
Dear List,
I have a theoretical question related to epidemiological data analysis: 
If the treatment status (tx = 0,1) changes over time for the patients in a non-randomized cohort, is there a way to estimate the treatment effect? 
(i.e., after joining the study, some patients may have to wait for a period of time before receiving the treatment, i.e., the situation of patient with id == 2 for the following data) 
Data format is like the stanford heart transplant data (Therneau et al 2000, p69), but the patients were not randomized in selection and the covariate balance is not achieved:
id	time	censor	tx	x1	x21	(0,10]	1	0	x11	x122	(0,8 ]	0	0	x21	x222	(9,19]	1	1	x21	x223	(0,13]	0	1	x31	x32
Is counting process form of a Cox model (coxph with start, stop, censoring status ~ tx + x1 + x2 covariates) sufficient?
Is it possible to implement the propensity score methodology (Rosenbaum et al, 1983) in such situations?
Any ideas/suggestions would be higly appreciated.
Thanks,
Ehsan 		 	   		  
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