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I have the following data for 200 cases/subjects:

  • time to death over a 15 year period, $t$. A datum with a value of 180 months means that the subject did not die over the 15 year period.
  • the frequency of three particular types of event associated with each subject $a_{i}$, $b_{i}$ and $c_{i}$,

Apart from the obvious survival statistics and KM curves, how can I test the association between $t$ and each of $a_{i}$, $b_{i}$ and $c_{i}$. I am hypothesizing that a profile of $t$ will be distinct for each of $a_{i}$, $b_{i}$ and $c_{i}$.

One way I was thinking of testing this hypothesis was to simply do Pearson's correlation between $t$ and each of $a_{i}$, $b_{i}$ and $c_{i}$. I could then visualize this with a scatter plot of $t$ vs each of $a_{i}$, $b_{i}$ and $c_{i}$. Any suggestions on a more sophisticated analysis including variations of KM analysis? Code examples with R appreciated.

user1202664
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  • Do the events of type $a$,$b$,$c$ occur before the start of the follow-up period, or during it? In the latter case I think you would need their exact timing. – Aniko Jun 03 '12 at 16:07
  • @Aniko all events have been counted before the follow-up period. – user1202664 Jun 03 '12 at 16:22

2 Answers2

1

Here is a good way to start in R.

library(survival)
# Assume your survival data is in the vector time
surv_time <- Surv(time, time < 180)

With that you can do the popular Cox Proportional Hazard model

# a, b, and c are the vectors with frequency data.
summary(coxph(surv_time ~ a+b+c))

You will get a complete test of the effect of your 3 variables on the survival. The Cox PH model is

$$ \lambda(t|X) = \lambda(t)\exp(X\beta) $$

where $\lambda(t|X)$ is the hazard function, interpreting as the chance of dying at time $t$, and $X$ is a set of measures on the individual. What the model says is that this hazard function can be anything (not necessary exponentially decreasing), but that increase/decrease of the variables in $X$ will increase/decrase the hazard in a proportional way. Variables significant in the test above will tend to multiply or divide the hazard a lot.

For a very didactic introduction to this (and many other) topic, I recommend Frank Harrell's Regression Modeling Strategies.

gui11aume
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0

Why not treat this as a competing risks model? The three types of event could be looked at as different outcomes. There is literature on this going back to the 1970s. A lot of recent work has been done by Jason Fine of UNC and Robert Gray. You can look for the Fine-Gray model. The cumulative incidence function is the generalization of Kaplan-Meier to competing risks.

Here is a link for a presentation that give background and other information. http://www.stata.com/meeting/australia09/aunz09_gutierrez.pdf

Michael R. Chernick
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