Hello everyone,
I am using coxme to estimate effect of two factors (dose and PS) on the response (collapse), with a random effect called group. And I would like to obtain the p-value of each fixed factor (e.g. dose). I could obtain the p-value either through the z-statistic of model output "fit1", or through anova of fit1 with a model fit2 (without dose).
fit1 <- coxme(Surv(time_to_event, event) ~  dose+ PS + (1|group), data=dat) fit2 <- coxme(Surv(time_to_event, event) ~  PS + (1|group), data=dat)  
somehow, using these two mehods always gives different p-values. 
###################output from fit 1###################Model:  Surv(time_to_event, event) ~ dose+ PS + (1 | group) Fixed coefficients               coef exp(coef)   se(coef)    z      pdose0.04431488  1.045311 0.01672696 2.65 0.0081PS1>1  1.24017717  3.456226 0.78835988 1.57 0.1200

###################output from anova(fit1, fit2)###################Analysis of Deviance Table Cox model: response is  Surv(time_to_event, event) Model 1: ~dose+ PS + (1 | group) Model 2: ~PS + (1 | PatientID)   loglik  Chisq Df P(>|Chi|)  1 -244.41                      2 -247.11 5.3965  1   0.02018 *
Can someone tell me why these two ways give different p-values? which one is the correct one?
Thanks
Chun 		 	   		  
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