Interval estimation and optimal design for the within-subject coefficient of variation for continuous and binary variables

Mohamed M. Shoukri, Naser Elkum, Stephen D. Walter

Research output: Contribution to journalArticle

8 Citations (Scopus)

Abstract

Background: In this paper we propose the use of the within-subject coefficient of variation as an index of a measurement's reliability. For continuous variables and based on its maximum likelihood estimation we derive a variance-stabilizing transformation and discuss confidence interval construction within the framework of a one-way random effects model. We investigate sample size requirements for the within-subject coefficient of variation for continuous and binary variables. Methods: We investigate the validity of the approximate normal confidence interval by Monte Carlo simulations. In designing a reliability study, a crucial issue is the balance between the number of subjects to be recruited and the number of repeated measurements per subject. We discuss efficiency of estimation and cost considerations for the optimal allocation of the sample resources. The approach is illustrated by an example on Magnetic Resonance Imaging (MRI). We also discuss the issue of sample size estimation for dichotomous responses with two examples. Results: For the continuous variable we found that the variance stabilizing transformation improves the asymptotic coverage probabilities on the within-subject coefficient of variation for the continuous variable. The maximum like estimation and sample size estimation based on prespecified width of confidence interval are novel contribution to the literature for the binary variable. Conclusion: Using the sample size formulas, we hope to help clinical epidemiologists and practicing statisticians to efficiently design reliability studies using the within-subject coefficient of variation, whether the variable of interest is continuous or binary.

Original languageEnglish
Article number24
JournalBMC Medical Research Methodology
Volume6
DOIs
Publication statusPublished - 10 May 2006
Externally publishedYes

Fingerprint

Sample Size
Confidence Intervals
Resource Allocation
Magnetic Resonance Imaging
Costs and Cost Analysis

ASJC Scopus subject areas

  • Medicine(all)
  • Health Informatics

Cite this

Interval estimation and optimal design for the within-subject coefficient of variation for continuous and binary variables. / Shoukri, Mohamed M.; Elkum, Naser; Walter, Stephen D.

In: BMC Medical Research Methodology, Vol. 6, 24, 10.05.2006.

Research output: Contribution to journalArticle

@article{913de2b6e3dc433285c6101e66bf0a5f,
title = "Interval estimation and optimal design for the within-subject coefficient of variation for continuous and binary variables",
abstract = "Background: In this paper we propose the use of the within-subject coefficient of variation as an index of a measurement's reliability. For continuous variables and based on its maximum likelihood estimation we derive a variance-stabilizing transformation and discuss confidence interval construction within the framework of a one-way random effects model. We investigate sample size requirements for the within-subject coefficient of variation for continuous and binary variables. Methods: We investigate the validity of the approximate normal confidence interval by Monte Carlo simulations. In designing a reliability study, a crucial issue is the balance between the number of subjects to be recruited and the number of repeated measurements per subject. We discuss efficiency of estimation and cost considerations for the optimal allocation of the sample resources. The approach is illustrated by an example on Magnetic Resonance Imaging (MRI). We also discuss the issue of sample size estimation for dichotomous responses with two examples. Results: For the continuous variable we found that the variance stabilizing transformation improves the asymptotic coverage probabilities on the within-subject coefficient of variation for the continuous variable. The maximum like estimation and sample size estimation based on prespecified width of confidence interval are novel contribution to the literature for the binary variable. Conclusion: Using the sample size formulas, we hope to help clinical epidemiologists and practicing statisticians to efficiently design reliability studies using the within-subject coefficient of variation, whether the variable of interest is continuous or binary.",
author = "Shoukri, {Mohamed M.} and Naser Elkum and Walter, {Stephen D.}",
year = "2006",
month = "5",
day = "10",
doi = "10.1186/1471-2288-6-24",
language = "English",
volume = "6",
journal = "BMC Medical Research Methodology",
issn = "1471-2288",
publisher = "BioMed Central",

}

TY - JOUR

T1 - Interval estimation and optimal design for the within-subject coefficient of variation for continuous and binary variables

AU - Shoukri, Mohamed M.

AU - Elkum, Naser

AU - Walter, Stephen D.

PY - 2006/5/10

Y1 - 2006/5/10

N2 - Background: In this paper we propose the use of the within-subject coefficient of variation as an index of a measurement's reliability. For continuous variables and based on its maximum likelihood estimation we derive a variance-stabilizing transformation and discuss confidence interval construction within the framework of a one-way random effects model. We investigate sample size requirements for the within-subject coefficient of variation for continuous and binary variables. Methods: We investigate the validity of the approximate normal confidence interval by Monte Carlo simulations. In designing a reliability study, a crucial issue is the balance between the number of subjects to be recruited and the number of repeated measurements per subject. We discuss efficiency of estimation and cost considerations for the optimal allocation of the sample resources. The approach is illustrated by an example on Magnetic Resonance Imaging (MRI). We also discuss the issue of sample size estimation for dichotomous responses with two examples. Results: For the continuous variable we found that the variance stabilizing transformation improves the asymptotic coverage probabilities on the within-subject coefficient of variation for the continuous variable. The maximum like estimation and sample size estimation based on prespecified width of confidence interval are novel contribution to the literature for the binary variable. Conclusion: Using the sample size formulas, we hope to help clinical epidemiologists and practicing statisticians to efficiently design reliability studies using the within-subject coefficient of variation, whether the variable of interest is continuous or binary.

AB - Background: In this paper we propose the use of the within-subject coefficient of variation as an index of a measurement's reliability. For continuous variables and based on its maximum likelihood estimation we derive a variance-stabilizing transformation and discuss confidence interval construction within the framework of a one-way random effects model. We investigate sample size requirements for the within-subject coefficient of variation for continuous and binary variables. Methods: We investigate the validity of the approximate normal confidence interval by Monte Carlo simulations. In designing a reliability study, a crucial issue is the balance between the number of subjects to be recruited and the number of repeated measurements per subject. We discuss efficiency of estimation and cost considerations for the optimal allocation of the sample resources. The approach is illustrated by an example on Magnetic Resonance Imaging (MRI). We also discuss the issue of sample size estimation for dichotomous responses with two examples. Results: For the continuous variable we found that the variance stabilizing transformation improves the asymptotic coverage probabilities on the within-subject coefficient of variation for the continuous variable. The maximum like estimation and sample size estimation based on prespecified width of confidence interval are novel contribution to the literature for the binary variable. Conclusion: Using the sample size formulas, we hope to help clinical epidemiologists and practicing statisticians to efficiently design reliability studies using the within-subject coefficient of variation, whether the variable of interest is continuous or binary.

UR - http://www.scopus.com/inward/record.url?scp=33745320133&partnerID=8YFLogxK

UR - http://www.scopus.com/inward/citedby.url?scp=33745320133&partnerID=8YFLogxK

U2 - 10.1186/1471-2288-6-24

DO - 10.1186/1471-2288-6-24

M3 - Article

C2 - 16686943

AN - SCOPUS:33745320133

VL - 6

JO - BMC Medical Research Methodology

JF - BMC Medical Research Methodology

SN - 1471-2288

M1 - 24

ER -