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Network meta-analysis of longitudinal data using fractional polynomials.

J P Jansen1,2, M C Vieira3, S Cope4

  • 1Redwood Outcomes, San Francisco, CA, U.S.A.

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PubMed
Summary

This study introduces a novel network meta-analysis method for synthesizing treatment effects across multiple time points, even with differing study schedules. This approach enhances the analysis of repeated measures in randomized controlled trials (RCTs).

Keywords:
fractional polynomialsmixed treatment comparisonnetwork meta-analysisrepeated measuresstudy level data

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Area of Science:

  • Biostatistics
  • Clinical Epidemiology
  • Health Research Methodology

Background:

  • Traditional network meta-analysis (NMA) often synthesizes a single treatment effect measure per study.
  • Many randomized controlled trials (RCTs) report outcomes at multiple time points, presenting analytical challenges.
  • Existing methods struggle to incorporate diverse time points across studies in NMA.

Purpose of the Study:

  • To present a novel network meta-analysis method for simultaneous analysis of outcomes at multiple time points.
  • To model the development of outcomes over time using fractional polynomials.
  • To synthesize treatment effect parameters across studies using Bayesian NMA.

Main Methods:

  • Utilized fractional polynomials to model time-dependent treatment effects within RCTs.
  • Employed Bayesian network meta-analysis to synthesize differences in polynomial parameters across studies.
  • Applied fixed and random effects second-order fractional polynomials to a case study on knee osteoarthritis RCTs.

Main Results:

  • Demonstrated the feasibility of simultaneously analyzing outcomes from multiple time points in NMA.
  • Successfully synthesized treatment effects even when follow-up times varied across studies.
  • The proposed fractional polynomial models proved effective for repeated measures in NMA.

Conclusions:

  • The developed NMA models offer a valuable extension for synthesizing repeated measures data.
  • This method accommodates studies with differing time points, enhancing data utilization.
  • The approach is particularly useful for complex RCT data with multiple outcome assessments.