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. 2020 Jul 1:370:m2215.
doi: 10.1136/bmj.m2215.

Non-adherence in non-inferiority trials: pitfalls and recommendations

Yin Mo  1   2   3   4 , Cherry Lim  5   4 , James A Watson  5   4 , Nicholas J White  5   4 , Ben S Cooper  5   4
Affiliations

Non-adherence in non-inferiority trials: pitfalls and recommendations

Yin Mo et al. BMJ. .

Erratum in

Abstract

Non-adherence in non-inferiority trials can affect treatment effect estimates and often increases the chance of claiming non-inferiority under the standard intention-to-treat analysis. This article discusses the implications of different patterns of non-adherence in non-inferiority trials and offers practical recommendations for trial design, alternative analysis strategies, and outcome reporting to reduce bias in treatment estimates and improve transparency in reporting.

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Conflict of interest statement

Competing interests: All authors have completed the ICMJE uniform disclosure form at www.icmje.org/coi_disclosure.pdf and declare: support from the Singapore National Medical Research Council and the UK Medical Research Council/Department for International Development for the submitted work; no financial relationships with any organisations that might have an interest in the submitted work in the previous three years; no other relationships or activities that could appear to have influenced the submitted work. Provenance and peer review: Not commissioned; externally peer reviewed.

Figures

Fig 1
Fig 1
Effect of non-adherence on biocreep. Panels show four scenarios if consecutive non-inferiority trials (comparing standard-of-care versus treatment A; treatment A versus treatment B; treatment B versus treatment C; treatment C versus treatment D) were to be carried out at 100%, 90%, 80% and 70% adherence. X axis represents consecutive non-inferiority trials; y axis represents decrease in true efficacies of treatments A, B, C, and D compared with the initial standard-of-care treatment. Treatments A, B, C, and D are 10%, 20%, 30%, and 40% less effective than the standard of care, respectively. Dot sizes are probabilities (represented by percentages next to dots) for the new and inferior experimental treatment to be accepted as non-inferior at the end of each trial. For example, if 100% adherence is maintained in the trials (first panel), the probability of treatment A being accepted as the new standard of care is 2%. By contrast, when the consecutive trials are conducted with 70% adherence (last panel), treatment D has a 7% chance that it will be accepted as the new standard of care, when its true efficacy is 40% less than the current standard of care. This pattern of non-adherence is crossover (that is, in the 70% adherence scenario, 30% of participants from each arm cross over to the opposite arm)
Fig 2
Fig 2
Common patterns of non-adherence in clinical trials. The directed acyclic graphs show the causal pathways from treatment allocation to outcome, highlighting the mechanisms causing non-adherence to allocated treatments
Fig 3
Fig 3
Causal associations among factors related to non-adherence in hypothetical non-inferiority trial (as described in the worked example)
Fig 4
Fig 4
Summary of data from hypothetical non-inferiority trial (as described in the worked example). Top panel shows the distribution of disease severity (range 0-1). Bottom panel shows the number of participants in each comparison group treated by one of two doctors used in the simulation
Fig 5
Fig 5
Comparison of analysis methods for hypothetical non-inferiority trial (as described in the worked example). Dashed line=non-inferiority margin. Both intention-to-treat and per protocol analyses reject the null hypothesis and agree that the experimental treatment is not inferior to the control treatment. Inverse probability weighting and instrumental variable estimation, however, would not have concluded non-inferiority
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