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Continuous(ly) missing outcome data in network meta-analysis: A one-stage pattern-mixture model approach.

Loukia M Spineli1, Chrysostomos Kalyvas2, Katerina Papadimitropoulou3,4

  • 1Midwifery Research and Education Unit, Hannover Medical School, Hannover, Germany.

Statistical Methods in Medical Research
|January 7, 2021
PubMed
Summary
This summary is machine-generated.

This study introduces a novel Bayesian one-stage pattern-mixture model to handle missing continuous outcome data in network meta-analyses. This approach offers more flexible modeling, improving the reliability of systematic review conclusions.

Keywords:
Bayesian analysisNetwork meta-analysiscontinuous outcomemissing outcome datapattern-mixture model

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Area of Science:

  • Statistics
  • Biostatistics
  • Medical Informatics

Background:

  • Systematic reviews require appropriate handling of aggregate missing outcome data to minimize bias.
  • Existing two-stage pattern-mixture models offer improvements but lack flexibility in modeling missing continuous outcome data.
  • This limits thorough investigation into the implications of missing data on review conclusions.

Purpose of the Study:

  • To propose a flexible one-stage pattern-mixture model within a Bayesian framework for addressing missing continuous outcome data in network meta-analyses.
  • To gain insights into the missingness process across different trials and interventions within a network.
  • To extend hierarchical network meta-analysis models to incorporate missingness parameters.

Main Methods:

  • Development of a one-stage pattern-mixture model under the Bayesian framework.
  • Extension of hierarchical network meta-analysis models to include a missingness parameter.
  • Consideration of informative missingness parameters (difference and ratio of means) and various prior structures.

Main Results:

  • The proposed model allows for flexible modeling of missing continuous outcome data.
  • It enables the investigation of the missingness process across different trials and interventions.
  • The method was successfully exemplified in two published network meta-analysis datasets with varying amounts of missing data.

Conclusions:

  • The one-stage Bayesian pattern-mixture model provides a more flexible and robust approach to handling aggregate missing continuous outcome data in network meta-analyses.
  • This method enhances the reliability of systematic review conclusions by better accounting for the missingness mechanism.
  • The approach facilitates a deeper understanding of how missing data influences review findings.