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,
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