Biomedical subjects

W J Conover

Publications and source records attributed to W J Conover.

3 recordsLinked to original sources

PubMed1988

Locally most powerful tests for detecting treatment effects when only a subset of patients can be expected to "respond" to treatment.

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.

Aspartate Aminotransferases↗

PubMed1982

Analysis of covariance using the rank transformation.

The rank transformation refers to the replacement of data by their ranks, with a subsequent analysis using the usual normal theory procedure, but calculated on the ranks rather than on the data. Rank transformation procedures have previously been shown by the authors to have properties of robustness and power in both regression and analysis of variance. It seems natural to consider the use of the rank transformation in analysis of covariance, which is a combination of regression and analysis of variance. In this paper the rank transformation approach to analysis of covariance is presented and examined. Comparisons are made with the rank transformation procedure given by Quade (1967, Journal of the American Statistical Association 62, 1187-1200), and some 'standard' data sets are used to compare the results of these two procedures. A Monte Carlo simulation study examines the behavior of these methods under the null hypothesis and under alternative hypotheses, with both normal and nonnormal distributions. All results are compared with the usual analysis of covariance procedure on the basis of robustness and power.

Analysis of Variance↗

PubMed1979

Statistical analysis of method comparison data. Testing normality.

A Lilliefors test of normality has been applied to data from precision and accuracy studies. Most data sets tested as non-normal. Simulation studies showed that the test is extremely sensitive to the rounded, narrowly distributed data that are typical of method performance studies in clinical chemistry. The Lilliefors test can be modified to be applicable to rounded data so that it gives fewer indications of non-normality. The authors conclude that the selection of a test of normality requires careful study of the properties of the test. Otherwise, the subsequent choice between parametric and nonparametric statistics may not be meaningful.

Chemistry, Clinical↗