Locally Most Powerful Tests for Detecting Treatment Effects When Only a Subset of Patients Can Be Expected to "Respond" to Treatment
- 1 March 1988
- journal article
- research article
- Published by JSTOR in Biometrics
- Vol. 44 (1) , 189-196
- https://doi.org/10.2307/2531906
Abstract
Two two-parameter models are developed for testing the hypothesis of no treatment effect against the alternative that a subset of the treated patients will show an improvement. To keep the range of measurements the same for treated and control patients, Lehmann alternatives are used in both models. Locally most powerful rank tests are developed for each model and each parameter. The asymptotic relative efficiency leads to a test that uses the scores s(i) = [i/(N + 1)]4. Two examples that support the usefulness of this nonparametric test are presented.This publication has 3 references indexed in Scilit:
- Two-Sample Rank Tests for Detecting Changes That Occur in a Small Proportion of the Treated PopulationBiometrics, 1987
- Testing for a Treatment Effect in the Presence of NonrespondersBiometrics, 1986
- Detection of a Treatment Effect When Not All Experimental Subjects Will Respond to TreatmentBiometrics, 1979