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What is the appropriate statistical test for Presence/Absence over time

You seem to have two groups observed at many different variables (milestones for different tasks, with values in 1...21). So this could be seen as a problem of multivariate regression, or maybe manova....
kjetil b halvorsen's user avatar
0 votes

Designing a model to analyze hospital length of stay

LOS is usually best modeled as a count variable using Poisson or negative binomial regression. Check the distribution of the variable using a histogram. If you would like to include hospital clusters ...
Jack's user avatar
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3 votes
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Is the Distribution of Survival Times always IID?

will the survival function still be monotonic and strictly decreasing? With at most one absorbing event per individual, a survival function $S(t)$ is just the complement of a cumulative probability ...
EdM's user avatar
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Interpreting results of the survival analysis

With over 134000 cases and ~8400 events, even a very small difference of no practical significance can show up as "statistically significant," with very low p-values. Your reported hazard ...
EdM's user avatar
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2 votes

Modelling Time to Events

The hazard $h(t)$ at time $t$ is the rate of change of the survival function $S(t)$ $$h(t) = -\frac{S'(t)}{S(t)}$$ For certain hitting time models, these hazards are not a constant ratio of time. This ...
Sextus Empiricus's user avatar
5 votes
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Modelling Time to Events

Thus it appears that we can now exactly model the time at which a certain event might happen, by treating the underlying probability distribution of this event as a (covariate dependent) Stochastic ...
EdM's user avatar
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1 vote

Can you lag a covariate in a Cox proportional hazards model?

A Cox model bases its estimates on the covariate values that are in place for all those at risk at each event time. The definitions of covariate values thus should represent the values that are most ...
EdM's user avatar
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0 votes

Restricted mean survival time for rare events

Coefficients of semi-parametric (e.g., Cox proportional hazards, PH) or fully parametric (e.g., accelerated failure time, AFT) survival regression models are useful here. In the PH context the ...
EdM's user avatar
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3 votes
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Can Survival Models model the time at which a random variable will first pass a certain point?

As Cliff AB's answer noted, we need to distinguish between survival (a.k.a. time-to-event) data and specific models for that data (like Cox-PH, accelerated failure time, first passage models you ...
Martin Modrák's user avatar
4 votes

Can Survival Models model the time at which a random variable will first pass a certain point?

Cox proportional hazards models the probability/odds that an event is of type A or of type B, given that an event A or B happened. By doing that it avoids the problem of figuring out the probability ...
Sextus Empiricus's user avatar
1 vote
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Cox proportional hazard model ran out of iterations = summary gave significant results with very small p values

This looks like a prime case of overfitting leading to odd results. You have far too few cases; even without the missing data, it's too many variables. With the missing data - well, it's very sparse. ...
Peter Flom's user avatar
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7 votes

Can Survival Models model the time at which a random variable will first pass a certain point?

First threshold is not an alternative to survival analysis, but rather a distribution choice for survival analysis. To quote the abstract from the linked paper: The threshold regression methodology ...
Cliff AB's user avatar
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3 votes

Can Survival Models model the time at which a random variable will first pass a certain point?

This might be naive, but : Accelerated Failure Time models model analyse the time to event. By encoding as 0 the individuals of the cohort which did not pass the threshold, and 1 the individuals ...
CaroZ's user avatar
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0 votes

Sampling weights in Cox proportional hazards models

The two will give identical point estimates in this setting. Standard error estimates might be slightly different due to different degree-of-freedom corrections (like the $n$ vs $n-1$ issue in ...
Thomas Lumley's user avatar
2 votes
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How to write a Gompertz model as an accelerated failure time model?

Although you can fit a parametric Gompertz regression model, it won't have a simple interpretation in terms of the proportional survival quantiles that AFT models provide. That type of interpretation ...
EdM's user avatar
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1 vote

Left censored dependent variable in SEM

is there a way in Amos to analyze such a model in which the dependent is non-linear? While AMOS can handle censored data (see the ...
Preston Botter's user avatar
1 vote

Suppose I am using KM curve to estimate S(t) parametrically (say assuming it follows lognormal)

The curve will be similar, but most months are a bit longer than 4 weeks, so a fit to t[weeks] will not be exactly the same as 0.25*t[months].
Niklas's user avatar
  • 21
3 votes

Is the Kaplan-Meier estimator appropriate when I have observed only one event?

Just to give a very intuitive perspective: in survival analysis the main unit we care about is the "case", as in the subject who experienced the event in study. Contrarily, when we analyze a ...
jmarkov's user avatar
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4 votes
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Is the Kaplan-Meier estimator appropriate when I have observed only one event?

Maybe "appropriate" but "not very useful." The Kaplan-Meier estimator will show a single drop in the survival curve, at the time of the death event. It will only use information ...
EdM's user avatar
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3 votes
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Survival analysis with missing values for some variables

I think a much better solution is to do multiple imputation. This can be implemented in major software packages. I know R and SAS have extensive capabilities here, and I am pretty sure that other ...
Peter Flom's user avatar
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5 votes

Which variables I should include in the multivariable Cox regression model?

Categorization of continuous covariates is almost never a good idea. The reason is that you are basically throwing away valuable information, which may lead to a loss of statistical power or even ...
Denzo's user avatar
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5 votes
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How to calculate sample size from hazard ratio?

The key reference here is Schoenfeld (1983), who gives the following formula: $$ \frac{(z_{1-\beta}+z_{1-\alpha})^2}{pA\times pB\times\text{log}(\text{HR})^2} $$ This will give you the number of ...
PBulls's user avatar
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5 votes
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Exponential likelihood

There are many options. Some models with available implementations include: multistate models that allow your rate to depend on the state exponential regression models that allow rates to be ...
Thomas Lumley's user avatar
0 votes

how to deal with exposure measurements long before inclusion using cox model

The PhD I am currently working on focuses specifically on this topic so I think I can give you some good tips. Important point: If your outcome is incidence of cardiovascular diseases, then you do ...
jmarkov's user avatar
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2 votes

Derivation of Closed-Form Gompertz-Makeham Life Expectancy

As happened previously, formulating the question and thinking about it helped to find a solution. Here it is: We start from $$\begin{aligned} e(x) &= \int_x^\infty \exp \left[ -ct - \frac{a}{b} (e^...
Martin Georg Haas's user avatar
6 votes
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Is the effect in a Cox proportional hazard collapsible if the covariates are normally distributed and the baseline hazard is constant?

Maths is hard, so I would always start by checking this sort of thing with simulation ...
Thomas Lumley's user avatar
3 votes
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Two-component Cox proportional hazards model Simulation

While I'm interested in seeing what @EdM has to say, this question seems like a straightforward yes, your approach works. I have coded up a version, limited to one group, in R and both the ...
Lukas Lohse's user avatar
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0 votes

how to deal with exposure measurements long before inclusion using cox model

At each event time, the Cox mode uses the current values of the covariates for all those at risk of the event. That said, it's possible to devise new covariates that represent some aspect of the ...
EdM's user avatar
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