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Meta-analysis of test accuracy studies using imputation for partial reporting of multiple thresholds.

Authors
  • Ensor, J1
  • Deeks, J J2
  • Martin, E C3
  • Riley, R D1
  • 1 Centre for Prognosis Research, Research Institute for Primary Care and Health Sciences, Keele University, Newcastle, UK.
  • 2 Institute of Applied Health Research, Public Health Building, University of Birmingham, Birmingham, UK.
  • 3 Manchester Pharmacy School, The University of Manchester, Manchester, UK.
Type
Published Article
Journal
Research synthesis methods
Publication Date
Mar 01, 2018
Volume
9
Issue
1
Pages
100–115
Identifiers
DOI: 10.1002/jrsm.1276
PMID: 29052347
Source
Medline
Keywords
Language
English
License
Unknown

Abstract

For tests reporting continuous results, primary studies usually provide test performance at multiple but often different thresholds. This creates missing data when performing a meta-analysis at each threshold. A standard meta-analysis (no imputation [NI]) ignores such missing data. A single imputation (SI) approach was recently proposed to recover missing threshold results. Here, we propose a new method that performs multiple imputation of the missing threshold results using discrete combinations (MIDC). The new MIDC method imputes missing threshold results by randomly selecting from the set of all possible discrete combinations which lie between the results for 2 known bounding thresholds. Imputed and observed results are then synthesised at each threshold. This is repeated multiple times, and the multiple pooled results at each threshold are combined using Rubin's rules to give final estimates. We compared the NI, SI, and MIDC approaches via simulation. Both imputation methods outperform the NI method in simulations. There was generally little difference in the SI and MIDC methods, but the latter was noticeably better in terms of estimating the between-study variances and generally gave better coverage, due to slightly larger standard errors of pooled estimates. Given selective reporting of thresholds, the imputation methods also reduced bias in the summary receiver operating characteristic curve. Simulations demonstrate the imputation methods rely on an equal threshold spacing assumption. A real example is presented. The SI and, in particular, MIDC methods can be used to examine the impact of missing threshold results in meta-analysis of test accuracy studies. © 2017 The Authors. Research Synthesis Methods published by John Wiley & Sons Ltd.

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