A joint latent variable model approach to item reduction and validation.
Identifieur interne : 002527 ( PubMed/Corpus ); précédent : 002526; suivant : 002528A joint latent variable model approach to item reduction and validation.
Auteurs : Steffanie M. Halberstadt ; Kathryn H. Schmitz ; Mary D. SammelSource :
- Biostatistics (Oxford, England) [ 1468-4357 ] ; 2012.
English descriptors
- KwdEn :
- Bias (Epidemiology), Biostatistics, Breast Neoplasms (surgery), Factor Analysis, Statistical, Female, Humans, Lymph Node Excision (adverse effects), Lymphedema (diagnosis), Lymphedema (etiology), Models, Statistical, Postoperative Complications (diagnosis), Postoperative Complications (etiology), Severity of Illness Index, Validation Studies as Topic.
- MESH :
- adverse effects : Lymph Node Excision.
- diagnosis : Lymphedema, Postoperative Complications.
- etiology : Lymphedema, Postoperative Complications.
- surgery : Breast Neoplasms.
- Bias (Epidemiology), Biostatistics, Factor Analysis, Statistical, Female, Humans, Models, Statistical, Severity of Illness Index, Validation Studies as Topic.
Abstract
Many applications of biomedical science involve unobservable constructs, from measurement of health states to severity of complex diseases. The primary aim of measurement is to identify relevant pieces of observable information that thoroughly describe the construct of interest. Validation of the construct is often performed separately. Noting the increasing popularity of latent variable methods in biomedical research, we propose a Multiple Indicator Multiple Cause (MIMIC) latent variable model that combines item reduction and validation. Our joint latent variable model accounts for the bias that occurs in the traditional 2-stage process. The methods are motivated by an example from the Physical Activity and Lymphedema clinical trial in which the objectives were to describe lymphedema severity through self-reported Likert scale symptoms and to determine the relationship between symptom severity and a "gold standard" diagnostic measure of lymphedema. The MIMIC model identified 1 symptom as a potential candidate for removal. We present this paper as an illustration of the advantages of joint latent variable models and as an example of the applicability of these models for biomedical research.
DOI: 10.1093/biostatistics/kxr018
PubMed: 21775486
Links to Exploration step
pubmed:21775486Le document en format XML
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The primary aim of measurement is to identify relevant pieces of observable information that thoroughly describe the construct of interest. Validation of the construct is often performed separately. Noting the increasing popularity of latent variable methods in biomedical research, we propose a Multiple Indicator Multiple Cause (MIMIC) latent variable model that combines item reduction and validation. Our joint latent variable model accounts for the bias that occurs in the traditional 2-stage process. The methods are motivated by an example from the Physical Activity and Lymphedema clinical trial in which the objectives were to describe lymphedema severity through self-reported Likert scale symptoms and to determine the relationship between symptom severity and a "gold standard" diagnostic measure of lymphedema. The MIMIC model identified 1 symptom as a potential candidate for removal. We present this paper as an illustration of the advantages of joint latent variable models and as an example of the applicability of these models for biomedical research.</div></front></TEI><pubmed><MedlineCitation Status="MEDLINE" Owner="NLM"><PMID Version="1">21775486</PMID><DateCreated><Year>2011</Year><Month>12</Month><Day>14</Day></DateCreated><DateCompleted><Year>2012</Year><Month>04</Month><Day>17</Day></DateCompleted><DateRevised><Year>2016</Year><Month>10</Month><Day>19</Day></DateRevised><Article PubModel="Print-Electronic"><Journal><ISSN IssnType="Electronic">1468-4357</ISSN><JournalIssue CitedMedium="Internet"><Volume>13</Volume><Issue>1</Issue><PubDate><Year>2012</Year><Month>Jan</Month></PubDate></JournalIssue><Title>Biostatistics (Oxford, England)</Title><ISOAbbreviation>Biostatistics</ISOAbbreviation></Journal><ArticleTitle>A joint latent variable model approach to item reduction and validation.</ArticleTitle><Pagination><MedlinePgn>48-60</MedlinePgn></Pagination><ELocationID EIdType="doi" ValidYN="Y">10.1093/biostatistics/kxr018</ELocationID><Abstract><AbstractText>Many applications of biomedical science involve unobservable constructs, from measurement of health states to severity of complex diseases. The primary aim of measurement is to identify relevant pieces of observable information that thoroughly describe the construct of interest. Validation of the construct is often performed separately. Noting the increasing popularity of latent variable methods in biomedical research, we propose a Multiple Indicator Multiple Cause (MIMIC) latent variable model that combines item reduction and validation. Our joint latent variable model accounts for the bias that occurs in the traditional 2-stage process. The methods are motivated by an example from the Physical Activity and Lymphedema clinical trial in which the objectives were to describe lymphedema severity through self-reported Likert scale symptoms and to determine the relationship between symptom severity and a "gold standard" diagnostic measure of lymphedema. The MIMIC model identified 1 symptom as a potential candidate for removal. We present this paper as an illustration of the advantages of joint latent variable models and as an example of the applicability of these models for biomedical research.</AbstractText></Abstract><AuthorList CompleteYN="Y"><Author ValidYN="Y"><LastName>Halberstadt</LastName><ForeName>Steffanie M</ForeName><Initials>SM</Initials><AffiliationInfo><Affiliation>Department of Biostatistics and Epidemiology, University of Pennsylvania, Philadelphia, PA 19104, USA. halberst@mail.med.upenn.edu</Affiliation></AffiliationInfo></Author><Author ValidYN="Y"><LastName>Schmitz</LastName><ForeName>Kathryn H</ForeName><Initials>KH</Initials></Author><Author ValidYN="Y"><LastName>Sammel</LastName><ForeName>Mary D</ForeName><Initials>MD</Initials></Author></AuthorList><Language>eng</Language><GrantList CompleteYN="Y"><Grant><GrantID>T32 MH065218</GrantID><Acronym>MH</Acronym><Agency>NIMH NIH HHS</Agency><Country>United States</Country></Grant><Grant><GrantID>R01-CA106851</GrantID><Acronym>CA</Acronym><Agency>NCI NIH HHS</Agency><Country>United States</Country></Grant><Grant><GrantID>T32 CA93283</GrantID><Acronym>CA</Acronym><Agency>NCI NIH HHS</Agency><Country>United States</Country></Grant></GrantList><PublicationTypeList><PublicationType UI="D016428">Journal Article</PublicationType><PublicationType UI="D052061">Research Support, N.I.H., Extramural</PublicationType></PublicationTypeList><ArticleDate DateType="Electronic"><Year>2011</Year><Month>07</Month><Day>20</Day></ArticleDate></Article><MedlineJournalInfo><Country>England</Country><MedlineTA>Biostatistics</MedlineTA><NlmUniqueID>100897327</NlmUniqueID><ISSNLinking>1465-4644</ISSNLinking></MedlineJournalInfo><CitationSubset>IM</CitationSubset><CommentsCorrectionsList><CommentsCorrections RefType="Cites"><RefSource>N Engl J Med. 2009 Aug 13;361(7):664-73</RefSource><PMID Version="1">19675330</PMID></CommentsCorrections><CommentsCorrections 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Version="1">13634291</PMID></CommentsCorrections><CommentsCorrections RefType="Cites"><RefSource>Stat Methods Med Res. 2008 Feb;17(1):5-32</RefSource><PMID Version="1">17855748</PMID></CommentsCorrections><CommentsCorrections RefType="Cites"><RefSource>J Clin Oncol. 2008 Dec 10;26(35):5689-96</RefSource><PMID Version="1">19001331</PMID></CommentsCorrections><CommentsCorrections RefType="Cites"><RefSource>J Clin Oncol. 2009 Apr 20;27(12):2007-14</RefSource><PMID Version="1">19289624</PMID></CommentsCorrections><CommentsCorrections RefType="Cites"><RefSource>Biometrics. 1996 Jun;52(2):650-63</RefSource><PMID Version="1">8672706</PMID></CommentsCorrections></CommentsCorrectionsList><MeshHeadingList><MeshHeading><DescriptorName UI="D015982" MajorTopicYN="N">Bias (Epidemiology)</DescriptorName></MeshHeading><MeshHeading><DescriptorName UI="D056808" MajorTopicYN="N">Biostatistics</DescriptorName></MeshHeading><MeshHeading><DescriptorName UI="D001943" MajorTopicYN="N">Breast Neoplasms</DescriptorName><QualifierName UI="Q000601" MajorTopicYN="N">surgery</QualifierName></MeshHeading><MeshHeading><DescriptorName UI="D005163" MajorTopicYN="N">Factor Analysis, Statistical</DescriptorName></MeshHeading><MeshHeading><DescriptorName UI="D005260" MajorTopicYN="N">Female</DescriptorName></MeshHeading><MeshHeading><DescriptorName UI="D006801" MajorTopicYN="N">Humans</DescriptorName></MeshHeading><MeshHeading><DescriptorName UI="D008197" MajorTopicYN="N">Lymph Node Excision</DescriptorName><QualifierName UI="Q000009" MajorTopicYN="N">adverse effects</QualifierName></MeshHeading><MeshHeading><DescriptorName UI="D008209" MajorTopicYN="N">Lymphedema</DescriptorName><QualifierName UI="Q000175" MajorTopicYN="N">diagnosis</QualifierName><QualifierName UI="Q000209" MajorTopicYN="N">etiology</QualifierName></MeshHeading><MeshHeading><DescriptorName UI="D015233" MajorTopicYN="Y">Models, Statistical</DescriptorName></MeshHeading><MeshHeading><DescriptorName UI="D011183" MajorTopicYN="N">Postoperative Complications</DescriptorName><QualifierName UI="Q000175" MajorTopicYN="N">diagnosis</QualifierName><QualifierName UI="Q000209" MajorTopicYN="N">etiology</QualifierName></MeshHeading><MeshHeading><DescriptorName UI="D012720" MajorTopicYN="N">Severity of Illness Index</DescriptorName></MeshHeading><MeshHeading><DescriptorName UI="D054928" MajorTopicYN="N">Validation Studies as Topic</DescriptorName></MeshHeading></MeshHeadingList><OtherID Source="NLM">PMC3276271</OtherID></MedlineCitation><PubmedData><History><PubMedPubDate PubStatus="entrez"><Year>2011</Year><Month>7</Month><Day>22</Day><Hour>6</Hour><Minute>0</Minute></PubMedPubDate><PubMedPubDate PubStatus="pubmed"><Year>2011</Year><Month>7</Month><Day>22</Day><Hour>6</Hour><Minute>0</Minute></PubMedPubDate><PubMedPubDate 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