Tutorial: survival analysis -- a statistic for clinical, efficacy, and theoretical applications.
Current demands for increased research attention to therapeutic efficacy, efficiency, and also for improved developmental models call for analysis of longitudinal outcome data. Statistical treatment of longitudinal speech and language data is difficult, but there is a family of statistical technique...
| Publicado en: | Journal of Speech, Language & Hearing Research Vol. 42; no. 2; pp. 432 - 448 |
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| Autor principal: | |
| Formato: | equations & formulas tables/charts Journal Article |
| Publicado: |
American Speech-Language-Hearing Association
Apr1999
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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=107211665&site=ehost-live header: @attributes: shortDbName: ccm uiTerm: 107211665 longDbName: CINAHL Complete uiTag: AN controlInfo: bkinfo: dissinfo: jinfo: jid: 10924388 1SM jtl: Journal of Speech, Language & Hearing Research issn: 10924388 maglogo: N pubinfo: dt: Apr1999 vid: 42 iid: 2 pid: 42 pub: American Speech-Language-Hearing Association place: Rockville, Maryland artinfo: ui: 107211665 107211665 1999061893 10.1044/jslhr.4202.432 NLM10229458 107211665 ppf: 432 ppct: 16 formats: fmt: – @attributes: type: T – @attributes: type: P tig: atl: Tutorial: survival analysis -- a statistic for clinical, efficacy, and theoretical applications. aug: au: Gruber FA affil: University of Wisconsin-Madison sug: subj: Survival Analysis Research, Speech-Language-Hearing Therapy Outcome Assessment Funding Source Prospective Studies Treatment Outcomes Regression Parametric Statistics Nonparametric Statistics Kaplan-Meier Estimator Cox Proportional Hazards Model ab: Current demands for increased research attention to therapeutic efficacy, efficiency, and also for improved developmental models call for analysis of longitudinal outcome data. Statistical treatment of longitudinal speech and language data is difficult, but there is a family of statistical techniques in common use in medicine, actuarial science, manufacturing, and sociology that has not been used in speech or language research. Survival analysis is introduced as a method that avoids many of the statistical problems of other techniques because it treats time as the outcome. In survival analysis, probabilities are calculated not just for groups but also for individuals in a group. This is a major advantage for clinical work. This paper provides a basic introduction to nonparametric and semiparametric survival analysis using speech outcomes as examples. A brief discussion of potential conflicts between actuarial analysis and clinical intuition is also provided. pubtype: Academic Journal doctype: equations & formulas tables/charts Journal Article ougenre: Article language: English refInfo: holdings: @attributes: islocal: N |
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