Aggregate-data estimation of an individual patient data linear random effects meta-analysis with a patient covariate-treatment interaction term.
Individual patient-data meta-analysis of randomized controlled trials is the gold standard for investigating how patient factors modify the effectiveness of treatment. Because participant data from primary studies might not be available, reliable alternatives using published data are needed. In this...
| Publicado en: | Biostatistics Vol. 14; no. 2; pp. 273 - 284 |
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| Autor principal: | |
| Formato: | research Journal Article |
| Publicado: |
Oxford University Press / USA
Apr2013
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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=104247684&site=ehost-live header: @attributes: shortDbName: ccm uiTerm: 104247684 longDbName: CINAHL Complete uiTag: AN controlInfo: bkinfo: dissinfo: jinfo: jid: 14654644 N58 jtl: Biostatistics issn: 14654644 maglogo: N pubinfo: dt: Apr2013 vid: 14 iid: 2 pid: 622 pub: Oxford University Press / USA artinfo: ui: 104247684 NLM23001065 2012036067 10.1093/biostatistics/kxs035 NLM23001065 PMC3590924 104247684 ppf: 273 ppct: 11 formats: tig: atl: Aggregate-data estimation of an individual patient data linear random effects meta-analysis with a patient covariate-treatment interaction term. aug: au: Kovalchik, Stephanie A affil: Division of Cancer Epidemiology and Genetics, National Cancer Institute, 6120 Executive Blvd., EPS 8047, Rockville, MD 20892, USA. sug: subj: Clinical Trials Linear Regression Meta Analysis Algorithms Analysis of Variance Antilipemic Agents Therapeutic Use Cholesterol Blood Coronary Arteriosclerosis Blood Coronary Arteriosclerosis Drug Therapy Coronary Arteriosclerosis Pathology Data Analysis, Statistical Human Male Pravastatin Therapeutic Use Probability Software Statistics Treatment Outcomes Male ab: Individual patient-data meta-analysis of randomized controlled trials is the gold standard for investigating how patient factors modify the effectiveness of treatment. Because participant data from primary studies might not be available, reliable alternatives using published data are needed. In this paper, I show that the maximum likelihood estimates of a participant-level linear random effects meta-analysis with a patient covariate-treatment interaction can be determined exactly from aggregate data when the model's variance components are known. I provide an equivalent aggregate-data EM algorithm and supporting software with the R package ipdmeta for the estimation of the "interaction meta-analysis" when the variance components are unknown. The properties of the methodology are assessed with simulation studies. The usefulness of the methods is illustrated with analyses of the effect modification of cholesterol and age on pravastatin in the multicenter placebo-controlled regression growth evaluation statin study. When a participant-level meta-analysis cannot be performed, aggregate-data interaction meta-analysis is a useful alternative for exploring individual-level sources of treatment effect heterogeneity. pubtype: Academic Journal doctype: meta analysis research Journal Article ougenre: Article language: English refInfo: holdings: @attributes: islocal: N |
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