Hello all,
I am hoping to use survival analysis to examine whether parasite attack
increases nest death in a species of social wasp. I therefore have data for
1. Whether the nest "died" in the 6 week census period
("Status", where
1=died, 0=survived)
2. The day number of death/last recorded day it was observed alive.
3. Whether the nest was attacked by the parasite (0/1 as with 1.)
4. The day number of attack/ last recorded day the nest was observed without
a parasite.
i.e. example dataset:
status death para paraday
0 42 0 42
1 32 0 42
1 25 1 13
0 42 1 25 ...
I've looked over r-help, as well as in Crawley etc., but I have yet to find
a solution. Can anyone point me in the right direction or literature?
Many thanks,
Edd Almond
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sickboyedd <sickboyedd <at> gmail.com> writes:> > > Hello all, > > I am hoping to use survival analysis to examine whether parasite attack > increases nest death in a species of social wasp. I therefore have data for > > 1. Whether the nest "died" in the 6 week census period ("Status", where > 1=died, 0=survived) > 2. The day number of death/last recorded day it was observed alive. > 3. Whether the nest was attacked by the parasite (0/1 as with 1.) > 4. The day number of attack/ last recorded day the nest was observed without > a parasite. > > i.e. example dataset: > > status death para paraday > 0 42 0 42 > 1 32 0 42 > 1 25 1 13 > 0 42 1 25 ... > > I've looked over r-help, as well as in Crawley etc., but I have yet to find > a solution. Can anyone point me in the right direction or literature? >You might want to send this to r-sig-ecology if you need further discussion. In the meantime, the very simplest thing (conditioning on whether the nest was attacked or not) would be library(survival) c1 = coxph(Surv(death,status)~para,data=mydata) (you should definitely read up a bit on survival analysis, Cox proportional hazards, etc.. I think there's a chapter in the book by Scheiner and Gurevitch, geared towards ecologists). Dealing with parasite attack in a more fine-grained way (i.e. assessing mortality before and after parasitism) would be a little trickier, but I wouldn't worry about it until after you've understood the first stage of the analysis. Ben Bolker
sickboyedd <sickboyedd <at> gmail.com> writes:> > > Hello all, > > I am hoping to use survival analysis to examine whether parasite attack > increases nest death in a species of social wasp. I therefore have data for > > 1. Whether the nest "died" in the 6 week census period ("Status", where > 1=died, 0=survived) > 2. The day number of death/last recorded day it was observed alive. > 3. Whether the nest was attacked by the parasite (0/1 as with 1.) > 4. The day number of attack/ last recorded day the nest was observed without > a parasite. > > i.e. example dataset: > > status death para paraday > 0 42 0 42 > 1 32 0 42 > 1 25 1 13 > 0 42 1 25 ... > > I've looked over r-help, as well as in Crawley etc., but I have yet to find > a solution. Can anyone point me in the right direction or literature? >The classic solution in biomedical work is a time-dependent covariate. Create a new data set like this: time1 time2 status parasite 0 42 0 0 0 32 1 0 0 13 0 0 13 25 1 1 ... The key is lines 3 and 4, which show the colony parasite free from day 0 to 13, and with parasite from day 13 to 25. Then one uses a Cox model with fit <- coxph(Surv(time1, time2, status) ~ parasite) summary(fit) It estimates the increase in death rate with parasite versus no parasite. These models were originally developed for treatment regimens that change over time. A given colony (subject) can have as many lines of data as you want, subject to the fact that the time intervals can't overlap (which would correspond to two copies of the same person alive at the same time). The status variable for a multi-line dataset =1 if THIS interval ends with an event. Look at the survival analysis chapter of Venables & Ripley, Modern Applied Statistics with S, for further insight. (or many other books) Terry Therneau