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Jacob Fiksel Department of Data & Computational Sciences, Vertex Pharmaceuticals , Boston 02210 , United States Corresponding author: Jacob Fiksel, Department of Data & Computational Sciences, Vertex Pharmaceuticals, 50 Northern Avenue, Boston, 02210, MA, USA (jacob_fiksel@vrtx.com). Search for other works by this author on: Oxford Academic
https://orcid.org/0000-0001-7067-1334 Biometrics, Volume 80, Issue 2, June 2024, ujae051, https://doi.org/10.1093/biomtc/ujae051
Published:
05 June 2024
Article history
Received:
24 April 2023
Revision received:
20 December 2023
Accepted:
21 May 2024
Published:
05 June 2024
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Jacob Fiksel, On exact randomization-based covariate-adjusted confidence intervals, Biometrics, Volume 80, Issue 2, June 2024, ujae051, https://doi.org/10.1093/biomtc/ujae051
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ABSTRACT
Randomization-based inference using the Fisher randomization test allows for the computation of Fisher-exact P-values, making it an attractive option for the analysis of small, randomized experiments with non-normal outcomes. Two common test statistics used to perform Fisher randomization tests are the difference-in-means between the treatment and control groups and the covariate-adjusted version of the difference-in-means using analysis of covariance. Modern computing allows for fast computation of the Fisher-exact P-value, but confidence intervals have typically been obtained by inverting the Fisher randomization test over a range of possible effect sizes. The test inversion procedure is computationally expensive, limiting the usage of randomization-based inference in applied work. A recent paper by Zhu and Liu developed a closed form expression for the randomization-based confidence interval using the difference-in-means statistic. We develop an important extension of Zhu and Liu to obtain a closed form expression for the randomization-based covariate-adjusted confidence interval and give practitioners a sufficiency condition that can be checked using observed data and that guarantees that these confidence intervals have correct coverage. Simulations show that our procedure generates randomization-based covariate-adjusted confidence intervals that are robust to non-normality and that can be calculated in nearly the same time as it takes to calculate the Fisher-exact P-value, thus removing the computational barrier to performing randomization-based inference when adjusting for covariates. We also demonstrate our method on a re-analysis of phase I clinical trial data.
covariate-adjustment, Fisher randomization test, randomization inference, robust inference
© The Author(s) 2024. Published by Oxford University Press on behalf of The International Biometric Society.
This article is published and distributed under the terms of the Oxford University Press, Standard Journals Publication Model (https://academic.oup.com/journals/pages/open_access/funder_policies/chorus/standard_publication_model)
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