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Re: st: Methods for selection bias
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)?
Privately, Hema replied:
> The "non-randomised data" refers to both the sample and the treatment assignment.
On 4/20/06, Anders Alexandersson <firstname.lastname@example.org> 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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