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I am trying to run a Cox regression on a sample 2,000,000 row dataset as follows using only R. This is a direct translation of a PHREG in SAS. The sample is representative of the structure of the original dataset.

##
library(survival)

### Replace 100000 by 2,000,000

test <- data.frame(start=runif(100000,1,100), stop=runif(100000,101,300), censor=round(runif(100000,0,1)), testfactor=round(runif(100000,1,11)))

test$testfactorf <- as.factor(test$testfactor)
summ <- coxph(Surv(start,stop,censor) ~ relevel(testfactorf, 2), test)

# summary(summ)
##

user  system elapsed 
9.400   0.090   9.481 

The main challenge is in the compute time for the original dataset (2m rows). As far as I understand, in SAS this could take up to 1 day, ... but at least it finishes.

  • Running the example with only 100,000 observations take only 9 seconds. Thereafter the time increases almost quadratically for every 100,000 increment in the number of observations.

  • I have not found any means to parallelize the operation (e.g., we can leverage a 48-core machine if this was possible)

  • Neither biglm nor any package from Revolution Analytics is available for Cox regression, and so I cannot leverage those.

Is there a means to represent this in terms of a logistic regression (for which there are packages in Revolution) or if there are any other alternatives to this problem? I know that they are fundamentally different, but it's the closest I can assume as a possibility given the circumstances.

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    $\begingroup$ Conditional logistic regression and Cox regression are strictly related. stayconsistent.wordpress.com/2009/01/25/… $\endgroup$ – boscovich Jun 16 '13 at 8:36
  • $\begingroup$ were you able to arrive at an elegant solution to this? I am calculating HRs from $coxph$ for thousands of genes over multiple dataets, and $coxph$ is a bottle neck. At present I am using $apply$ to loop over genes. PS stack is not letting me comment as I am new user. $\endgroup$ – Arshi Arora Mar 12 at 21:03
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I run cox regression on a 7'000'000 observation dataset using R and this is not a problem. Indeed, on bivariate models I get the estimates in 52 seconds. I suggest that it is -as often with R- a problem related to the RAM available. You may need at least 12GB to run the model smoothly.

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I went directly to the hardcore fit function (agreg.fit), which under the hood is called for the computations:

n <- nrow(test)
y <- as.matrix(test[, 1:3])
attr(y, "type") <- "right"
x <- matrix(1:11, n, 11, byrow=TRUE)
colnames(x) <- paste("level", 1:11, sep="")
x <- x[, -2] == test$testfactor
mode(x) = "numeric"

fit2 <- agreg.fit(x, y, strata=NULL, control=coxph.control(), method="efron", 
                  init=rep(0, 10), rownames=1:n)

However, the time elapsed when doubling the sample size gets quadratic as you mentioned. Also decreasing the epsilon in coxph.control does not help.

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