Active‐controlled, non‐inferiority trials in oncology: arbitrary limits, infeasible sample sizes and uninformative data analysis. Is there another way?

Abstract

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.

Antimicrobial Agents for Complicated Skin and Skin‐Structure Infections: Justification of Noninferiority Margins in the Absence of Placebo‐Controlled Trials

“…Preservation of 50% of the comparator drug's efficacy relative to placebo or no therapy has been suggested as reasonable when setting noninferiority margins [60–64], but others have argued that this method is highly conservative [65], and this issue has not been clarified [64]. To preserve one-half of the lower limit of the treatment effects we found, the non-inferiority margin should be 14% for cellulitis/erysipelas, 21% for wound or ulcer infection, and 7% for major abscess.…”

PISC Expert Team White Paper: Toward a Consistent Standard of Evidence When Evaluating the Efficacy of an Experimental Treatment From a Randomized, Active-Controlled Trial

“…For oncology noninferiority trials, Carroll (2006) proposed a stepwise approach to design and analysis. In the first design step, the trial is sized to show indirectly that the new treatment would have beaten placebo; in the second analysis step, the probability that the new treatment is superior to placebo is assessed and, if sufficiently high in the third and final step, the relative efficacy of the new treatment to active control is assessed on a continuum of effect retention via an "effect retention likelihood plot."…”

10 Years with ICH E10: Choice of Control Groups

“…These are the assumptions that must be made when the current study is conducted without a placebo control group. These issues are manifested in a number of different topics that E10 forces one to consider, including: Hypothesis testing framework with appropriate null and alternative hypotheses under a nested family of tests Establishment of the fixed margin based on estimation of active‐control effect from multiple historical studies Minimization of ‘bias towards no‐difference’ resulting from non‐adherence, treatment withdrawal, missing data, protocol deviations, and ‘bio‐creep’ Appropriate patient population to analyze (e.g., Intent‐to‐treat versus Per‐Protocol) Study designs with active and placebo controls …”

“…Hypothesis testing framework with appropriate null and alternative hypotheses under a nested family of tests [18,19] Establishment of the fixed margin based on estimation of active-control effect from multiple historical studies [20,21] Minimization of 'bias towards no-difference' resulting from non-adherence, treatment withdrawal, missing data, protocol deviations, and 'bio-creep' [6,22] Appropriate patient population to analyze (e.g., Intent-to-treat versus Per-Protocol) [23] Study designs with active and placebo controls [24] The literature is starting to be populated with case studies showing how these issues are being addressed by regulatory agencies [25]. Much of this effort has been within the regulatory agencies and has facilitated scientific discussion between sponsors and regulators.…”