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Estimating negative likelihood ratio confidence when test sensitivity is 100%: A bootstrapping approach

Marill, Keith A ; Chang, Yuchiao ; Wong, Kim F ; Friedman, Ari B Davidian, Marie

Statistical methods in medical research, 2017-08, Vol.26 (4), p.1936-1948 [Periódico revisado por pares]

London, England: SAGE Publications

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  • Título:
    Estimating negative likelihood ratio confidence when test sensitivity is 100%: A bootstrapping approach
  • Autor: Marill, Keith A ; Chang, Yuchiao ; Wong, Kim F ; Friedman, Ari B
  • Davidian, Marie
  • Assuntos: Approaches ; Automation ; Binomial Distribution ; Bootstrap methods ; Confidence Intervals ; Coverage ; Critical Care - methods ; Diagnostic Tests, Routine - methods ; Diagnostic Tests, Routine - standards ; Emergency medical services ; Estimation ; Extremes ; Freeware ; Humans ; Intensive care ; Likelihood Functions ; Likelihood ratio ; Monte Carlo Method ; Patients ; Population ; Prognosis ; Sample Size ; Sensitivity ; Sensitivity analysis ; Sensitivity and Specificity ; Software ; Source code
  • É parte de: Statistical methods in medical research, 2017-08, Vol.26 (4), p.1936-1948
  • Notas: ObjectType-Article-1
    SourceType-Scholarly Journals-1
    ObjectType-Feature-2
    content type line 23
  • Descrição: Objectives Assessing high-sensitivity tests for mortal illness is crucial in emergency and critical care medicine. Estimating the 95% confidence interval (CI) of the likelihood ratio (LR) can be challenging when sample sensitivity is 100%. We aimed to develop, compare, and automate a bootstrapping method to estimate the negative LR CI when sample sensitivity is 100%. Methods The lowest population sensitivity that is most likely to yield sample sensitivity 100% is located using the binomial distribution. Random binomial samples generated using this population sensitivity are then used in the LR bootstrap. A free R program, “bootLR,” automates the process. Extensive simulations were performed to determine how often the LR bootstrap and comparator method 95% CIs cover the true population negative LR value. Finally, the 95% CI was compared for theoretical sample sizes and sensitivities approaching and including 100% using: (1) a technique of individual extremes, (2) SAS software based on the technique of Gart and Nam, (3) the Score CI (as implemented in the StatXact, SAS, and R PropCI package), and (4) the bootstrapping technique. Results The bootstrapping approach demonstrates appropriate coverage of the nominal 95% CI over a spectrum of populations and sample sizes. Considering a study of sample size 200 with 100 patients with disease, and specificity 60%, the lowest population sensitivity with median sample sensitivity 100% is 99.31%. When all 100 patients with disease test positive, the negative LR 95% CIs are: individual extremes technique (0,0.073), StatXact (0,0.064), SAS Score method (0,0.057), R PropCI (0,0.062), and bootstrap (0,0.048). Similar trends were observed for other sample sizes. Conclusions When study samples demonstrate 100% sensitivity, available methods may yield inappropriately wide negative LR CIs. An alternative bootstrapping approach and accompanying free open-source R package were developed to yield realistic estimates easily. This methodology and implementation are applicable to other binomial proportions with homogeneous responses.
  • Editor: London, England: SAGE Publications
  • Idioma: Inglês
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  • Realização: ABCD-USP USP   Logos de Redes Sociais: Freepik

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