to know at a given age with no prior cancer diagnoses, what is the cancer free survival? I know that ...
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34 views

Implementing random forest to predict the success rate and time of completion

I have a large dataset with more than 120 columns for which I'm trying to classify whether the order will be successful or not. There are two parameters that I need figure out. One is the probability ...
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42 views

Why is the derivative not zero?

I am working with survival models and I am using R's coxph function to learn the Cox proportional hazard model. To try it out, I am using the standard veteran dataset (obtainable by loading the ...
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6 views

Power analysis for spatially structured variables in survival analysis

I am working with colleagues on an analysis of survival in a chronic neurological disease. There are several well known factors that influence survival. We have data from a good register which ...
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53 views

Are this two definitions of survival equivalent

Let $\lambda_i(t)$ denote hazard of the $i$-th individual at time $t$ and let $\lambda(t)=\frac{\sum_{i=1}^n S_i(t) \lambda_i(t)}{\sum_{i=1}^n S_i(t)}$ be the weighted average of individual hazards. ...
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Non-independent variables (covariates) in survival analysis

I want to perform survival analysis, and compare at which of 3 spatial scales habitat variables affect the most nest survival. I thought to perform analysis separately for each spatial scale and ...
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27 views

Censoring “Death” in Time-To-Recovery Analysis

I am performing time-to-recovery analysis comparing 2 groups. In both groups, a few subjects died from the disease under consideration (instead of recovering). Is it appropriate to consider the deaths ...
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6 views

KM plots with PFS > OS

I'm plotting KM curves for OS and PFS for the same population. There is not much difference between the groups, ie. most of the "events" for PFS are actually death, not progression but there are a few ...
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9 views

Cox model with known measurement error

I am trying to perform a univariate Cox PH survival analysis in R. My covariate was generated from another analysis, meaning that I have a 95% CI range (between 0 and 1) for the values. Right now, I ...
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16 views

developing a prediction model for HIV outcome in cox regression using cross validation/GCV

During the application of cross-validation in sufficient large dataset (say 6000), is there a recommended ratio to split the data in to learning/training and testing/validation data set? I have seen ...
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21 views

Type one right censored data [duplicate]

Does anyone have any idea about how to simulate censored data in R? For example, generate a sample of 200 with 30% of that Type 1 right censored, based on Weibull distribution. (30% of 200 = 60).
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79 views

Survival analysis with cures when it is known that for some subjects the event (death) will never occur

Say we have the following set up. At time t=0 there are N infected patients. There is a treatment which, if taken until t=T, cures 100%. However, some patients will be cured before t=T while others ...
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21 views

Calibration and validation of an accelerated failure time model on new data

I have an existing AFT (accelerated failure time) model which I'm using on a new dataset, with the obvious intent of testing whether the model predicts the new data well. A first step is to look at ...
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29 views

AUC of Survival ROC smaller than 0.5

If we obtain an AUC < 0.5 analysing a binary response variable (0/1) with a marker, this means the negative values of marker is associated to the value 1 of the response. We could use the trick to ...
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2answers
23 views

Competing hazards for event that makes the event of interest more likely

Suppose I'm Netflix. I'm using survival analysis methods (kaplan-meier curves) to study when my customers decide to cancel their subscription. However, I've noticed that customers that experience ...
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24 views

What is the R code for a 2 one-sided equivalence log-rank test? [closed]

I am trying to perform an equivalence test (alpha = 0.1, beta = 0.8, equivalence interval = 0.85-1.25) for two samples of survival data. Is there a code in R in the Survival package or any other ...
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1answer
20 views

how to deal with unavailable survival data

I'm writing a retrospective study presenting an overall survival analysis comparing two groups of patients. I implement a Cox regression model with some covariates and then present the results in ...
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30 views

simulate censored data (right censored) using R statistics [duplicate]

I want to generate right censored data, but I want to be able to pass in a parameter to a function to dictate that a certain percentage of the data will be censored. I have found this R-package: ...
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1answer
39 views

Hazard estimate of 'muhaz' function?

library(muhaz) nsam = 5000 time <- rexp(nsam,4) cause0 = rbinom(nsam,1,.75) haz = muhaz(time,cause0) plot(haz) I simulated failure time data which is ...
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How do I solve for Weibull parameters given a failure function's pmf and cdf values?

I am trying to find the parameters for a Weibull distribution to model data I have on retention for my company's subscription packages. The data I have is the failure and survival rates from month to ...
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Within-sibling-pair survival model

I'm modeling a time-to-event outcome (death) in a setting where the key independent variable (education) is measured for each member of a sibling pair. If I were simply interested in determining ...