Comment: Quantifying the Fraction of Missing Information for Hypothesis Testing in Statistical and Genetic Studies

Zheng, Tian; Lo, Shaw-Hwa

The authors suggest an interesting way to measure
the fraction of missing information in the context of
hypothesis testing. The measure seeks to quantify the
impact of missing observations on the test between two
hypotheses. The amount of impact can be useful information
for applied research. An example is, in genetics,
where multiple tests of the same sort are performed
on different variables with different missing rates, and
follow-up studies may be designed to resolve missing
values in selected variables.
In this discussion, we offer our prospective views on
the use of relative information in a follow-up study.
For studies where the impact of missing observations
varies greatly across different variables and where the
investigators have the flexibility of designing studies
that can have different efforts on variables, an optimal
design may be derived using relative information measures
to improve the cost-effectiveness of the followup.



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Statistical Science

More About This Work

Academic Units
Institute of Mathematical Statistics
Published Here
March 28, 2015