Exploring how non-inferiority and equivalence are assessed in paediatrics: a systematic review

Camille Aupiais1, Sarah Zohar2, Garry Taverny3

  • 1Inserm UMR 1123-ECEVE, UMRS 1138, Team 22, CRC; APHP, Hôpital Robert Debré, University Paris Diderot, Paris, France.

Insights

Quality of non-inferiority and equivalence trials in paediatrics needs improvement. Methodological reporting and interpretation issues, particularly concerning margins and conclusions, are prevalent in pediatric research.

Area of Science:

  • Clinical Trials
  • Paediatric Research
  • Biostatistics

Background:

  • Non-inferiority and equivalence trials are crucial in paediatric research to demonstrate therapeutic non-inferiority or equivalence.
  • Evaluating the quality of these trials is essential for reliable clinical decision-making in children.

Purpose of the Study:

  • To review the characteristics, methodology, and reporting standards of non-inferiority and equivalence trials specifically within the paediatric context.
  • To identify areas for improvement in the design, execution, and reporting of these critical trial types in children.

Main Methods:

  • Systematic search of PubMed and Cochrane databases up to September 2016.
  • Inclusion criteria: non-inferiority/equivalence randomized controlled trials in children, published in high-impact journals.
  • Analysis of trial characteristics, statistical methods, margin reporting, and author conclusions.

Main Results:

  • 125 trials were included; 10% had inconsistent statistical hypotheses.
  • Trials often evaluated interventions with easier administration or better safety profiles.
  • Margins were reported in 95% of trials, but justification methods were only reported in 37%; margins were wider in smaller trials.
  • Disagreement with author conclusions occurred in 11% of reports.

Conclusions:

  • Significant improvements are needed in the methodology, reporting, and interpretation of paediatric non-inferiority and equivalence trials.
  • Unjustified margins and conclusions unsupported by data are common issues.
  • Development and application of appropriate methods are necessary to address challenges like small sample sizes in this vulnerable population.
Abstract

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