Logrank test for trend (proportional hazards)

Question

How one can create a logrank test for trend and does it differ from normal logrank test? Any suggestions or literature? Maybe some R examples and functions?

To add to that it is important to state what type of variable you wish to associate with time-to-event. If it is an unordered categorical variable then the logrank test as it was originally proposed is a decent choice. There is no reason not to use its generalization the Cox PH model though. And don't fall for the commonly used "tests for trend" in which a continuous variable is split into quantile groups and the quantile group integers are treated as linear in the log hazard. This is a poorly fitting model if there ever was one. — Frank Harrell, Jun 20 '15 at 18:44

potockan · Accepted Answer · 2015-06-21T12:03:56.157

3

Try comp from survMisc package. It extends the survival package. It counts statistic and p-value for logrank test, as well as for Gehan-Breslow, Tarone-Ware, Peto-Peto and Fleming-Harrington tests and tests for trend (for all of the above mentioned). The example taken from the manual is the following:

data(larynx, package="KMsurv")
s4 <- survfit(Surv(time, delta) ~ stage, data=larynx)
comp(s4)
comp(s4)$tests$trendTests # outputs only the results for trend tests

If you compare the results with

survdiff(Surv(time, delta) ~ stage, data=larynx)

you get the same result for 'traditional' logrank test (not the trend test).

edited Jun 21 '15 at 12:03

answered Jun 20 '15 at 17:24

potockan

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please edit yout question with a link to a vignette – Marcin Kosiński Jun 21 '15 at 08:51
I meant the manual, not the vignette. – potockan Jun 21 '15 at 13:34
Have you noticed that the p-values from this package are in relations `p1=1-p2` of standard SAS output? Where p1 is p-value from R package, and p2 is p-value from SAS? – Marcin Kosiński Jun 24 '15 at 21:03
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Yes, I have noticed that. And sometimes the results are slightly different then these obtained from SAS. – potockan Jul 07 '15 at 09:41
That's quite funny ins't it? – Marcin Kosiński Jul 07 '15 at 09:42
It is, maybe sth isn't done correctly and someone should report it to the authors? – potockan Jul 07 '15 at 09:52
Maybe should we first read their documentation about that? – Marcin Kosiński Jul 07 '15 at 09:55
Oh yeah, probably that's cleverer. – potockan Jul 07 '15 at 10:01

score 2 · Answer 2 · answered Aug 20 '14 at 10:19

Your question is not very clear, so not sure if this is what you are looking for. To test the proportional hazards assumption you can use the Grambsch-Therneau test on Schoenfeld residuals of the proportional hazards model. This essentially tests the slope of (scaled) residuals as a function of follow-up time.

Logrank test for trend (proportional hazards)

2 Answers2