Exact confidence intervals for the average causal effect on a binary outcome

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  • Xinran Li
    Department of Statistics Harvard University Cambrdige 02138 MA U.S.A.
  • Peng Ding
    Department of Statistics University of California Berkeley 94720‐3860 CA U.S.A.

説明

<jats:p>Based on the physical randomization of completely randomized experiments, in a recent article in <jats:italic>Statistics in Medicine</jats:italic>, 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 <jats:italic>n</jats:italic>, their second approach requires performing <jats:italic>O</jats:italic>(<jats:italic>n</jats:italic><jats:sup>4</jats:sup>)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 <jats:italic>O</jats:italic>(<jats:italic>n</jats:italic><jats:sup>2</jats:sup>)randomization tests. We provide technical details and R code in the <jats:ext-link xmlns:xlink="http://www.w3.org/1999/xlink" xlink:href="#sim6764-supinf-0001">Supporting Information</jats:ext-link>. Copyright © 2016 John Wiley & Sons, Ltd.</jats:p>

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