Reporting and handling of missing data in predictive research for prevalent undiagnosed type 2 diabetes mellitus: a systematic review

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dc.contributor.authorMasconi, Katya Len_ZA
dc.contributor.authorMatsha, Tandien_ZA
dc.contributor.authorEchouffo-Tcheugui, Justinen_ZA
dc.contributor.authorErasmus, Rajiven_ZA
dc.contributor.authorKengne, Andreen_ZA
dc.date.accessioned2015-12-07T08:52:14Z
dc.date.available2015-12-07T08:52:14Z
dc.date.issued2015en_ZA
dc.description.abstractMissing values are common in health research and omitting participants with missing data often leads to loss of statistical power, biased estimates and, consequently, inaccurate inferences. We critically reviewed the challenges posed by missing data in medical research and approaches to address them. To achieve this more efficiently, these issues were analyzed and illustrated through a systematic review on the reporting of missing data and imputation methods (prediction of missing values through relationships within and between variables) undertaken in risk prediction studies of undiagnosed diabetes. Prevalent diabetes risk models were selected based on a recent comprehensive systematic review, supplemented by an updated search of English-language studies published between 1997 and 2014. Reporting of missing data has been limited in studies of prevalent diabetes prediction. Of the 48 articles identified, 62.5% (n=30) did not report any information on missing data or handling techniques. In 21 (43.8%) studies, researchers opted out of imputation, completing case-wise deletion of participants missing any predictor values. Although imputation methods are encouraged to handle missing data and ensure the accuracy of inferences, this has seldom been the case in studies of diabetes risk prediction. Hence, we elaborated on the various types and patterns of missing data, the limitations of case-wise deletion and state-of the-art methods of imputations and their challenges. This review highlights the inexperience or disregard of investigators of the effect of missing data in risk prediction research. Formal guidelines may enhance the reporting and appropriate handling of missing data in scientific journals.en_ZA
dc.identifier.apacitationMasconi, K. L., Matsha, T., Echouffo-Tcheugui, J., Erasmus, R., & Kengne, A. (2015). Reporting and handling of missing data in predictive research for prevalent undiagnosed type 2 diabetes mellitus: a systematic review. <i>EPMA Journal</i>, http://hdl.handle.net/11427/15656en_ZA
dc.identifier.chicagocitationMasconi, Katya L, Tandi Matsha, Justin Echouffo-Tcheugui, Rajiv Erasmus, and Andre Kengne "Reporting and handling of missing data in predictive research for prevalent undiagnosed type 2 diabetes mellitus: a systematic review." <i>EPMA Journal</i> (2015) http://hdl.handle.net/11427/15656en_ZA
dc.identifier.citationMasconi, K. L., Matsha, T. E., Echouffo-Tcheugui, J. B., Erasmus, R. T., & Kengne, A. P. (2015). Reporting and handling of missing data in predictive research for prevalent undiagnosed type 2 diabetes mellitus: a systematic review. EPMA Journal, 6(1), 7.en_ZA
dc.identifier.ris TY - Journal Article AU - Masconi, Katya L AU - Matsha, Tandi AU - Echouffo-Tcheugui, Justin AU - Erasmus, Rajiv AU - Kengne, Andre AB - Missing values are common in health research and omitting participants with missing data often leads to loss of statistical power, biased estimates and, consequently, inaccurate inferences. We critically reviewed the challenges posed by missing data in medical research and approaches to address them. To achieve this more efficiently, these issues were analyzed and illustrated through a systematic review on the reporting of missing data and imputation methods (prediction of missing values through relationships within and between variables) undertaken in risk prediction studies of undiagnosed diabetes. Prevalent diabetes risk models were selected based on a recent comprehensive systematic review, supplemented by an updated search of English-language studies published between 1997 and 2014. Reporting of missing data has been limited in studies of prevalent diabetes prediction. Of the 48 articles identified, 62.5% (n=30) did not report any information on missing data or handling techniques. In 21 (43.8%) studies, researchers opted out of imputation, completing case-wise deletion of participants missing any predictor values. Although imputation methods are encouraged to handle missing data and ensure the accuracy of inferences, this has seldom been the case in studies of diabetes risk prediction. Hence, we elaborated on the various types and patterns of missing data, the limitations of case-wise deletion and state-of the-art methods of imputations and their challenges. This review highlights the inexperience or disregard of investigators of the effect of missing data in risk prediction research. Formal guidelines may enhance the reporting and appropriate handling of missing data in scientific journals. DA - 2015 DB - OpenUCT DO - 10.1186/s13167-015-0028-0 DP - University of Cape Town J1 - EPMA Journal LK - https://open.uct.ac.za PB - University of Cape Town PY - 2015 T1 - Reporting and handling of missing data in predictive research for prevalent undiagnosed type 2 diabetes mellitus: a systematic review TI - Reporting and handling of missing data in predictive research for prevalent undiagnosed type 2 diabetes mellitus: a systematic review UR - http://hdl.handle.net/11427/15656 ER - en_ZA
dc.identifier.urihttp://hdl.handle.net/11427/15656
dc.identifier.urihttp://dx.doi.org/10.1186/s13167-015-0028-0
dc.identifier.vancouvercitationMasconi KL, Matsha T, Echouffo-Tcheugui J, Erasmus R, Kengne A. Reporting and handling of missing data in predictive research for prevalent undiagnosed type 2 diabetes mellitus: a systematic review. EPMA Journal. 2015; http://hdl.handle.net/11427/15656.en_ZA
dc.language.isoengen_ZA
dc.publisherBioMed Central Ltden_ZA
dc.publisher.departmentDepartment of Medicineen_ZA
dc.publisher.facultyFaculty of Health Sciencesen_ZA
dc.publisher.institutionUniversity of Cape Town
dc.rightsThis is an Open Access article distributed under the terms of the Creative Commons Attribution Licenseen_ZA
dc.rights.holder2015 Masconi et al.; licensee BioMed Central.en_ZA
dc.rights.urihttp://creativecommons.org/licenses/by/4.0en_ZA
dc.sourceEPMA Journalen_ZA
dc.source.urihttp://www.epmajournal.com/en_ZA
dc.subject.otherPredictiveen_ZA
dc.subject.otherPreventive and Personalized Medicineen_ZA
dc.subject.otherDiabetes mellitusen_ZA
dc.subject.otherRisken_ZA
dc.subject.otherGuidelinesen_ZA
dc.subject.otherPatternsen_ZA
dc.subject.otherScreeningen_ZA
dc.subject.otherModelingen_ZA
dc.subject.otherPatient Stratificationen_ZA
dc.titleReporting and handling of missing data in predictive research for prevalent undiagnosed type 2 diabetes mellitus: a systematic reviewen_ZA
dc.typeJournal Articleen_ZA
uct.type.filetypeText
uct.type.filetypeImage
uct.type.publicationResearchen_ZA
uct.type.resourceArticleen_ZA
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