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Confidence interval for quantiles and percentiles.

ABSTRACT: Quantiles and percentiles represent useful statistical tools for describing the distribution of results and deriving reference intervals and performance specification in laboratory medicine. They are commonly intended as the sample estimate of a population parameter and therefore they need to be presented with a confidence interval (CI). In this work we discuss three methods to estimate CI on quantiles and percentiles using parametric, nonparametric and resampling (bootstrap) approaches. The result of our numerical simulations is that parametric methods are always more accurate regardless of sample size when the procedure is appropriate for the distribution of results for both extreme (2.5th and 97.5th) and central (25th, 50th and 75th) percentiles and corresponding quantiles. We also show that both nonparametric and bootstrap methods suit well the CI of central percentiles that are used to derive performance specifications through quality indicators of laboratory processes whose underlying distribution is unknown.

SUBMITTER: Ialongo C

PROVIDER: S-EPMC6294150 | biostudies-literature | 2019 Feb

REPOSITORIES: biostudies-literature

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Confidence interval for quantiles and percentiles.

Ialongo Cristiano C

Biochemia medica 20181215 1

Quantiles and percentiles represent useful statistical tools for describing the distribution of results and deriving reference intervals and performance specification in laboratory medicine. They are commonly intended as the sample estimate of a population parameter and therefore they need to be presented with a confidence interval (CI). In this work we discuss three methods to estimate CI on quantiles and percentiles using parametric, nonparametric and resampling (bootstrap) approaches. The res ...[more]

PMID: 30591808

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Project description:BackgroundThe intraclass correlation coefficient (ICC) is widely used in biomedical research to assess the reproducibility of measurements between raters, labs, technicians, or devices. For example, in an inter-rater reliability study, a high ICC value means that noise variability (between-raters and within-raters) is small relative to variability from patient to patient. A confidence interval or Bayesian credible interval for the ICC is a commonly reported summary. Such intervals can be constructed employing either frequentist or Bayesian methodologies.MethodsThis study examines the performance of three different methods for constructing an interval in a two-way, crossed, random effects model without interaction: the Generalized Confidence Interval method (GCI), the Modified Large Sample method (MLS), and a Bayesian method based on a noninformative prior distribution (NIB). Guidance is provided on interval construction method selection based on study design, sample size, and normality of the data. We compare the coverage probabilities and widths of the different interval methods.ResultsWe show that, for the two-way, crossed, random effects model without interaction, care is needed in interval method selection because the interval estimates do not always have properties that the user expects. While different methods generally perform well when there are a large number of levels of each factor, large differences between the methods emerge when the number of one or more factors is limited. In addition, all methods are shown to lack robustness to certain hard-to-detect violations of normality when the sample size is limited.ConclusionsDecision rules and software programs for interval construction are provided for practical implementation in the two-way, crossed, random effects model without interaction. All interval methods perform similarly when the data are normal and there are sufficient numbers of levels of each factor. The MLS and GCI methods outperform the NIB when one of the factors has a limited number of levels and the data are normally distributed or nearly normally distributed. None of the methods work well if the number of levels of a factor are limited and data are markedly non-normal. The software programs are implemented in the popular R language.

Dataset Information

Confidence interval for quantiles and percentiles.

Publications

Confidence interval for quantiles and percentiles.

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