I am estimating a Gompertz proportional hazards model in R using the package "flexsurvreg", but I'm having a hard time understanding the output of this function. My dataset is collected from skeletal remains, so my event is estimated age at death and my explanatory variable in community dwelling- either in monastic or urban communities during the medieval period in the UK. In the past, I've used the Cox proportional hazards model from the "survival" package, but the standard in my field is a Gompertz model. The problem is that the output of flexsurvreg is different than the coxph function so I don't know how to interpret the results.
Here's the output from flexsurvreg:
Call:
flexsurvreg(formula = Surv(age) ~ monastery, data = dat, dist = "gompertz")
Estimates:
data mean est L95% U95% se exp(est) L95%
shape NA 0.072851 0.069204 0.076498 0.001861 NA NA
rate NA 0.002167 0.001817 0.002584 0.000194 NA NA
monasteryUrban 0.322643 0.338239 0.205303 0.471174 0.067826 1.402475 1.227897
U95%
shape NA
rate NA
monasteryUrban 1.601874
N = 1029, Events: 1029, Censored: 0
Total time at risk: 42770.5
Log-likelihood = -4113.269, df = 3
AIC = 8232.537
And here's the output from coxph on the same data
Call:
coxph(formula = Surv(age) ~ monastery, data = dat)
n= 1029, number of events= 1029
coef exp(coef) se(coef) z Pr(>|z|)
monasteryUrban 0.21742 1.24286 0.06753 3.22 0.00128 **
---
Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1
exp(coef) exp(-coef) lower .95 upper .95
monasteryUrban 1.243 0.8046 1.089 1.419
Concordance= 0.531 (se = 0.011 )
Likelihood ratio test= 10.11 on 1 df, p=0.001
Wald test = 10.37 on 1 df, p=0.001
Score (logrank) test = 10.41 on 1 df, p=0.001
For the flexsurvreg output, how do I interpret the overall performance of the model- is log-likelihood the same thing as a likelihood ratio test? How can I tell whether the hazards between my two groups (monastic vs. urban dwellers) are significantly different?