Methods. Using multiple imputation for analysis of incomplete data in clinical research.
BACKGROUND: Sample loss and missing data are inevitable in multivariate and longitudinal research. Ad hoc approaches such as analysis of incomplete data or substituting the group mean for missing data, while common, may unnecessarily reduce statistical power and threaten study validity. Multiple imp...
| Publicado en: | Nursing Research Vol. 51; no. 5; pp. 339 - 344 |
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| Autor principal: | |
| Formato: | tables/charts Journal Article |
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Lippincott Williams & Wilkins
2002 Sep-Oct
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| Acceso en línea: | Ver este registro en EBSCOhost |
| fields | @attributes: recordID: 1 pdfLink: plink: https://search.ebscohost.com/login.aspx?direct=true&db=ccm&AN=106982707&site=ehost-live header: @attributes: shortDbName: ccm uiTerm: 106982707 longDbName: CINAHL Complete uiTag: AN controlInfo: bkinfo: dissinfo: jinfo: jid: 00296562 1HA jtl: Nursing Research issn: 00296562 maglogo: N pubinfo: dt: 2002 Sep-Oct vid: 51 iid: 5 pid: 433 pub: Lippincott Williams & Wilkins place: Baltimore, Maryland artinfo: ui: 106982707 106982707 2002160085 10.1097/00006199-200209000-00012 NLM12352784 106982707 ppf: 339 ppct: 5 formats: tig: atl: Methods. Using multiple imputation for analysis of incomplete data in clinical research. aug: au: McCleary L affil: Junin-Lunenfeld Applied Research Unit, Baycrest Centre for Geriatric Care, 3560 Bathurst Street, Toronto, Ontario, Canada M6A 2E1; lmccleary@klaru-baycrest.on.ca sug: subj: Data Analysis, Statistical Clinical Nursing Research Variable T-Tests Data Analysis Software Prospective Studies Descriptive Statistics ab: BACKGROUND: Sample loss and missing data are inevitable in multivariate and longitudinal research. Ad hoc approaches such as analysis of incomplete data or substituting the group mean for missing data, while common, may unnecessarily reduce statistical power and threaten study validity. Multiple imputation for missing data is a newly accessible, methodologically rigorous approach to dealing with the problem of missing data. APPROACH: To (a) discuss the problem of missing data in clinical research, and (b) describe the technique of multiple imputation. A case of analysis of multivariate psychosocial data is presented to illustrate the practice of multiple imputation. RESULTS: The advantages of multiple imputation are it (a) results in unbiased estimates, providing more validity than ad hoc approaches to missing data; (b) uses all available data, preserving sample size and statistical power; (c) may be used with standard statistical software; and, (d) results are readily interpreted. DISCUSSION: Accessible, user-friendly computer programs are available to perform multiple imputation for missing data making ad hoc approaches to missing data obsolete. pubtype: Academic Journal doctype: tables/charts Journal Article ougenre: Article language: English refInfo: holdings: @attributes: islocal: N |
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