• Sarah L Whitehouse, Benjamin J R F Bolland, Jonathan R Howell, Ross W Crawford, and A John Timperley.
• Orthopaedic Research Unit, Institute of Health and Biomedical Innovation, Queensland University of Technology, The Prince Charles Hospital, Chermside, Queensland, Australia.
• J Arthroplasty. 2014 Sep 1;29(9):1827-34.

AbstractMortality following hip arthroplasty is affected by a large number of confounding variables each of which must be considered to enable valid interpretation. Relevant variables available from the 2011 NJR data set were included in the Cox model. Mortality rates in hip arthroplasty patients were lower than in the age-matched population across all hip types. Age at surgery, ASA grade, diagnosis, gender, provider type, hip type and lead surgeon grade all had a significant effect on mortality. Schemper's statistic showed that only 18.98% of the variation in mortality was explained by the variables available in the NJR data set. It is inappropriate to use NJR data to study an outcome affected by a multitude of confounding variables when these cannot be adequately accounted for in the available data set.Copyright © 2014 Elsevier Inc. All rights reserved.

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