Imputation of a true endpoint from a surrogate: application to a cluster randomized controlled trial with partial information on the true endpointReportar como inadecuado


Imputation of a true endpoint from a surrogate: application to a cluster randomized controlled trial with partial information on the true endpoint


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Publication Date: 2003-09-24

Language: English

Type: Article

Metadata: Show full item record

Citation: Nixon, R. M., Duffy, S., & Fender, G. R. K. (2003). Imputation of a true endpoint from a surrogate: application to a cluster randomized controlled trial with partial information on the true endpoint.

Description: RIGHTS : This article is licensed under the BioMed Central licence at http://www.biomedcentral.com/about/license which is similar to the 'Creative Commons Attribution Licence'. In brief you may : copy, distribute, and display the work; make derivative works; or make commercial use of the work - under the following conditions: the original author must be given credit; for any reuse or distribution, it must be made clear to others what the license terms of this work are.

Abstract: Abstract Background The Anglia Menorrhagia Education Study (AMES) is a randomized controlled trial testing the effectiveness of an education package applied to general practices. Binary data are available from two sources; general practitioner reported referrals to hospital, and referrals to hospital determined by independent audit of the general practices. The former may be regarded as a surrogate for the latter, which is regarded as the true endpoint. Data are only available for the true end point on a sub set of the practices, but there are surrogate data for almost all of the audited practices and for most of the remaining practices. Methods The aim of this paper was to estimate the treatment effect using data from every practice in the study. Where the true endpoint was not available, it was estimated by three approaches, a regression method, multiple imputation and a full likelihood model. Results Including the surrogate data in the analysis yielded an estimate of the treatment effect which was more precise than an estimate gained from using the true end point data alone. Conclusions The full likelihood method provides a new imputation tool at the disposal of trials with surrogate data.

Identifiers: http://dx.doi.org/10.1186/1471-2288-3-17

This record's URL: http://www.dspace.cam.ac.uk/handle/1810/238129

Rights:

Rights Holder: Nixon et al.; licensee BioMed Central Ltd.





Autor: Nixon, Richard M.Duffy, StephenFender, Guy R. K.

Fuente: https://www.repository.cam.ac.uk/handle/1810/238129



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