An empirical comparison of three methods for multiple cut-off diagnostic test meta-analysis of the Patient Health Questionnaire-9 (PHQ-9) depression screening tool using published data versus individual level data

Andrea Benedetti*, Brooke Levis , Gerta Rücker , Hayley E Jones, Martin Schumacher , John P A Ioannidis , Brett Thombs , DEPRESsion Screening Data (DEPRESSD) Collaboration

*Corresponding author for this work

Research output: Contribution to journalArticle (Academic Journal)peer-review

Abstract

Selective cut-off reporting in primary diagnostic accuracy studies with continuous or ordinal data may result in biased estimates when meta-analyzing studies. Collecting individual participant data (IPD) and estimating accuracy across all relevant cut-offs for all studies can overcome such bias but is labour-intensive.

We meta-analyzed the diagnostic accuracy of the Patient Health Questionnaire-9 (PHQ-9) depression screening tool. We compared results for two statistical methods proposed by Steinhauser and by Jones to account for missing cut-offs, with results from a series of bivariate random effects models (BRM) estimated separately at each cut-off. We applied the methods to a dataset that contained information only on cut-offs that were reported in the primary publications,
and to the full IPD dataset that contained information for all cut-offs for every study. For each method, we estimated pooled sensitivity and specificity and associated 95% confidence intervals for each cut-off and area under the curve (AUC).

The full IPD dataset comprised data from 45 studies, 15,020 subjects and 1,972 cases of major depression, and included information on every possible cut-off.

When using data available in publications, using statistical approaches out-performed the BRM applied to the same data.

AUC was similar for all approaches when using the full IPD dataset, though pooled estimates were slightly different.

Overall, using statistical methods to fill in missing cut-off data recovered the receiver operating characteristic (ROC) curve from the full IPD dataset well when using only the published subset. All methods performed similarly when applied to the full IPD dataset.
Original languageEnglish
Number of pages16
JournalResearch Synthesis Methods
Early online date13 Sep 2020
DOIs
Publication statusE-pub ahead of print - 13 Sep 2020

Keywords

  • individual participant data
  • meta-analysis
  • diagnostic accuracy
  • bivariate random effects model

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