Active-controlled, non-inferiority trials in oncology: arbitrary limits, infeasible sample sizes and uninformative data analysis. is there another way?
In oncology, it may not always be possible to evaluate the efficacy of new medicines in placebo-controlled trials. Furthermore, while some newer, biologically targeted anti-cancer treatments may be expected to deliver therapeutic benefit in terms of better tolerability or improved symptom control, they may not always be expected to provide increased efficacy relative to existing therapies. This naturally leads to the use of active-control, non-inferiority trials to evaluate such treatments. In recent evaluations of anti-cancer treatments, the non-inferiority margin has often been defined in terms of demonstrating that at least 50% of the active control effect has been retained by the new drug using methods such as those described by Rothmann et al., Statistics in Medicine 2003; 22:239-264 and Wang and Hung Controlled Clinical Trials 2003; 24:147-155. However, this approach can lead to prohibitively large clinical trials and results in a tendency to dichotomize trial outcome as either 'success' or 'failure' and thus oversimplifies interpretation. With relatively modest modification, these methods can be used to define a stepwise approach to design and analysis. In the first design step, the trial is sized to show indirectly that the new drug would have beaten placebo; in the second analysis step, the probability that the new drug is superior to placebo is assessed and, if sufficiently high in the third and final step, the relative efficacy of the new drug to control is assessed on a continuum of effect retention via an 'effect retention likelihood plot'. This stepwise approach is likely to provide a more complete assessment of relative efficacy so that the value of new treatments can be better judged.
Carroll
Kevin J
KJ
AstraZeneca Pharmaceuticals, Global Clinical Information Science, Alderley Park, Macclesfield, UK. kevin.carroll2@astrazeneca.com
D002986:Clinical Trials as Topic; D016001:Confidence Intervals; D018849:Controlled Clinical Trials as Topic; D003627:Data Interpretation, Statistical; D004341:Drug Evaluation; D004345:Drug Industry / Q000379:methods*; D023381:Endpoint Determination; D006801:Humans; D008495:Medical Oncology / Q000379:methods; D015233:Models, Statistical; D010919:Placebos; D012107:Research Design; D018401:Sample Size; D013810:Therapeutic Equivalency
D010919:Placebos