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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Survival analysis with time-varying covariates observed only once
I am currently researching fish survival analysis. The weight and number of live fish were observed until the end of the rearing period, and the number, weight, and timing of dead fish during the ...
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Retrospective survival analysis censoring
I'm doing a retrospective survival analysis where I evaluate the recurrence of disease after surgery. The problem is that some of the patients don't come again to the clinic after surgery to do the ...
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Survival analysis vs Cumulative incidence vc Incidence rate for cohort with varying baseline dates
Research question:
I am interested in analyzing in R whether people with a specific level (out of four levels) of a blood parameter BP have a higher risk for developing a specific disease D.
A ...
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Testing the proportional hazards assumption with a time varying covariate
One of the cohort studies I have carried out recently involved examining the association between post-diagnostic beta-blocker use and breast cancer outcomes in a cohort of breast cancer patients. ...
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Cox Regression: handling a time interval of drug administration when only given once
I would like to investigate the effect of a drug on survival with Cox regression. The drug can be administered either once or twice. In the univariate Cox regression, I see that a shorter time ...
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Time-varying cox survival analysis with unknown age
I'm using coyote telemetry data to understand the impacts of various environmental characteristics on their survival. The data I have includes the locations of a few hundred animals throughout their ...
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Comparison of Kaplan-Meier and Cox models for survival analysis
I am looking at the results for survival analysis for leukaemia patients, categorised based on Copy Number Variation (CNV) levels using array comparative genomic hybridisation. So far I have plotted ...
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How to calculate Table1 for weighted data? [duplicate]
In many scientific papers, covariate balance is presented in Table 1 before and after weighting (e.g. IPTW, Overlap weights, etc), stratified by treatment group.
Continuous variables, for example, are ...
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Methods for fitting a distribution to regression data
I'm hoping to find a method/algorithm/approach for fitting to a distribution to regression data.
Essentially I have a problem where I have survival data with independent variables, but only cases that ...
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Non-constant treatment periods for A/B test samples
I have 4 A/B testing samples with 4 treatment groups and their 4 respective control groups (n=10000~), with known (but not distinct) treatment periods. The issue is that the treatment periods overlap ...
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Survival analysis - Kaplan Meier curve suddently drops
I'm doing a survival analysis (where i'm a newby by the way) for churn, to try to understand how long an insurance policy stays "alive". My universe are all policies that were created ...
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Causal survival analysis in a large prospective cohort when treating incident events as the exposure
I'm currently working on a large prospective cohort with the basic demographic characteristics and various socioeconomic factors collected at baseline. This cohort was follow up since baseline entry ...
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Do I need to care if an adjustment variable violates the proportional hazards assumption?
I'm assessing the effect of an exposure variable on cancer risk. I am not necessarily trying to build the model that best predicts cancer risk, I am instead trying to best isolate the effect on cancer ...
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Results different using specific years for survival analysis vs zero time starting point
I am using survival analysis to model Treaty ratification, using country-treaty dyads as my primary unit. This means that I have several hundred survival spells covering different time periods (when ...
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Survival analysis sample size considerations
In statistical modelling, it is advised to have at least 10 observations per predictor as a rule of thumb. It is further suggested that in survival analysis this corresponds to number of uncensored ...
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Logistic regression odds vs Survival analysis odds
Why do I get significantly different answers from the logistic regression and survival analyses? How can I fix this code?
Logistic regression:
...
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Cox ph model with icenReg: how to code data without event indicator
I want to analyze my data with the interval-censored cox regression of the icenReg package.
I have 5 time-points (1,2,3,4,5). Events can only happen once. Each person starts with baseline (T1). People ...
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Calculate incidence density/ rate after weighting
How to calculate an incidence density/ rate for weighted data, e.g. using weighting methods such as matching weights, IPW, ...?
-- UPDATE 06.11.2023:
Regard the following paper as an example of ...
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In SAS what is the difference of including treatment variable in model and using the strata statement for treatment variable?
Im building a Cox Regression in SAS. My code looks like this:
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Change Points in Cox Model
I have trained a Cox model on my dataset, which comprises patients entering the study at various time points. I suspect there might be multiple change points indicating shifting effects over time. My ...
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Cox model with two time varying independent variables for two legal reforms
I want to perform a proportional hazards model to study if job contracts were affected and became shorter/dismissal was more likely to happen after two labour reforms took place.
I understand that the ...
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Compare median time between different groups and years
I want to investigate whether there is a difference in median waiting time between different groups in different year. I'll try to explain as best I can.
My data looks like this:
Year
Country
Waiting ...
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Survival Analysis: adjusting the Kaplan-Meier curve and optimal sample size?
Following the tutorial https://www.youtube.com/watch?v=ujIMPpl2Tr0 from Stanford university, if the log-rank test (a popular test of Kaplan-Meier curves) does not contain report a statistically ...
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Churn/ survival prediction - Cox Regression and Recursive Factors
I'm trying to come up with a straightforward subscription churn (survival) model and - in broad strokes - see the most important factors by size.
I know this can be a loaded topic.
It looks like doing ...
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When to use robust SE in weighted survival analysis?
When do you use robust standard errors in a weighted survival analysis?
Generally in a weighted analysis or are there other reasons for this?
The survival package ...
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Measuring the association of a variable with 2 different survival outcomes
I have survival data for a cohort of patients and the outcome is either cardiovascular or non-CV death. I wish to assess whether some variables make it so that the patient is more likely do die from ...
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Intepretation of hazard times delta t
I'm working through J.F. Lawless's 'Statistical Models and Methods for Lifetime Data'. At one point, it's said that:
In particular, $h(t)\Delta t$ is the approximate probability of death in $[t,t+\...
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Difference between mean and Kaplan-Meier estimate of mean
I have a panel dataset with individuals on a waiting list for a specific event. The individual can either: still be on the waiting list, have died on the waiting list, be withdrawn from the waiting ...
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Modelling Incidents in Survival Analysis
I'm working with a mixture cure model in a panel dataset covering multiple products across several years. The time-to-event, defined as the duration between the auction start and product sale, is ...
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Survival Analysis patient recycling
I have a Survival Analysis problem. In this problem the patient participates with a medicine until this cure is rejected due to inefficacy or adverse effects. Of course the patient can continue until ...
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Familial risk study using case control database?
If I have a dataset with cases and controls ( cases being people with colon cancer, control are people without colon cancer), and for each case or control, there are multiple relatives linked to them ...
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Can you use survival analysis on subjects spanning two separate time periods?
I have a dataset where the subjects are job contracts. It spans from the 80s to 2019.
Each contract has a start of contract and end of contract date, unless they were still active in 2019 in which ...
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Time-Dependent Probabilities and Survival Analysis
I am working on a data-driven collision risk model for cars. Using car trajectory data, the model computes the probability of collision at each data timestamp with 2 sub-models:
An overlap model: By ...
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Simulating Time-Dependent Covariate and Applying coxph Function to Estimate Association Parameters
Let $T$ denote a failure time random variable drawn from a proportional hazards model with a time-invariant covariate $X\sim U(2, 7)$ and a time-varying covariate $Z(t) = kt$ for $k > 0$. Assuming ...
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What to consider when calculating the number of subjects required for survival studies?
I was looking for formulas for calculating the number of subjects needed in survival analysis studies.
The formula that comes up most often is the one that takes the following parameters into account.
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Survival analysis using as exposure a case-control study variable
I have doubts about how to correctly analyze the following study, since it is neither a nested case control nor a case-cohort study, but it is a previous matched case-control study in which the "...
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Convergence of estimated Survival Functions
Q1 part A&B I have so far
$$\underset{n\rightarrow\infty} {\lim} \frac{1}{n}\sum_{i=1}^nI(T_i>x)$$
since we are summing an indicator variable we can say it has a Bernoulli distribution with ...
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Interpreting HR when using tt() function
Assuming non-proportional hazards for karno (and based off p 21):
https://cran.r-project.org/web/packages/survival/vignettes/timedep.pdf
How does one interpret the HR for karno?
Is the HR = 0.9590 for ...
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How do I interpret a cox proportional hazard model?
I fitted a cox proportional hazards model in R using the coxph(). My explanatory variables include; gender, residence, antenatal care, tetanus injection, iron tablets, size at birth, place of delivery ...
Sammywills
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Differences among Cox regression, accelerated failure time regression and linear regression when using no censoring data
I would like to investigate whether social economic status (SES) would be associated with the age of onset of mental illness, after controlling some possible confounders, in the cohort of patients ...
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Survival analysis large number of small batches
I have a group of >10,000 right-censored survival cases, composed of a large number of small batches, with 1 to 7 cases in each batch. Failure rate overall is low, with 95% survival at maximum ...
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How do I incorporate a patient's current age into a survival model?
I have a dataset with 10,000 patients, and for each patient, I have the following information:
Biological sex (male/female)
Baseline age (age at the time the patient joined the study)
Age at the ...
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How to aggregate survival type of data to single measure independent of time?
Thank you for the opportunity to ask this question. Since I do not speak fluently (still learning) the language of mathematics, I explain this in plain English. I understand this is too ideal but I ...
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Validity of using non-time variable in survival analysis
I’ve conducted a time-to-event analysis (accelerated failure time) for a research project, looking at the effect of an intervention on the probability of needing a blood transfusion during surgery. ...
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MLE of weibull distribution with survival data
I would like to ask about estimating parameters of Weibull distribution (a, and b)
I am trying to code likelihood of weibull distribution with survival data $(T_i, \Delta_i),$ which I believe is: $(ab)...
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Cox model with ridge term: how to choose value of theta?
The coxph() function in R package "survival" is used to fit the Cox proportional hazards model. This function allows a ridge() term in the formula to penalise selected terms, which requires ...
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Calculate the risk probability with coefficients derived from Cox regression
I am new to survival analysis and I was wondering whether you could, similar to the logistic regression, calculate the intercept and the risk probability with the coefficients you acquire from the cox ...
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What is the predicted "risk score" generated by sjPlot plot_model?
I ran the following time-varying covariate cox model:
...
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Counting process style survival analysis - do I still need to interact with time?
I'm working in Stata with a Cox model and I have a dataset in counting process style (multiple records per subject).
Each observation per subject has covariants that could potentially be different ...
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Transition Probabilities and Weibull Regression
I am currently reading a book on health technology assessment: Decision Modelling for Health Economic Evaluation (Briggs et al).
Given all information below, I'd appreciate if someone could show how ...
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