Context

I'm attempting to understand how R's coxph() accepts and handles repeated entries for subjects (or patient/customer if you prefer). Some call this Long format, others call it 'repeated measures'.

See for example the data set that includes the ID column in the Answers section at:

Best packages for Cox models with time varying covariates

Also assume covariates are time-varying throughout and there is exactly one censor (i.e. event) variable, which is binary.

Questions

1. In the above link's answer, if ID is not given as a parameter in the call to coxph() should the results be the same as including cluster(ID) as a parameter in coxph()?

I attempted to search for documentation, but the following doesn't seem to clearly address (1): https://stat.ethz.ch/pipermail/r-help//2013-July/357466.html

2. If the answer to (1) is 'no', then (mathematically) why? It seems cluster() in coxph() seeks correlations between subjects as per subsection 'cluster' on pg. 20 at

https://cran.r-project.org/web/packages/survival/survival.pdf

3. Vague question: how does coxph() with repeated measures compare to R's frailtypack regression methods?

Addenda

The following hints at using cluster(ID):

Is there a repeated measures aware version of the logrank test?

as does:

https://stat.ethz.ch/pipermail/r-help//2013-July/357466.html

GEE approach: add "+ cluster(subject)" to the model statement in coxph Mixed models approach: Add " + (1|subject)" to the model statment in coxme.

Thanks in advance!

Context

I'm attempting to understand how R's coxph() accepts and handles repeated entries for subjects (or patient/customer if you prefer). Some call this Long format, others call it 'repeated measures'.

See for example the data set that includes the ID column in the Answers section at:

Best packages for Cox models with time varying covariates

Also assume covariates are time-varying throughout and there is exactly one censor (i.e. event) variable, which is binary.

Questions

1. In the above link's answer, if ID is not given as a parameter in the call to coxph() should the results be the same as including cluster(ID) as a parameter in coxph()?

I attempted to search for documentation, but the following doesn't seem to clearly address (1): https://stat.ethz.ch/pipermail/r-help//2013-July/357466.html

2. If the answer to (1) is 'no', then (mathematically) why? It seems cluster() in coxph() seeks correlations between subjects as per subsection 'cluster' on pg. 20 at

https://cran.r-project.org/web/packages/survival/survival.pdf

3. Vague question: how does coxph() with repeated measures compare to R's frailtypack regression methods?

Addenda

The following hints at using cluster(ID):

Is there a repeated measures aware version of the logrank test?

as does:

https://stat.ethz.ch/pipermail/r-help//2013-July/357466.html

GEE approach: add "+ cluster(subject)" to the model statement in coxph Mixed models approach: Add " + (1|subject)" to the model statment in coxme.

Thanks in advance!

Context

I'm attempting to understand how R's coxph() accepts and handles repeated entries for subjects (or patient/customer if you prefer). Some call this Long format, others call it 'repeated measures'.

See for example the data set that includes the ID column in the Answers section at:

Best packages for Cox models with time varying covariates

Also assume covariates are time-varying throughout and there is exactly one censor (i.e. event) variable, which is binary.

Questions

1. In the above link's answer, if ID is not given as a parameter in the call to coxph() should the results be the same as including cluster(ID) as a parameter in coxph()?

I attempted to search for documentation, but the following doesn't seem to clearly address (1): https://stat.ethz.ch/pipermail/r-help//2013-July/357466.html

2. If the answer to (1) is 'no', then (mathematically) why? It seems cluster() in coxph() seeks correlations between subjects as per subsection 'cluster' on pg. 20 at

https://cran.r-project.org/web/packages/survival/survival.pdf

3. Vague question: how does coxph() with repeated compare to R's frailtypack regression methods?

Addenda

The following hints at using cluster(ID):

Is there a repeated measures aware version of the logrank test?

as does:

https://stat.ethz.ch/pipermail/r-help//2013-July/357466.html

GEE approach: add "+ cluster(subject)" to the model statement in coxph Mixed models approach: Add " + (1|subject)" to the model statment in coxme.

Thanks in advance!

Context

I'm attempting to understand how R's coxph() accepts and handles repeated entries for subjects (or patient/customer if you prefer). Some call this Long format, others call it 'repeated measures'.

See for example the data set that includes the ID column in the Answers section at:

Best packages for Cox models with time varying covariates

Also assume covariates are time-varying throughout and there is exactly one censor (i.e. event) variable, which is binary.

Questions

1. In the above link's answer, if ID is not given as a parameter in the call to coxph() should the results be the same as including cluster(ID) as a parameter in coxph()?

I attempted to search for documentation, but the following doesn't seem to clearly address (1): https://stat.ethz.ch/pipermail/r-help//2013-July/357466.html

2. If the answer to (1) is 'no', then (mathematically) why? It seems cluster() in coxph() seeks correlations between subjects as per subsection 'cluster' on pg. 20 at

https://cran.r-project.org/web/packages/survival/survival.pdf

3. Vague question: how does coxph() with repeated measures compare to R's frailtypack regression methods?

Addenda

The following hints at using cluster(ID):

Is there a repeated measures aware version of the logrank test?

as does:

https://stat.ethz.ch/pipermail/r-help//2013-July/357466.html

GEE approach: add "+ cluster(subject)" to the model statement in coxph Mixed models approach: Add " + (1|subject)" to the model statment in coxme.

Thanks in advance!