Statistics Papers
Title
Building a Stronger Instrument in an Observational Study of Perinatal Care for Premature Infants
Document Type
Journal Article
Date of this Version
2010
Publication Source
Journal of the American Statistical Association
Volume
105
Issue
492
Start Page
1285
Last Page
1296
DOI
10.1198/jasa.2010.ap09490
Abstract
An instrument is a random nudge toward acceptance of a treatment that affects outcomes only to the extent that it affects acceptance of the treatment. Nonetheless, in settings in which treatment assignment is mostly deliberate and not random, there may exist some essentially random nudges to accept treatment, so that use of an instrument might extract bits of random treatment assignment from a setting that is otherwise quite biased in its treatment assignments. An instrument is weak if the random nudges barely influence treatment assignment or strong if the nudges are often decisive in influencing treatment assignment. Although ideally an ostensibly random instrument is perfectly random and not biased, it is not possible to be certain of this; thus a typical concern is that even the instrument might be biased to some degree. It is known from theoretical arguments that weak instruments are invariably sensitive to extremely small biases; for this reason, strong instruments are preferred. The strength of an instrument is often taken as a given. It is not. In an evaluation of effects of perinatal care on the mortality of premature infants, we show that it is possible to build a stronger instrument, we show how to do it, and we show that success in this task is critically important. We also develop methods of permutation inference for effect ratios, a key component in an instrumental variable analysis.
Copyright/Permission Statement
This is an Accepted Manuscript of an article published by Taylor & Francis in Journal of the American Statistical Association on 01 Jan 2012, available online: http://wwww.tandfonline.com/10.1198/jasa.2010.ap09490.
Keywords
design sensitivity, effect ratio, instrumental variable, nonbipartite matching, observational study, optimal matching, sensitivity analysis
Recommended Citation
Baiocchi, M., Small, D., Lorch, S. A., & Rosenbaum, P. R. (2010). Building a Stronger Instrument in an Observational Study of Perinatal Care for Premature Infants. Journal of the American Statistical Association, 105 (492), 1285-1296. http://dx.doi.org/10.1198/jasa.2010.ap09490
Date Posted: 27 November 2017