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Is differential follow up periods in survival analysis a problem?

After the last post-surgery observation time for the group with the newer surgery method, no such patient will be in the risk set for events among those who ...
EdM's user avatar
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1 vote

Hazard Ratio from two cox models

It's seldom wise to subset data and run separate models. I think that your separate modelA and modelB might be misleading you. ...
EdM's user avatar
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2 votes

Considering 96 observation for estimating the intercept (rule of thumb)

As I recall, that recommendation for 96 is to get a precise estimate of the intercept (log-odds at reference conditions) in a logistic regression model. If all you care about is how changes in ...
EdM's user avatar
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0 votes

Optimal sequence analyses and hypothesis testing

From your updated question, I understand that you want to test whether the sequence pattern (cluster membership) followed by a dyad can predict the number of aggressions within the dyad. Cases are ...
Gilbert's user avatar
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2 votes

Limited outcome events. Choice of method

The usual rule of thumb is that you need about 15 events per coefficient that you are estimating to avoid overfitting a survival model. You have 16 events, on that basis barely enough to evaluate the ...
EdM's user avatar
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-1 votes

Perfusion Analysis Counts as Survival Analysis?

This is pharmacokinetics. It certainly is not survival analysis, which evaluates the times to events (even if the event isn't always death). Even if the concentration versus time plot of a drug might ...
EdM's user avatar
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1 vote

Limited outcome events. Choice of method

I am not sure this answers your question, but just for "fun", I plugged your data into a simple 2x2 contingency matrix. Fisher-exact says $p=0.000001$. Pearson $\chi^2$ says $p=0.00000001$. ...
jginestet's user avatar
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0 votes

Defining clinical follow-up: Fixed Period vs. Maximum Duration

It depends on what you are trying to prove. If you are trying to test that complication rates have decreased over the years (or at a minimnum test if they have changed over time), then you need to ...
jginestet's user avatar
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14 votes
Accepted

How can a hazard function be negative?

The hazard certainly can't become negative, as you rightly point out. The problem is the way that plot is constructed. Here's the associated code, which assumes that you have loaded the ...
EdM's user avatar
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2 votes
Accepted

Defining clinical follow-up: Fixed Period vs. Maximum Duration

You are effectively performing "survival analysis," but your event is complication rather than death. Tools for survival analysis can be used for any defined type of event if you want to ...
EdM's user avatar
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2 votes
Accepted

Forecasting Survival Analysis

A Kaplan-Meier curve displays the survival data available to date, but you need to make assumptions to extrapolate. If you are willing to assume a particular parametric distribution, like the Weibull ...
EdM's user avatar
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0 votes
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Time related categorical variable for cox regression

You think that there are 2 calendar-date cutoffs at which hazards might have taken step changes: at t_x and then later at t_y. ...
EdM's user avatar
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1 vote

Estimate Weibull Shape and Scale function from lifetable data in R

Technically, you might consider your data to be "interval censored": you know the range of times within which individuals dies, but not the actual times. With some care, interval censoring ...
EdM's user avatar
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0 votes

How should stratification factors be accounted for in analysis?

The stratified Cox is nearly always preferred to covariate adjustment. Covariate adjustment assumes that there is a "common" unknown baseline hazard function; but stratified Cox allows such ...
Zack Fisher's user avatar
1 vote

Dropping a level of a categorical variable with small number of subjects on cox regression

In a Cox regression, categories with small N may have "no event" at all, so all (the few) cases in the category are censored. In that case you could conclude that the hazard is ...
BenP's user avatar
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1 vote
Accepted

Dealing with categorical variables on cox regression

Say category 1 is the reference. If $b_2$ is the regression coefficient of category 2 then you can interpret $e^{b_2}$ in general as follows: the hazard of category 2 is $e^{b_2}$ times as large as ...
BenP's user avatar
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1 vote

How to explain Hazard Ratio in layperson's terms

What you really need to explain what hazard means. It goes like that: suppose someone tells you that there is a 50% probability that you are going to die during the next 20 years. This bad news but it ...
Yossi Levy's user avatar
0 votes

Multi State Survival Analysis Transition Between States Triggered By different events

The short answer is "it depends." The medium-length answer: how you model the possibility you describe depends on the assumptions you're willing to make about the data-generating process (...
SMzgr356's user avatar
4 votes

What's the gain from multistate model comparing to transition specific survival models?

Just speaking in general, there are major advantages of doing unified vs. separate analyses in such settings. One of the biggest advantages is that you can estimate unconditional quantities in a ...
Frank Harrell's user avatar
1 vote
Accepted

relative Hazard rate

You have to distinguish between the hazard rate, which isn't modeled directly in Cox proportional hazards (PH) regression, from a hazard ratio, which is. The hazard rate is the instantaneous risk of ...
EdM's user avatar
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1 vote

Surival Prediction - Train/Test data vs Production data

It isn't quite correct to say "these right-censored customers [are] the ones that I'm interested in generating a prediction for." You want to generate predictions for all individuals like ...
EdM's user avatar
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0 votes

Discrepancy between ggsurvplot predicted survival curves and raw data

The power of survival analysis comes from the number of events, not the number of total cases. The usual rule of thumb, to avoid overfitting, is to estimate no more than 1 coefficient per 15 or so ...
EdM's user avatar
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1 vote

Regression spline for time to allow for slope changes

The study involved pairs of clinics, with an initial "baseline" observation phase before interventions. Then one member of the pair had an intervention and the other served as control during ...
EdM's user avatar
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3 votes

Interpretation of hazard ratios - impact on time to event?

If a variable in a model has an HR <1, that means that a one-unit increase in thar variable decreases the likelihood of that event happening at any given time. What that means in terms of the event ...
Graham Wright's user avatar
3 votes

Interpretation of hazard ratios - impact on time to event?

The HR measures the relationship of the covariate to the instantaneous hazard of the event. One trouble with equating this to "time to event" is that the events are typically censored, and ...
Peter Flom's user avatar
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0 votes

Cox Regression: handling immediate drop-outs

The number of patients who are censored is irrelevant to the analysis. That is to say, the number of clinical events truly drives the comparisons, whether death or disease progression or clinical ...
AdamO's user avatar
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2 votes
Accepted

Survival analysis - any benefit to splitting on failure time instead of covariate change for creating time-varying covariates?

That sort of splitting helps if you want to define hazard ratios as explicit functions of time (ie, interactions between a covariate and a function of time). For example, if you want $\text{trt}\times ...
Thomas Lumley's user avatar
0 votes
Accepted

Correct interpretation of survival curves that eventually meet

... the HR suggests the exposure is protective for the outcome for only the first 5 years or so That seems like a reasonable interpretation The survival curves seem to come together after about 17 ...
EdM's user avatar
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5 votes

Interpretation of R output for stratified cox-ph model

When you use strata(sex), coxph should estimate a separate hazard for males and females. There is no need to include ...
Demetri Pananos's user avatar
1 vote
Accepted

Validity of disease duration variable in survival analysis

It seems that there is no difference between "disease duration" and the time between diagnosis and relapse: disease duration is measured from the start of observation (i.e. at the point of ...
EdM's user avatar
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