Treatment–subgroup interaction: An example from a published, phase II clinical trial

Abstract

Phase II trial designs that ignore between-patient heterogeneity and do not allow for treatment–subgroup interactions may produce very large false positive and false negative error rates if efficacy varies by subgroup. Recent discussions of this problem were illustrated with scenarios and computer simulations. In this short communication, we reanalyzed a published phase II trial to highlight the need to consider between-patient heterogeneity and the possibility of treatment–subgroup interaction when designing and analyzing phase II studies. The single-arm trial evaluated amsacrine plus cytosine arabinoside, vincristine, and prednisone (a combination abbreviated as OAP) for adult acute leukemia, when standard treatment was adriamycin plus OAP. We carried out an analysis of covariance (ANCOVA) incorporating data from historical control patients who met eligibility criteria for the trial and received standard treatment at the study center in the years immediately preceding the trial. Patients administered experimental treatment and control patients were classified as having favorable or unfavorable prognosis according to their predicted probability of response to standard treatment. When the prognostic subgroup of patients was ignored, the response rates for experimental and standard treatment appeared similar. However, fitting an ANCOVA model determined that the effects of subgroup, treatment, and their interaction were statistically significant: experimental treatment was superior to standard treatment in patients with unfavorable prognosis and inferior to standard treatment in patients with favorable prognosis. This real-world example of treatment–subgroup interaction highlights the need to employ phase II designs that consider between-patient heterogeneity and the possibility that efficacy differs by subgroup.