A framework for the analysis of heterogeneity of treatment effect in patient-centered outcomes research.
Individuals vary in their response to a treatment. Understanding this heterogeneity of treatment effect is critical for evaluating how well a treatment can be expected to work for an individual or a subgroup of individuals. An overemphasis on hypothesis testing has resulted in a dichotomy of all het...
| Publicado en: | Journal of Clinical Epidemiology Vol. 66; no. 8; pp. 818 - 826 |
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| Autores principales: | , , , , |
| Formato: | research Journal Article |
| Publicado: |
Elsevier B.V.
2013
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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=104189207&site=ehost-live header: @attributes: shortDbName: ccm uiTerm: 104189207 longDbName: CINAHL Complete uiTag: AN controlInfo: bkinfo: dissinfo: jinfo: jid: 08954356 20C jtl: Journal of Clinical Epidemiology issn: 08954356 maglogo: N pubinfo: dt: 2013 vid: 66 iid: 8 pid: 82545 pub: Elsevier B.V. place: Philadelphia, Pennsylvania artinfo: ui: 104189207 NLM23651763 2012174868 10.1016/j.jclinepi.2013.02.009 NLM23651763 PMC4450361 104189207 ppf: 818 ppct: 8 formats: tig: atl: A framework for the analysis of heterogeneity of treatment effect in patient-centered outcomes research. aug: au: Varadhan, Ravi Segal, Jodi B Boyd, Cynthia M Wu, Albert W Weiss, Carlos O affil: Department of Medicine, Johns Hopkins University School of Medicine, Baltimore, MD 21205, USA; Department of Biostatistics, Johns Hopkins University Bloomberg School of Public Health, Baltimore, MD 21205, USA; The Center on Aging and Health, Johns Hopkins University, Baltimore, MD 21205, USA. Electronic address: rvaradhan@jhmi.edu. sug: subj: Health Services Research Methods Data Analysis, Statistical Outcome Assessment Methods Clinical Trials Human Meta Analysis Reproducibility of Results ab: Individuals vary in their response to a treatment. Understanding this heterogeneity of treatment effect is critical for evaluating how well a treatment can be expected to work for an individual or a subgroup of individuals. An overemphasis on hypothesis testing has resulted in a dichotomy of all heterogeneity of treatment effect analyses into confirmatory (hypothesis testing) and exploratory (hypothesis finding) analyses. This limited view of heterogeneity of treatment effect is inadequate for creating evidence that is useful for informing patient-centered decisions. An expanded framework for heterogeneity of treatment effect assessment is proposed. It recognizes four distinct goals of heterogeneity of treatment effect analyses: hypothesis testing, hypothesis finding, reporting subgroup effects for meta-analysis, and individual-level prediction. Accordingly, two new types of heterogeneity of treatment effect analyses are proposed: descriptive and predictive. Descriptive heterogeneity of treatment effect analyses report treatment effects for prespecified subgroups in accordance with prospectively specified analytic strategy. They need not be powered to detect heterogeneity of treatment effect. They emphasize estimation and reporting of subgroup effects rather than hypothesis testing. Sampling properties (e.g., standard error) of descriptive analysis can be characterized, thus facilitating meta-analysis of subgroup effects. Predictive heterogeneity of treatment effect analyses estimate probabilities of beneficial and adverse responses of individuals to treatments and facilitates optimal treatment decisions for different types of individuals. Procedures are also suggested to improve reliability of heterogeneity of treatment effect assessment from observational studies. Heterogeneity of treatment effect analysis should be identified as confirmatory, descriptive, exploratory, or predictive analysis. Evidence should be interpreted in a manner consistent with the analytic goal. pubtype: Academic Journal doctype: research Journal Article ougenre: Article language: English refInfo: holdings: @attributes: islocal: N |
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