I every body!
Before to present my problem I apologize that I creates the same topic on the forum stackoverflow, but I think the best place for my question was here.
I'm a PhD student and I'm working on survival data currently, but I don't feel comfortable with survival analysis. I have to correlated problem I think I try to explain it in details
I my case, I'm working with samples in 6 cities which are in 2 treatments conditions (control and chemical stress). I haven't censored data.
The biological question is how genetic diversity (illustrated by the city) can interact with environment modifications (the treatment)?
To answer I created a survival parametric model with weibull distribution (AIC method to evaluate best models).
And I tested first if in control condition only, survival was different.
data <- subset(survival.data, survival.data$treatment=="treated") surv <- with(data, Surv(time,event)) weibull.model <- survreg(surv~city,dist="weibull", data=data)
The ouput model confirm significant differences (p.val<0.05) and this is my first problem. If I want to compare differences between cities, how can I be sure this is a treatment effect or just because a lineage have a longer longevity in mean? My idea was to correct it by the maximum lifespan mean for every city before to use the model, but I didn't see any information for that. I discovered the relative survival but I think it's not my case. I tried it and I transformed my data which are represented as % of total life per city [treated]/[max(untreated)]*100
If I resolved this point next analysis should be differents
If not, I will work withclassical survival data but I want to test the interaction term which represent the genotype x environnement interaction With ANOVA I can have this term and use a post-hoc test as TukeyHSD function in R, but here the output of the model use a parameter of the factors as reference and I can't have the interaction.
Call: coxph(formula = surv~treatment*city,dist="weibull", data=data) coef exp(coef) se(coef) z p treatmentpq 5.178 177.335 0.568 9.11 < 2e-16 cityparis -0.674 0.510 0.866 -0.78 0.43671 citysapporo 2.354 10.524 0.539 4.37 1.2e-05 citysokol 0.277 1.320 0.671 0.41 0.67931 citytokyo 1.893 6.641 0.553 3.42 0.00062 citywatsonville 1.389 4.009 0.572 2.43 0.01522 treatmentpq:cityparis 0.644 1.904 0.904 0.71 0.47630 treatmentpq:citysapporo -1.707 0.181 0.601 -2.84 0.00450 treatmentpq:citysokol -1.085 0.338 0.720 -1.51 0.13191 treatmentpq:citytokyo -2.098 0.123 0.613 -3.42 0.00062 treatmentpq:citywatsonville -0.542 0.581 0.631 -0.86 0.39009 Likelihood ratio test=488.6 on 11 df, p=<2e-16 n= 360, number of events= 249
In forum, publications or google I didn't see this type of analysis... Is it possible with survival data ? Maybe I can modelize it with glm approach but i'm not sure.
Thank you! ps: Sorry for English native speaker if I'm unclear...