[R] Looking for Post-hoc tests (a la TukeyHSD) or interaction-level independent contrasts for survival analysis.

Huot, Matthieu Matthieu.Huot at dfo-mpo.gc.ca
Tue Sep 15 21:09:19 CEST 2015

Hi Tom

I know the post is over 7-8 years old but I am having the same question. How to do a post-hoc test like TukeyHSD on coxph type output.

Have you received any info in this matter?

Looking for Post-hoc tests (a la TukeyHSD) or interaction-level independent contrasts for survival analysis.
Thomas Oliver toliver at stanford.edu <mailto:r-help%40r-project.org?Subject=Re%3A%20%5BR%5D%20Looking%20for%20Post-hoc%20tests%20%28a%20la%20TukeyHSD%29%20or%20interaction-level%0A%20independent%20contrasts%20for%20survival%20analysis.&In-Reply-To=%3C6.>
Tue Apr 29 23:12:33 CEST 2008

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Hello all R-helpers,

    I've performed an experiment to test for differential effects of

elevated temperatures on three different groups of corals.  I'm

currently performing a cox proportional hazards regression with

censoring on the survivorship (days to mortality) of each individual

in the experiment with two factors: Temperature Treatment (2 levels:

ambient and elevated) and experimental group (3 levels: say 1,2,3).

In my experiment, all three groups survived equally well in the

ambient control treatment, but  two of three of the groups succumbed

to heat stress in the elevated temperature treatment.  I can see that

the third group had a small degree of mortality, but it appears to be

significantly less than the other two and may be not significantly

different from the ambient controls.

I would like to ask three questions:  1) Is group 3 different from

controls? 2) Is group 3 different from group 1 and/or group 2 in the

elevated treatment? and 3) are groups 1 and 2 different from each

other in the elevated treatment?

   Because I'm testing for differential effects among the elevated

temperature treatment group, and "I've seen the data" by now,  the

analysis would be easiest for me if I performed a responsible

multiple comparisons test like TukeyHSD to see how each of the six

Treatment:Group subgroups compared to each other.  TukeyHSD does not

appear to be defined for outputs from the function coxph -- (see

survival library).

cph <- coxph(Surv(DayToMort, Censor) ~ Treatment*Group, data=subb)

--> Does anyone know of an implementation of TukeyHSD that would

work, or of another post-hoc multiple comparison test?

I believe that another responsible tack would be to clearly define

the contrasts I'd like to make within the interaction term. However

this has yet to work as fully as I'd like it.

  I've successfully set the contrasts matrix for the three-level

factor "Group" following Crawley's The R Book.




By setting these contrasts and then looking at the interaction terms

in the coxph model, this allows me to compare groups _within_ each

separate treatment, and confirms both that #2) that groups 1 and 2

are not sig. different in the elevated treatment, and #3) the group3

corals survived significantly better than the other groups in the

elevated treatment. BUT it does not allow me to say if the group3

survival is or is not different from its own control.(#1 above).

To make this comparison, I've tried setting the contrast matrix for

the Treatment:Group interaction term, with no success.  Whenever I

attempt to do so, I run the code below:


#Build a matrix

rownames(cmat)<-rownames(contrasts(subb$Treatment:subb$Group))  #give

cmat the correct rownames


#give cmat the correct colnames

contrasts(subb$Treatment:subb$Group)<-cmat           #try to assign cmat

and I get this error message:

Error in contrasts(subb$Treatment:subb$Group, ) <- cmat :

   could not find function ":<-"

Alternatively I could run:




             [,1] [,2] [,3] [,4] [,5]

Con:1   1    0    0    0    0

Con:2    0    1    0    0    0

Con:3    0    0    1    0    0

Exp:1    0    0    0    1    0

Exp:2    0    0    0    0    1

Exp:3   -1   -1   -1   -1   -1

But even that doesn't appear to affect the output of :

cph <- coxph(Surv(DayToMort, Censor) ~ Treatment*Group, data=subb)

--> Is what I'm trying to do statistically invalid and R is trying to

quietly save me from statistical destruction, or it is just being a

pain?  Is there a way around it?

--> Any other suggestions?

Many Thanks in Advance,

Tom Oliver

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