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Randomization‐based interval estimation in randomized clinical trials
Author(s) -
Wang Yanying,
Rosenberger William F.
Publication year - 2020
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.8577
Subject(s) - randomization , confidence interval , interval estimation , statistics , coverage probability , restricted randomization , inference , population , mathematics , interval (graph theory) , credible interval , tolerance interval , randomized controlled trial , computer science , medicine , artificial intelligence , surgery , environmental health , combinatorics
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.