An Introduction to Practical Sequential Inferences via Single-Arm Binary Response Studies Using the binseqtest R Package
We review sequential designs, including group sequential and two-stage designs, for testing or estimating a single binary parameter. We use this simple case to introduce ideas common to many sequential designs, which in this case can be explained without explicitly using stochastic processes. We foc...
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Published in | The American statistician Vol. 68; no. 4; pp. 230 - 242 |
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Main Authors | , |
Format | Journal Article |
Language | English |
Published |
Alexandria
Taylor & Francis
02.10.2014
AMERICAN STATISTICAL ASSOCIATION American Statistical Association |
Subjects | |
Online Access | Get full text |
ISSN | 0003-1305 1537-2731 |
DOI | 10.1080/00031305.2014.951126 |
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Summary: | We review sequential designs, including group sequential and two-stage designs, for testing or estimating a single binary parameter. We use this simple case to introduce ideas common to many sequential designs, which in this case can be explained without explicitly using stochastic processes. We focus on methods provided by our newly developed R package,
binseqtest
, which exactly bound the Type I error rate of tests and exactly maintain proper coverage of confidence intervals. Within this framework, we review some allowable practical adaptations of the sequential design. We explore issues such as the following: How should the design be modified if no assessment was made at one of the planned sequential stopping times? How should the parameter be estimated if the study needs to be stopped early? What reasons for stopping early are allowed? How should inferences be made when the study is stopped for crossing the boundary, but later information is collected about responses of subjects that had enrolled before the decision to stop but had not responded by that time? Answers to these questions are demonstrated using basic methods that are available in our
binseqtest
R package. Supplementary materials for this article are available online. |
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Bibliography: | SourceType-Scholarly Journals-1 ObjectType-Feature-1 content type line 14 |
ISSN: | 0003-1305 1537-2731 |
DOI: | 10.1080/00031305.2014.951126 |