Dataset Information

Simulation data for an estimation of the maximum theoretical value and confidence interval for the correlation coefficient.

ABSTRACT: The data presented in this article are related to the article titled "Molecular Dynamics as a tool for in silico screening of skin permeability" (Rocco et al., 2017) [1]. Knowledge of the confidence interval and maximum theoretical value of the correlation coefficient r can prove useful to estimate the reliability of developed predictive models, in particular when there is great variability in compiled experimental datasets. In this Data in Brief article, data from purposely designed numerical simulations are presented to show how much the maximum r value is worsened by increasing the data uncertainty. The corresponding confidence interval of r is determined by using the Fisher r?Z transform.

SUBMITTER: Rocco P

PROVIDER: S-EPMC5540710 | biostudies-literature | 2017 Oct

REPOSITORIES: biostudies-literature

ACCESS DATA

Publications

Simulation data for an estimation of the maximum theoretical value and confidence interval for the correlation coefficient.

Rocco Paolo P Cilurzo Francesco F Minghetti Paola P Vistoli Giulio G Pedretti Alessandro A

Data in brief 20170723

The data presented in this article are related to the article titled "Molecular Dynamics as a tool for in silico screening of skin permeability" (Rocco et al., 2017) [1]. Knowledge of the confidence interval and maximum theoretical value of the correlation coefficient <i>r</i> can prove useful to estimate the reliability of developed predictive models, in particular when there is great variability in compiled experimental datasets. In this Data in Brief article, data from purposely designed nume ...[more]

PMID: 28795106

Similar Datasets

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

Simulation data for an estimation of the maximum theoretical value and confidence interval for the correlation coefficient.

Publications

Simulation data for an estimation of the maximum theoretical value and confidence interval for the correlation coefficient.

Similar Datasets

OmicsDI is part of the ELIXIR infrastructure

Tweets