Volume 39, Issue 21 pp. 2843-2854
TUTORIAL IN BIOSTATISTICS

Randomization-based interval estimation in randomized clinical trials

Yanying Wang

Yanying Wang

Department of Statistics, George Mason University, Fairfax, Virginia, USA

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William F. Rosenberger

Corresponding Author

William F. Rosenberger

Department of Statistics, George Mason University, Fairfax, Virginia, USA

Correspondence William F. Rosenberger, Department of Statistics, George Mason University, 4400 University Drive MS 4A7, Fairfax, VA 22030, USA.

Email: [email protected]

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First published: 03 June 2020
Citations: 15

Abstract

Randomization-based interval estimation takes into account the particular randomization procedure in the analysis and preserves the confidence level even in the presence of heterogeneity. It is distinguished from population-based confidence intervals with respect to three aspects: definition, computation, and interpretation. The article contributes to the discussion of how to construct a confidence interval for a treatment difference from randomization tests when analyzing data from randomized clinical trials. The discussion covers (i) the definition of a confidence interval for a treatment difference in randomization-based inference, (ii) computational algorithms for efficiently approximating the endpoints of an interval, and (iii) evaluation of statistical properties (ie, coverage probability and interval length) of randomization-based and population-based confidence intervals under a selected set of randomization procedures when assuming heterogeneity in patient outcomes. The method is illustrated with a case study.

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