Literature DB >> 24567441

Optimal selection of individuals for repeated covariate measurements in follow-up studies.

Jaakko Reinikainen1, Juha Karvanen2, Hanna Tolonen3.   

Abstract

Repeated covariate measurements bring important information on the time-varying risk factors in long epidemiological follow-up studies. However, due to budget limitations, it may be possible to carry out the repeated measurements only for a subset of the cohort. We study cost-efficient alternatives for the simple random sampling in the selection of the individuals to be remeasured. The proposed selection criteria are based on forms of the D-optimality. The selection methods are compared with the simulation studies and illustrated with the data from the East-West study carried out in Finland from 1959 to 1999. The results indicate that cost savings can be achieved if the selection is focused on the individuals with high expected risk of the event and, on the other hand, on those with extreme covariate values in the previous measurements.
© The Author(s) 2014.

Keywords:  data collection; follow-up study; missing covariate data; optimal design; repeated measurements

Mesh:

Year:  2014        PMID: 24567441     DOI: 10.1177/0962280214523952

Source DB:  PubMed          Journal:  Stat Methods Med Res        ISSN: 0962-2802            Impact factor:   3.021


  1 in total

1.  Principles of Experimental Design for Big Data Analysis.

Authors:  Christopher C Drovandi; Christopher Holmes; James M McGree; Kerrie Mengersen; Sylvia Richardson; Elizabeth G Ryan
Journal:  Stat Sci       Date:  2017-08       Impact factor: 2.901

  1 in total

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