Online Program Home
  My Program

All Times EDT

Abstract Details

Activity Number: 57 - Nonparametric Modeling I
Type: Contributed
Date/Time: Sunday, August 8, 2021 : 3:30 PM to 5:20 PM
Sponsor: Section on Nonparametric Statistics
Abstract #318560
Title: Constructing Confidence Intervals from Randomization Tests
Author(s): Yanying Wang*
Companies: Bristol Myers Squibb
Keywords: randomization tests; interval estimation; Robbins-Monro algorithm; bisection method; Monte Carlo re-randomization test

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 presentation 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.

Authors who are presenting talks have a * after their name.

Back to the full JSM 2021 program