File Download

There are no files associated with this item.

  Links for fulltext
     (May Require Subscription)
Supplementary

Article: Bias Control In The Analysis Of Case–control Studies With Incidence Density Sampling

TitleBias Control In The Analysis Of Case–control Studies With Incidence Density Sampling
Authors
KeywordsBias reduction
logistic regression
incidence density sampling
matched case–control study
Issue Date2019
PublisherOxford University Press. The Journal's web site is located at http://ije.oxfordjournals.org/
Citation
International Journal of Epidemiology, 2019, Epub How to Cite?
AbstractBackground: Previous simulation studies of the case–control study design using incidence density sampling, which required individual matching for time, showed biased estimates of association from conditional logistic regression (CLR) analysis; however, the reason for this is unknown. Separately, in the analysis of case–control studies using the exclusive sampling design, it has been shown that unconditional logistic regression (ULR) with adjustment for an individually matched binary factor can give unbiased estimates. The validity of this analytic approach in incidence density sampling needs evaluation. Methods: In extensive simulations using incidence density sampling, we evaluated various analytic methods: CLR with and without a bias-reduction method, ULR with adjustment for time in quintiles (and residual time within quintiles) and ULR with adjustment for matched sets and bias reduction. We re-analysed a case–control study of Haemophilus influenzae type B vaccine using these methods. Results: We found that the bias in the CLR analysis from previous studies was due to sparse data bias. It can be controlled by the bias-reduction method for CLR or by increasing the number of cases and/or controls. ULR with adjustment for time in quintiles usually gave results highly comparable to CLR, despite breaking the matches. Further adjustment for residual time trends was needed in the case of time-varying effects. ULR with adjustment for matched sets tended to perform poorly despite bias reduction. Conclusions: Studies using incidence density sampling may be analysed by either ULR with adjustment for time or CLR, possibly with bias reduction.
Persistent Identifierhttp://hdl.handle.net/10722/279506
ISSN
2019 Impact Factor: 7.707
2015 SCImago Journal Rankings: 4.381
ISI Accession Number ID

 

DC FieldValueLanguage
dc.contributor.authorCheung, YB-
dc.contributor.authorMa, X-
dc.contributor.authorLam, KF-
dc.contributor.authorLi, J-
dc.contributor.authorMilligan, P-
dc.date.accessioned2019-11-01T07:18:40Z-
dc.date.available2019-11-01T07:18:40Z-
dc.date.issued2019-
dc.identifier.citationInternational Journal of Epidemiology, 2019, Epub-
dc.identifier.issn0300-5771-
dc.identifier.urihttp://hdl.handle.net/10722/279506-
dc.description.abstractBackground: Previous simulation studies of the case–control study design using incidence density sampling, which required individual matching for time, showed biased estimates of association from conditional logistic regression (CLR) analysis; however, the reason for this is unknown. Separately, in the analysis of case–control studies using the exclusive sampling design, it has been shown that unconditional logistic regression (ULR) with adjustment for an individually matched binary factor can give unbiased estimates. The validity of this analytic approach in incidence density sampling needs evaluation. Methods: In extensive simulations using incidence density sampling, we evaluated various analytic methods: CLR with and without a bias-reduction method, ULR with adjustment for time in quintiles (and residual time within quintiles) and ULR with adjustment for matched sets and bias reduction. We re-analysed a case–control study of Haemophilus influenzae type B vaccine using these methods. Results: We found that the bias in the CLR analysis from previous studies was due to sparse data bias. It can be controlled by the bias-reduction method for CLR or by increasing the number of cases and/or controls. ULR with adjustment for time in quintiles usually gave results highly comparable to CLR, despite breaking the matches. Further adjustment for residual time trends was needed in the case of time-varying effects. ULR with adjustment for matched sets tended to perform poorly despite bias reduction. Conclusions: Studies using incidence density sampling may be analysed by either ULR with adjustment for time or CLR, possibly with bias reduction.-
dc.languageeng-
dc.publisherOxford University Press. The Journal's web site is located at http://ije.oxfordjournals.org/-
dc.relation.ispartofInternational Journal of Epidemiology-
dc.rightsPre-print: Journal Title] ©: [year] [owner as specified on the article] Published by Oxford University Press [on behalf of xxxxxx]. All rights reserved. Pre-print (Once an article is published, preprint notice should be amended to): This is an electronic version of an article published in [include the complete citation information for the final version of the Article as published in the print edition of the Journal.] Post-print: This is a pre-copy-editing, author-produced PDF of an article accepted for publication in [insert journal title] following peer review. The definitive publisher-authenticated version [insert complete citation information here] is available online at: xxxxxxx [insert URL that the author will receive upon publication here].-
dc.subjectBias reduction-
dc.subjectlogistic regression-
dc.subjectincidence density sampling-
dc.subjectmatched case–control study-
dc.titleBias Control In The Analysis Of Case–control Studies With Incidence Density Sampling-
dc.typeArticle-
dc.identifier.emailLam, KF: hrntlkf@hkucc.hku.hk-
dc.identifier.authorityLam, KF=rp00718-
dc.description.naturelink_to_subscribed_fulltext-
dc.identifier.doi10.1093/ije/dyz116-
dc.identifier.scopuseid_2-s2.0-85077175388-
dc.identifier.hkuros308299-
dc.identifier.isiWOS:000509522900031-
dc.publisher.placeUnited Kingdom-
dc.identifier.issnl0300-5771-

Export via OAI-PMH Interface in XML Formats


OR


Export to Other Non-XML Formats