Biomedical Engineering Reference
In-Depth Information
13.3.6
Multiple Imputation Approaches :::::::::::::::::::::::: 360
13.3.7
Other Closely Related Methods ::::::::::::::::::::::::: 361
13.4
Software: \Interval" R Package ::::::::::::::::::::::::::::::::::: 362
13.4.1
Using the \Interval" R Package :::::::::::::::::::::::::: 362
13.4.2
Validation of the \Interval" Package ::::::::::::::::::::: 365
13.5
Simulation with Regular Assessment :::::::::::::::::::::::::::: 365
13.6
Recommendations :::::::::::::::::::::::::::::::::::::::::::::::: 370
Bibliography :::::::::::::::::::::::::::::::::::::::::::::::::::::: 371
13.1
Introduction
In this chapter we consider two-sample or k-sample rank tests for interval-
censored responses. As with right-censored responses, the most common type
of test is a logrank test or some weighted version of the logrank test such as
a generalization of the Wilcoxon rank sum test for censoring. We focus on
the tests available in the interval R package (Fay and Shaw, 2010) and the
tests proposed in Freidlin et al. (2007). We additionally show that the tests
of Zhao and Sun (2004) and Sun et al. (2005) calculated in the SAS macros
described in So et al. (2010) are closely related to tests available in theinterval
R package. We focus on practical aspects of the analysis.
Within each section, we give an overview of the main ideas without much
mathematical notation. Applied researchers may focus on these overviews, the
application section, and the recommendation section.
13.2
Description
of
Interval-Censored
Data
and
As-
sumptions for Testing
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