Instrumental variables: to strengthen or not to strengthen?

Author:

Heng Siyu1,Zhang Bo2ORCID,Han Xu3,Lorch Scott A4,Small Dylan S5

Affiliation:

1. Department of Biostatistics, New York University , New York, NY , USA

2. Vaccine and Infectious Disease Division, Fred Hutchinson Cancer Center , Seattle, WA , USA

3. Department of Statistics, Operations, and Data Science, Temple University , Philadelphia, PA , USA

4. Department of Pediatrics, The Children’s Hospital of Philadelphia , Philadelphia, PA , USA

5. Department of Statistics and Data Science, University of Pennsylvania , Philadelphia, PA 19104 , USA

Abstract

Abstract Instrumental variables (IVs) are extensively used to handle unmeasured confounding. However, weak IVs may cause problems. Many matched studies have considered strengthening an IV through discarding some of the sample. It is widely accepted that strengthening an IV tends to increase the power of non-parametric tests and sensitivity analyses. We re-evaluate this conventional wisdom and offer new insights. First, we evaluate the trade-off between IV strength and sample size assuming a valid IV and exhibit conditions under which strengthening an IV increases power. Second, we derive a criterion for checking the validity of a sensitivity analysis model with a continuous dose and show that the widely used Γ sensitivity analysis model, which was used to argue that strengthening an IV increases the power of sensitivity analyses in large samples, does not work for continuous IVs. Third, we quantify the bias of the Wald estimator with a possibly invalid IV and leverage it to develop a valid sensitivity analysis framework and show that strengthening an IV may or may not increase the power of sensitivity analyses. We use our framework to study the effect on premature babies of being delivered in a high technology/high volume neonatal intensive care unit.

Funder

New York University Research Catalyst Prize

New York University School of Global Public Health

National Institute of Health

Publisher

Oxford University Press (OUP)

Subject

Statistics, Probability and Uncertainty,Economics and Econometrics,Social Sciences (miscellaneous),Statistics and Probability

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4. Building a stronger instrument in an observational study of perinatal care for premature infants;Baiocchi;Journal of the American Statistical Association,2010

5. Near/far matching: A study design approach to instrumental variables;Baiocchi;Health Services and Outcomes Research Methodology,2012

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