B is the specification for time-varying covariates. Otherwise, your model
will think that each row is one independent observation that either had an
event or was censored at "time" or "total_time."
HTH,
Daniel
javier palacios wrote:>
> Dear R-community,
>
> which of the following two formats is correct? Are both correct?
>
> Please, consider this example:
>
> data table:
>
> Data
> S sta time TDC1 total_time
> A 1 0 1 48.50 1
> B 0 0 1 65.96 2
> B 1 1 2 65.08 2
> C 0 0 1 0.00 2
> C 1 1 2 0.00 2
> D 0 0 1 72.74 2
> D 1 1 2 72.52 2
> E 0 0 1 61.84 2
> E 0 1 2 60.56 2
> F 0 0 1 35.04 4
> F 0 1 2 36.97 4
> F 0 2 3 37.92 4
> F 1 3 4 39.01 4
>
> time - time to event
> sta - starting time
> TDC - time dependent covariates
> total_time - total time at risk
>
> option A
>
> coxph(Surv(time,S) ~ time_dependent_covariates,
> data=data.frame(Data))
>
> option B
>
> coxph(Surv(sta,time,S) ~ time_dependent_covariates,
> data=data.frame(Data))
>
> option C
>
> coxph(Surv(total_time,S) ~ time_dependent_covariates,
> data=data.frame(Data))
>
>
> How can time at risk be visualized in the coxph output?
>
> Best regards,
>
> Javier
>
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