SOME ROBUSTNESS ISSUES FOR COMPARING MULTIPLE LOGISTIC REGRESSION SLOPES TO A CONTROL FOR SMALL SAMPLES

摘要

For comparing several logistic regression slopes to that of a control for small sample sizes, Dasgupta et al. (2001) proposed an "asymptotic" small-sample test and a "pivoted" version of that test statistic. Their results show both methods perform well in terms of Type Ⅰ error control and marginal power when the response is related to the explanatory variable via a logistic regression model. This study finds, via Monte Carlo simulations, that when the underlying relationship is probit, complementary log-log, linear, or even non-monotonic, the "asymptotic" and the "pivoted" small-sample methods perform fairly well in terms of Type Ⅰ error control and marginal power. Unlike their large sample competitors, they are generally robust to departures from the logistic regression model.