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Detecting and accounting for violations of the constancy assumption in non-inferiority clinical trials.

ABSTRACT: Randomized, placebo-controlled clinical trials are the gold standard for evaluating a novel therapeutic agent. In some instances, it may not be considered ethical or desirable to complete a placebo-controlled clinical trial and, instead, the placebo is replaced by an active comparator with the objective of showing either superiority or non-inferiority to the active comparator. In a non-inferiority trial, the experimental treatment is considered non-inferior if it retains a pre-specified proportion of the effect of the active comparator as represented by the non-inferiority margin. A key assumption required for valid inference in the non-inferiority setting is the constancy assumption, which requires that the effect of the active comparator in the non-inferiority trial is consistent with the effect that was observed in previous trials. It has been shown that violations of the constancy assumption can result in a dramatic increase in the rate of incorrectly concluding non-inferiority in the presence of ineffective or even harmful treatment. In this paper, we illustrate how Bayesian hierarchical modeling can be used to facilitate multi-source smoothing of the data from the current trial with the data from historical studies, enabling direct probabilistic evaluation of the constancy assumption. We then show how this result can be used to adapt the non-inferiority margin when the constancy assumption is violated and present simulation results illustrating that our method controls the type-I error rate when the constancy assumption is violated, while retaining the power of the standard approach when the constancy assumption holds. We illustrate our adaptive procedure using a non-inferiority trial of raltegravir, an antiretroviral drug for the treatment of HIV.

SUBMITTER: Koopmeiners JS

PROVIDER: S-EPMC5332430 | biostudies-literature | 2018 May

REPOSITORIES: biostudies-literature

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Detecting and accounting for violations of the constancy assumption in non-inferiority clinical trials.

Koopmeiners Joseph S JS Hobbs Brian P BP

Statistical methods in medical research 20160901 5

Randomized, placebo-controlled clinical trials are the gold standard for evaluating a novel therapeutic agent. In some instances, it may not be considered ethical or desirable to complete a placebo-controlled clinical trial and, instead, the placebo is replaced by an active comparator with the objective of showing either superiority or non-inferiority to the active comparator. In a non-inferiority trial, the experimental treatment is considered non-inferior if it retains a pre-specified proporti ...[more]

PMID: 27587591

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Project description:BackgroundNon-inferiority trials are increasingly used to evaluate new treatments that are expected to have secondary advantages over standard of care, but similar efficacy on the primary outcome. When designing a non-inferiority trial with a binary primary outcome, the choice of effect measure for the non-inferiority margin (e.g. risk ratio or risk difference) has an important effect on sample size calculations; furthermore, if the control event risk observed is markedly different from that assumed, the trial can quickly lose power or the results become difficult to interpret.MethodsWe propose a new way of designing non-inferiority trials to overcome the issues raised by unexpected control event risks. Our proposal involves using clinical judgement to specify a 'non-inferiority frontier', i.e. a curve defining the most appropriate non-inferiority margin for each possible value of control event risk. Existing trials implicitly use frontiers defined by a fixed risk ratio or a fixed risk difference. We discuss their limitations and propose a fixed arcsine difference frontier, using the power-stabilising transformation for binary outcomes, which may better represent clinical judgement. We propose and compare three ways of designing a trial using this frontier: testing and reporting on the arcsine scale; testing on the arcsine scale but reporting on the risk difference or risk ratio scale; and modifying the margin on the risk difference or risk ratio scale after observing the control event risk according to the power-stabilising frontier.ResultsTesting and reporting on the arcsine scale leads to results which are challenging to interpret clinically. For small values of control event risk, testing on the arcsine scale and reporting results on the risk difference scale produces confidence intervals at a higher level than the nominal one or non-inferiority margins that are slightly smaller than those back-calculated from the power-stabilising frontier alone. However, working on the arcsine scale generally requires a larger sample size compared to the risk difference scale. Therefore, working on the risk difference scale, modifying the margin after observing the control event risk, might be preferable, as it requires a smaller sample size. However, this approach tends to slightly inflate type I error rate; a solution is to use a slightly lower significance level for testing, although this modestly reduces power. When working on the risk ratio scale instead, the same approach based on the modification of the margin leads to power levels above the nominal one, maintaining type I error under control.ConclusionsOur proposed methods of designing non-inferiority trials using power-stabilising non-inferiority frontiers make trial design more resilient to unexpected values of the control event risk, at the only cost of requiring somewhat larger sample sizes when the goal is to report results on the risk difference scale.

Project description:BackgroundIn a non-inferiority (NI) trial, analysis based on the intention-to-treat (ITT) principle is anti-conservative, so current guidelines recommend analysing on a per-protocol (PP) population in addition. However, PP analysis relies on the often implausible assumption of no confounders. Randomisation-based efficacy estimators (RBEEs) allow for treatment non-adherence while maintaining a comparison of randomised groups. Fischer et al. have developed an approach for estimating RBEEs in randomised trials with two active treatments, a common feature of NI trials. The aim of this paper was to demonstrate the use of RBEEs in NI trials using this approach, and to appraise the feasibility of these estimators as the primary analysis in NI trials.MethodsTwo NI trials were used. One comparing two different dosing regimens for the maintenance of remission in people with ulcerative colitis (CODA), and the other comparing an orally administered treatment to an intravenously administered treatment in preventing skeletal-related events in patients with bone metastases from breast cancer (ZICE). Variables that predicted adherence in each of the trial arms, and were also independent of outcome, were sought in each of the studies. Structural mean models (SMMs) were fitted that conditioned on these variables, and the point estimates and confidence intervals compared to that found in the corresponding ITT and PP analyses.ResultsIn the CODA study, no variables were found that differentially predicted treatment adherence while remaining independent of outcome. The SMM, using standard methodology, moved the point estimate closer to 0 (no difference between arms) compared to the ITT and PP analyses, but the confidence interval was still within the NI margin, indicating that the conclusions drawn would remain the same. In the ZICE study, cognitive functioning as measured by the corresponding domain of the QLQ-C30, and use of chemotherapy at baseline were both differentially associated with adherence while remaining independent of outcome. However, while the SMM again moved the point estimate closer to 0, the confidence interval was wide, overlapping with any NI margin that could be justified.ConclusionDeriving RBEEs in NI trials with two active treatments can provide a randomisation-respecting estimate of treatment efficacy that accounts for treatment adherence, is straightforward to implement, but requires thorough planning during the design stage of the study to ensure that strong baseline predictors of treatment are captured. Extension of the approach to handle nonlinear outcome variables is also required.Trial registrationThe CODA study: ClinicalTrials.gov, identifier: NCT00708656 . Registered on 8 April 2008. The ZICE study trial: ClinicalTrials.gov, identifier: NCT00326820 . Registered on 16 May 2006.

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Detecting and accounting for violations of the constancy assumption in non-inferiority clinical trials.

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Detecting and accounting for violations of the constancy assumption in non-inferiority clinical trials.

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