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Multivariate one-sided testing in matched observational studies as an adversarial game
Name
1812.07728.pdf
Description
Submitted version
Size
543.92 KB
Format
Adobe PDF
Checksum (MD5)
bb9c4e7512e70eaed176e4090d910433
Author(s)
Cohen, Peter L.
Fogarty, Colin B
Date Issued
December 2020
Journal
Biometrika
Publisher
Oxford University Press (OUP)
Citation
Cohen, Peter L. et al. “Multivariate one-sided testing in matched observational studies as an adversarial game.” Biometrika, 107, 4 (December 2020): 809–825 © 2020 The Author(s)
Version
Original manuscript
Abstract
We present a multivariate one-sided sensitivity analysis for matched observational studies, appropriate when the researcher has specified that a given causal mechanism should manifest itself in effects on multiple outcome variables in a known direction. The test statistic can be thought of as the solution to an adversarial game, where the researcher determines the best linear combination of test statistics to combat nature’s presentation of the worst-case pattern of hidden bias. The corresponding optimization problem is convex, and can be solved efficiently even for reasonably sized observational studies. Asymptotically, the test statistic converges to a chi-bar-squared distribution under the null, a common distribution in order-restricted statistical inference. The test attains the largest possible design sensitivity over a class of coherent test statistics, and facilitates one-sided sensitivity analyses for individual outcome variables while maintaining familywise error control through its incorporation into closed testing procedures.
MIT Department
Massachusetts Institute of Technology. Operations Research Center
Terms of Use
Creative Commons Attribution-Noncommercial-Share Alike
Persistent DSpace Link
DOI of Published Version
10.1093/BIOMET/ASAA024
