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Re: st: Methods for selection bias


From   "Anders Alexandersson" <andersalex@gmail.com>
To   statalist@hsphsun2.harvard.edu
Subject   Re: st: Methods for selection bias
Date   Fri, 21 Apr 2006 09:45:04 -0400

I would consider -permute- for permutation tests based on Monte Carlo
simulations. What assumptions can you make, e.g., do you have
independent samples (i.e. unmatched data)?

Anders Alexandersson
andersalex@gmail.com

Privately, Hema replied:
> The "non-randomised data" refers to both the sample and the treatment assignment.

On 4/20/06, Anders Alexandersson <andersalex@gmail.com> wrote:
> Does "non-randomised data" here refer to the sample and/or to the
> treatment assignment?
>
> On 4/20/06, Hema Mistry <Hema.Mistry@brunel.ac.uk> wrote:
>
> > I was wondering whether you can provide me with some advice or point me in the right
> > direction.  I am trying to find methods which can deal with data that is non-randomised
> > and suffers from selection bias. After searching various databases etc I have come up
> > with the following methods:
> > 1) Regression analyses
> > 2) Propensity score - matching, stratification, regression, classification trees
> > 3) Instrumental variables
> > 4) Sample selection models
> > 5) Two-part models
> > 6) Inverse probability weighting
> >
> > Before I start using these methods in various datasets I was just wondering whether
> > users are aware of any other methods which I have not identified?
> > Can you recommend any good text books or key people that maybe I should contact?

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