A Variance Shrinkage Method Improves Arm-Based Bayesian Network Meta-Analysis

Details

• Personal Author:
  Chu H ; Hodges JS ; Lin L ; MacLehose RF ; Wang Z
• Description:
  Network meta-analysis is a commonly used tool to combine direct and indirect evidence in systematic reviews of multiple treatments to improve estimation compared to traditional pairwise meta-analysis. Unlike the contrast-based network meta-analysis approach, which focuses on estimating relative effects such as odds ratios, the arm-based network meta-analysis approach can estimate absolute risks and other effects, which are arguably more informative in medicine and public health. However, the number of clinical studies involving each treatment is often small in a network meta-analysis, leading to unstable treatment-specific variance estimates in the arm-based network meta-analysis approach when using non- or weakly informative priors under an unequal variance assumption. Additional assumptions, such as equal (i.e. homogeneous) variances for all treatments, may be used to remedy this problem, but such assumptions may be inappropriately strong. This article introduces a variance shrinkage method for an arm-based network meta-analysis. Specifically, we assume different treatment variances share a common prior with unknown hyperparameters. This assumption is weaker than the homogeneous variance assumption and improves estimation by shrinking the variances in a data-dependent way. We illustrate the advantages of the variance shrinkage method by reanalyzing a network meta-analysis of organized inpatient care interventions for stroke. Finally, comprehensive simulations investigate the impact of different variance assumptions on statistical inference, and simulation results show that the variance shrinkage method provides better estimation for log odds ratios and absolute risks. [Description provided by NIOSH]
• Subjects:
  Occupational Health Public Health Safety
• Keywords:
  Analytical Models Author Keywords: Bayesian Inference Mathematical Models Network Meta-analysis Public Health Statistical Analysis Statistical Quality Control Variance Prior Variance Shrinkage Method
• ISSN:
  0962-2802
• Document Type:
  Text
• Funding:
  Grant
• Genre:
  Journal Article
• Place as Subject:
  Florida ; Minnesota ; OSHA Region 4 ; OSHA Region 5
• CIO:
  National Institute for Occupational Safety and Health (NIOSH)
• Topic:
  Workplace Safety & Health
• Location:
  North America
• Pages in Document:
  151-165
• Volume:
  30
• Issue:
  1
• NIOSHTIC Number:
  nn:20064506
• Citation:
  Stat Methods Med Res 2021 Jan; 30(1):151-165
• Contact Point Address:
  Haitao Chu, Division of Biostatistics, School of Public Health, University of Minnesota, Minneapolis, MN 55455, USA
• Email:
  chux0051@umn.edu
• Federal Fiscal Year:
  2021
• Performing Organization:
  University of Minnesota Twin Cities
• Peer Reviewed:
  True
• Start Date:
  20050701
• Source Full Name:
  Statistical Methods in Medical Research
• End Date:
  20250630
• Collection(s):
  National Institute for Occupational Safety and Health
• Main Document Checksum:
  urn:sha-512:a57e02320875173d4341a61837ec69aec46fd2e74a52d835973d269b3a09b0041376c85e571c0de9a6dcf433396844fb1cb88b9958c7d9daece22906007905fe
• Download URL:
  https://stacks.cdc.gov/view/cdc/222956/cdc_222956_DS1.pdf
• File Type:
  [PDF - 444.02 KB ]