Global tests for combination drug studies in factorial trials.
To test the hypothesis that there are some studied dose combinations more effective in treating a disease than their respective component doses of two drugs, Hung, Chi and Lipicky proposed two alpha-level tests for normally distributed data. This paper extends the utilities of these tests to the outcome variable that has variance as a function of its mean, such as with a binomially distributed outcome, and to incomplete factorial design settings where some cells are not studied. I explore the impacts of excluding cells from study on the power performances of these tests.
Hung
H M
HM
Statistical Evaluation and Research Branch, Food and Drug Administration, Rockville, Maryland 20852, USA.
D000465:Algorithms; D000959:Antihypertensive Agents / Q000627:therapeutic use; D016010:Binomial Distribution; D001794:Blood Pressure / Q000187:drug effects; D004359:Drug Therapy, Combination; D005163:Factor Analysis, Statistical; D006801:Humans; D006973:Hypertension / Q000188:drug therapy; D015233:Models, Statistical; D009010:Monte Carlo Method; D016032:Randomized Controlled Trials as Topic / Q000706:statistics & numerical data*; D016896:Treatment Outcome
D000959:Antihypertensive Agents