Suppose I have survival data with more than one row per subject, because I have splitted the follow-up time of each subject into pieces (maybe because I have one or more time-varying variables or maybe just because I want to fit a Poisson model with a non-constant hazard over time).
Do I have to use the robust variance / covariance estimator (that is implemented for example in Stata with the option
vce(cluster clustvar)) to take into account that I have more than one observation per subject (i.e. they're not independent)?
Edit (15 March 2012):
Lambert and Royston in their book carry out this analysis: they split each subject's follow-up on one time-scale (let's say attained age)[*] and fit a Poisson regression including attained age as the dependent variable (modelled for example using splines) plus the offset, so that it's possible to model the incidence of some disease according to attained age.
They do not use the robust variance/covariance estimator, but I haven't found in the text any explanation of why the single rows (or episodes) can be considered independent.
The question: Can someone explain to me why the single rows (or episodes) can be considered as independent?
[*]To clarify what's been done, let's take for example subject number
1001. He/she enters the study at
80.00219 years of age and develop the disease at
85.037236 years (
_d==1). This is what happens to the record of this subject after splitting it up.
(The offset variable is defined as
id _d _t _t0 1001 0 81 80.00219 1001 0 82 81 1001 0 83 82 1001 0 84 83 1001 0 85 84 1001 1 85.037236 85