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Interval estimation for minimal clinically important difference and its classification error via a bootstrap scheme

Statistical Theory and Related Fields 2020年第2期4卷 135-145页

作者： Zehua Zhou Jiwei Zhao Melissa Kluczynski Department of Biostatistics School of Public Health and Health ProfessionsState University of New York at BuffaloBuffaloNYUSA Department of Orthopaedics Jacobs School of Medicine and Biomedical SciencesState University of New York at BuffaloBuffaloNYUSA

With the improved knowledge on clinical relevance and more convenient access to the patientreported outcome data,clinical researchers prefer to adopt minimal clinically important difference(MCID)rather than statistical significance as a testing standard to examine the effectiveness of certain intervention or treatment in clinical trials.A practical method to determining the MCID is based on the diagnostic *** using this approach,the MCID can be formulated as the solution of a large margin classification ***,this method only produces the point estimation,hence lacks ways to evaluate its *** this paper,we introduce an m-out-of-n bootstrap approach which provides the interval estimations for MCID and its classification error,an associated accuracy measure for performance assessment.A variety of extensive simulation studies are implemented to show the advantages of our proposed *** of the chondral lesions and meniscus procedures(ChAMP)trial is our motivating example and is used to illustrate our method.

关键词： Minimal clinically important difference classification error confidence interval non-convex optimisation bootstrap m-out-of-n bootstrap

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Accuracy Assessment and Guidelines for Manual Traffic Counts from Pre-Recorded Video Data

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Journal of Transportation Technologies 2023年第4期13卷 497-523页

作者： Mishuk Majumder Chester Wilmot Department of Civil & Environmental Engineering Louisiana State University Baton Rouge LA USA

Traffic count is the fundamental data source for transportation planning, management, design, and effectiveness evaluation. Recording traffic flow and counting from the recorded videos are increasingly used due to convenience, high accuracy, and cost-effectiveness. Manual counting from pre-recorded video footage can be prone to inconsistencies and errors, leading to inaccurate counts. Besides, there are no standard guidelines for collecting video data and conducting manual counts from the recorded videos. This paper aims to comprehensively assess the accuracy of manual counts from pre-recorded videos and introduces guidelines for efficiently collecting video data and conducting manual counts by trained individuals. The accuracy assessment of the manual counts was conducted based on repeated counts, and the guidelines were provided from the experience of conducting a traffic survey on forty strip mall access points in Baton Rouge, Louisiana, USA. The percentage of total error, classification error, and interval error were found to be 1.05 percent, 1.08 percent, and 1.29 percent, respectively. Besides, the percent root mean square errors (RMSE) were found to be 1.13 percent, 1.21 percent, and 1.48 percent, respectively. Guidelines were provided for selecting survey sites, instruments and timeframe, fieldwork, and manual counts for an efficient traffic data collection survey.

关键词： Traffic Survey Counting error Transportation Planning Total error Collecting Video Data classification error Standard Guidelines Repeated Counts Interval error

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