  In pharmacogenomic studies, biomedical researchers commonly analyze the
association between genotype and biological response by using the
Kruskal--Wallis test or one-way analysis of variance (ANOVA) after logarithmic
transformation of the obtained data. However, because these methods detect
unexpected biological response patterns, the power for detecting the expected
pattern is reduced. Previously, we proposed a combination of the maximum
contrast method and the permuted modified maximum contrast method for unequal
sample sizes in pharmacogenomic studies. However, we noted that the
distribution of the permuted modified maximum contrast statistic depends on a
nuisance parameter $\sigma^2$, which is the population variance. In this paper,
we propose a modified maximum contrast method with a statistic that does not
depend on the nuisance parameter. Furthermore, we compare the performance of
these methods via simulation studies. The simulation results showed that the
modified maximum contrast method gave the lowest false-positive rate;
therefore, this method is powerful for detecting the true response patterns in
some conditions. Further, it is faster and more accurate than the permuted
modified maximum contrast method. On the basis of these results, we suggest a
rule of thumb to select the appropriate method in a given situation.
