Data-driven methods distort optimal cutoffs and accuracy estimates of depression screening tools: a simulation study using individual participant data

Parash Mani Bhandari, Brooke Levis, Dipika Neupane, Scott B. Patten, Ian Shrier, Brett D. Thombs, Andrea Benedetti, the Depression Screening Data (DEPRESSD) EPDS Group

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2 Citations (Scopus)

Abstract

Objective: To evaluate, across multiple sample sizes, the degree that data-driven methods result in (1) optimal cutoffs different from population optimal cutoff and (2) bias in accuracy estimates. Study design and setting: A total of 1,000 samples of sample size 100, 200, 500 and 1,000 each were randomly drawn to simulate studies of different sample sizes from a database (n = 13,255) synthesized to assess Edinburgh Postnatal Depression Scale (EPDS) screening accuracy. Optimal cutoffs were selected by maximizing Youden's J (sensitivity+specificity–1). Optimal cutoffs and accuracy estimates in simulated samples were compared to population values. Results: Optimal cutoffs in simulated samples ranged from ≥ 5 to ≥ 17 for n = 100, ≥ 6 to ≥ 16 for n = 200, ≥ 6 to ≥ 14 for n = 500, and ≥ 8 to ≥ 13 for n = 1,000. Percentage of simulated samples identifying the population optimal cutoff (≥ 11) was 30% for n = 100, 35% for n = 200, 53% for n = 500, and 71% for n = 1,000. Mean overestimation of sensitivity and underestimation of specificity were 6.5 percentage point (pp) and -1.3 pp for n = 100, 4.2 pp and -1.1 pp for n = 200, 1.8 pp and -1.0 pp for n = 500, and 1.4 pp and -1.0 pp for n = 1,000. Conclusions: Small accuracy studies may identify inaccurate optimal cutoff and overstate accuracy estimates with data-driven methods.

Original languageEnglish
Pages (from-to)137-147
Number of pages11
JournalJournal of Clinical Epidemiology
Volume137
DOIs
Publication statusPublished - Sep 2021

Keywords

  • Accuracy estimates
  • Bias
  • Cherry-picking
  • Data-driven methods
  • Depression
  • Optimal cutoff

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