My original message was
> I'm examining data from a quite complex designed experiment, where 4%
> of the response observations are missing. I perfectly know that the
> missing mechanism depends on the values of covariates (all available)
> and results in very large values of the response being missing. The
> full records are therefore biased with respect to the original design
> whose effects are not comparable anymore. For this reason I'm thinking
> of re-constructing the missing response data, though standard multiple
> imputations (as those available in SAS proc MI) do not apply in this
> case, due to the informativeness of the missing mechanism.
> Unfortunatly my bibliography about missing data in experiments stops
> at chapter 2 of Little and Rubin's 1987 (!) book. Does anybody have
> any idea?
I wish to thank D.M. Dufe, S. Witte, H.T. Zhu, T. Dumortier, B.F. Egan
and F. Pesarin who referenced:
1) Geert Molenberghs' home page (quite valuable slides available for
download);
2) Literature about censoring in survival analysis;
3) JRSSB, Biometrika and JASA (!);
4) James Robins' home page (still to check);
5) Winbugs for Bayesian analysis;
6) Use of permutation tests and freely available software NPC test 2.0.
--
Alessio Pollice
Dipartimento di Scienze Statistiche
Università delgi Studi di Bari
Via C. Rosalba 53
70124 Bari, ITALY
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