Evaluation of the Independence at Home Demonstration: An Examination of the First Four Years
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- Lewandowski, Daniel & Kurowicka, Dorota & Joe, Harry, 2009. "Generating random correlation matrices based on vines and extended onion method," Journal of Multivariate Analysis, Elsevier, vol. 100(9), pages 1989-2001, October.
- Anirban Basu & Willard G. Manning, 2010. "Estimating lifetime or episode‐of‐illness costs under censoring," Health Economics, John Wiley & Sons, Ltd., vol. 19(9), pages 1010-1028, September.
- repec:mpr:mprres:5863 is not listed on IDEAS
- Peter Z. Schochet, 2008. "Statistical Power for Random Assignment Evaluations of Education Programs," Journal of Educational and Behavioral Statistics, , vol. 33(1), pages 62-87, March.
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Keywords
primary care; elderly ; payment incentive; home-based primary care; Medicare;All these keywords.
NEP fields
This paper has been announced in the following NEP Reports:- NEP-IAS-2019-06-17 (Insurance Economics)
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