View in EDS

Drawing statistical conclusions from experiments with multiple quantitative measurements per subject.

Saved in:
Bibliographic Details
Title: Drawing statistical conclusions from experiments with multiple quantitative measurements per subject.
Authors: Holland-Letz, Tim1 (AUTHOR) t.holland-letz@dkfz.de, Kopp-Schneider, Annette1 (AUTHOR)
Source: Radiotherapy & Oncology. Nov2020, Vol. 152, p30-33. 4p.
Subjects: Standard deviations, Statistical models, Confidence intervals, Measurement, Experiments
Abstract: In experiments with multiple quantitative measurements per subject, for example measurements on multiple lesions per patient, the additional measurements on the same patient provide limited additional information. Treating these measurements as independent observations will produce biased estimators for standard deviations and confidence intervals, and increases the risk of false positives in statistical tests. The problem can be remedied in a simple way by first taking the average of all observations of each specific patient, and then doing all further calculations only on the list of these patient means. A more sophisticated statistical modeling of the experiment, for example in a linear mixed model, is only required if (i) there is a large imbalance in the number of observations per patient or (ii) there is a specific interest in actually identifying the various sources of variation in the experiment. [ABSTRACT FROM AUTHOR]
Copyright of Radiotherapy & Oncology is the property of Elsevier B.V. and its content may not be copied or emailed to multiple sites without the copyright holder's express written permission. Additionally, content may not be used with any artificial intelligence tools or machine learning technologies. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)
Database: Engineering Source
FullText Text:
  Availability: 0
Header DbId: egs
DbLabel: Engineering Source
An: 147153383
AccessLevel: 6
PubType: Academic Journal
PubTypeId: academicJournal
PreciseRelevancyScore: 0
IllustrationInfo
Items – Name: Title
  Label: Title
  Group: Ti
  Data: Drawing statistical conclusions from experiments with multiple quantitative measurements per subject.
– Name: Author
  Label: Authors
  Group: Au
  Data: <searchLink fieldCode="AR" term="%22Holland-Letz%2C+Tim%22">Holland-Letz, Tim</searchLink><relatesTo>1</relatesTo> (AUTHOR)<i> t.holland-letz@dkfz.de</i><br /><searchLink fieldCode="AR" term="%22Kopp-Schneider%2C+Annette%22">Kopp-Schneider, Annette</searchLink><relatesTo>1</relatesTo> (AUTHOR)
– Name: TitleSource
  Label: Source
  Group: Src
  Data: <searchLink fieldCode="JN" term="%22Radiotherapy+%26+Oncology%22">Radiotherapy & Oncology</searchLink>. Nov2020, Vol. 152, p30-33. 4p.
– Name: Subject
  Label: Subjects
  Group: Su
  Data: <searchLink fieldCode="DE" term="%22Standard+deviations%22">Standard deviations</searchLink><br /><searchLink fieldCode="DE" term="%22Statistical+models%22">Statistical models</searchLink><br /><searchLink fieldCode="DE" term="%22Confidence+intervals%22">Confidence intervals</searchLink><br /><searchLink fieldCode="DE" term="%22Measurement%22">Measurement</searchLink><br /><searchLink fieldCode="DE" term="%22Experiments%22">Experiments</searchLink>
– Name: Abstract
  Label: Abstract
  Group: Ab
  Data: In experiments with multiple quantitative measurements per subject, for example measurements on multiple lesions per patient, the additional measurements on the same patient provide limited additional information. Treating these measurements as independent observations will produce biased estimators for standard deviations and confidence intervals, and increases the risk of false positives in statistical tests. The problem can be remedied in a simple way by first taking the average of all observations of each specific patient, and then doing all further calculations only on the list of these patient means. A more sophisticated statistical modeling of the experiment, for example in a linear mixed model, is only required if (i) there is a large imbalance in the number of observations per patient or (ii) there is a specific interest in actually identifying the various sources of variation in the experiment. [ABSTRACT FROM AUTHOR]
– Name: AbstractSuppliedCopyright
  Label:
  Group: Ab
  Data: <i>Copyright of Radiotherapy & Oncology is the property of Elsevier B.V. and its content may not be copied or emailed to multiple sites without the copyright holder's express written permission. Additionally, content may not be used with any artificial intelligence tools or machine learning technologies. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract.</i> (Copyright applies to all Abstracts.)
PLink https://search.ebscohost.com/login.aspx?direct=true&site=eds-live&db=egs&AN=147153383
RecordInfo BibRecord:
  BibEntity:
    Identifiers:
      – Type: doi
        Value: 10.1016/j.radonc.2020.08.009
    Languages:
      – Code: eng
        Text: English
    PhysicalDescription:
      Pagination:
        PageCount: 4
        StartPage: 30
    Subjects:
      – SubjectFull: Standard deviations
        Type: general
      – SubjectFull: Statistical models
        Type: general
      – SubjectFull: Confidence intervals
        Type: general
      – SubjectFull: Measurement
        Type: general
      – SubjectFull: Experiments
        Type: general
    Titles:
      – TitleFull: Drawing statistical conclusions from experiments with multiple quantitative measurements per subject.
        Type: main
  BibRelationships:
    HasContributorRelationships:
      – PersonEntity:
          Name:
            NameFull: Holland-Letz, Tim
      – PersonEntity:
          Name:
            NameFull: Kopp-Schneider, Annette
    IsPartOfRelationships:
      – BibEntity:
          Dates:
            – D: 01
              M: 11
              Text: Nov2020
              Type: published
              Y: 2020
          Identifiers:
            – Type: issn-print
              Value: 01678140
          Numbering:
            – Type: volume
              Value: 152
          Titles:
            – TitleFull: Radiotherapy & Oncology
              Type: main
ResultId 1