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Exact confidence intervals for the average causal effect on a binary outcome
Author(s) -
Li Xinran,
Ding Peng
Publication year - 2016
Publication title -
statistics in medicine
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 1.996
H-Index - 183
eISSN - 1097-0258
pISSN - 0277-6715
DOI - 10.1002/sim.6764
Subject(s) - confidence interval , outcome (game theory) , statistics , binary number , robust confidence intervals , mathematics , econometrics , arithmetic , mathematical economics
Based on the physical randomization of completely randomized experiments, in a recent article in Statistics in Medicine , Rigdon and Hudgens propose two approaches to obtaining exact confidence intervals for the average causal effect on a binary outcome. They construct the first confidence interval by combining, with the Bonferroni adjustment, the prediction sets for treatment effects among treatment and control groups, and the second one by inverting a series of randomization tests. With sample size n , their second approach requires performing O ( n 4 )randomization tests. We demonstrate that the physical randomization also justifies other ways to constructing exact confidence intervals that are more computationally efficient. By exploiting recent advances in hypergeometric confidence intervals and the stochastic order information of randomization tests, we propose approaches that either do not need to invoke Monte Carlo or require performing at most O ( n 2 )randomization tests. We provide technical details and R code in the Supporting Information. Copyright © 2016 John Wiley & Sons, Ltd.

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