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When does the use of individual patient data in network meta-analysis make a difference? A simulation study.

Steve Kanters1, Mohammad Ehsanul Karim2,3, Kristian Thorlund4

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Individual patient data (IPD) in network meta-analyses (NMA) significantly enhances estimate validity and precision compared to aggregate data (AgD) only. However, IPD benefits diminish in large, dense networks with limited data.

Keywords:
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Area of Science:

  • Biostatistics
  • Clinical Epidemiology
  • Health Economics

Background:

  • Network meta-analysis (NMA) is increasingly utilizing individual patient data (IPD).
  • Comparing IPD-NMA with aggregate data (AgD)-only NMA is crucial for understanding methodological impacts.
  • Simulations are essential for evaluating factors influencing NMA validity and precision.

Purpose of the Study:

  • To assess the impact of combining IPD and AgD versus AgD alone in NMA.
  • To determine how various factors influence the validity and precision of NMA estimates.
  • To compare the performance of different NMA strategies through simulation.

Main Methods:

  • Simulated three NMA strategies: AgD-NMA, AgD-NMA-MR, and IPD-AgD NMA.
  • Evaluated 108 parameter permutations, including network size, IPD proportion, and trial characteristics.
  • Assessed model performance using mean squared error (MSE), bias, and standard errors (SE).

Main Results:

  • IPD-NMA demonstrated superior validity and precision across most scenarios.
  • IPD-NMA showed a median MSE 0.54 times that of AgD-NMA-MR.
  • Standard errors for IPD-NMA treatment effects were 1/5 the size of AgD-NMA-MR.
  • IPD benefits were most pronounced in small or sparse networks.

Conclusions:

  • IPD significantly improves NMA validity and precision in most scenarios.
  • The impact of IPD is dependent on the amount of data available.
  • In large, dense networks, negligible IPD may offer limited additional value.