[R] Clustered data with Design package--bootcov() vs. robcov()

[jjh21]

Another question related to bootcov():

A reviewer is concerned with the fact that bootstrapping the standard errors
does not give the same answers each time. What is a good way to address this
concern? Could I bootstrap, say, 100 times and report the mean standard
error of those 100 estimates? I am already doing 1,000 replications in the
bootstrap, but of course the answer is still slightly different each time.



Frank E Harrell Jr wrote:
> 
> 
> robcov does not use bootstrapping.  It uses the cluster sandwich 
> (Huber-White) variance-covariance estimator for which there are 
> references in the help file (see especially Lin).
> 
> Both robcov and bootcov work best when there is a large number of small 
> clusters.  If the clusters are somewhat large and greatly vary in size, 
> expect to be in trouble and consider a full modeling approach 
> (generalized least squares, mixed models, etc.).
> 
> One advantage of robcov is that you get the same result every time, 
> unlike bootstrapping.  But even in the case of cluster sizes of one, the 
> sandwich estimator can be inefficient (see the Gould paper) or can 
> result in the "right" estimates of the "wrong" quantity (see a paper by 
> Friedman in American Statistician).
> 
> Frank
> 
>> 
>> Thank you.
> 
> 
> -- 
> Frank E Harrell Jr   Professor and Chair           School of Medicine
>                       Department of Biostatistics   Vanderbilt University
> 
> ______________________________________________
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> 

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