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Multiple imputation in a large-scale complex survey: a practical guide

¹ Department of Health Care Policy, Harvard Medical, School, 180 Longwood Ave., Boston, MA 02115, USA
² Department of Health Care Policy, Harvard Medical School, 180 Longwood Ave., Boston, MA 02115, USA
³ Department of Biostatistics and Computational Biology, Dana-Farber Cancer Institute, 375 Longwood Ave., Boston, MA 02115, USA

^* To whom correspondence should be addressed. E-mail: he{at}hcp.med.harvard.edu.

	Abstract

The Cancer Care Outcomes Research and Surveillance (CanCORS) Consortium is a multisite, multimode, multiwave study of the quality and patterns of care delivered to population-based cohorts of newly diagnosed patients with lung and colorectal cancer. As is typical in observational studies, missing data are a serious concern for CanCORS, following complicated patterns that impose severe challenges to the consortium investigators. Despite the popularity of multiple imputation of missing data, its acceptance and application still lag in large-scale studies with complicated data sets such as CanCORS. We use sequential regression multiple imputation, implemented in public-available software, to deal with non-response in the CanCORS surveys and construct a centralised completed database that can be easily used by investigators from multiple sites. Our work illustrates the feasibility of multiple imputation in a large-scale multiobjective survey, showing its capacity to handle complex missing data. We present the implementation process in detail as an example for practitioners and discuss some of the challenging issues which need further research.

First published on August 4, 2009
Statistical Methods in Medical Research 2009, doi:10.1177/0962280208101273


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