Discussion and conclusion
This article introduced and illustrated the LDT criterion for evaluating the performance and efficiency of large-sample UDU
test proposals. The intent was not to introduce a new proposal or to provide a definitive evaluation of any specific large-sample
UDU test, but merely to suggest an objective way of comparing test proposals and of ensuring that large-sample UDU tests will
meet or exceed the quality standard set by the hUSP test.
The LDTs for parametric tests generally depend on the parameters of the assumed population distribution. For illustration,
LDTs were examined for normal and t distributions. In principle, LDTs could be evaluated against other distributions that include skewness (e.g., log normal)
or bimodality (e.g., mixed normal). The authors used 85–115% LC as the range of interest for defining coverage, although other
ranges (e.g., 75–125% LC) may also be of interest. Furthermore, the LDT approach could be extended to quality measures other
than coverage, or to multivariate measures.
It may not always be possible to calculate the LDT of a test directly by algebra and intuitive arguments. However, the LDT
can always be obtained by extrapolation to a large sample size using simulation. This article showed how coverage corresponding
to the 10% and 90% acceptance probabilities varied as a function of the sample size, which can be used to evaluate and compare
the power and efficiency of competing tests.
To ensure that quality standards are maintained at an appropriate level, it is imperative that common quality criteria be
identified and adopted. This article presents the LDT approach to assist in reaching this common goal.
Yanhui Hu* is principal process development engineer, and David LeBlond is senior statistician, both at Abbott Laboratories, D050Z, AMJ23, Abbott Park, IL 60064, tel. 847.938.8885, yanhui.hu@abbott.com
.
*To whom all correspondence should be addressed.
Submitted: Apr. 4, 2011. Accepted: June 13, 2011.
References
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