Biomarker combinations for diagnosis and prognosis in multicenter studies: Principles and methods

Allison Meisner, Chirag R. Parikh, Kathleen F. Kerr

Research output: Contribution to journalArticlepeer-review

6 Scopus citations


Many investigators are interested in combining biomarkers to predict a binary outcome or detect underlying disease. This endeavor is complicated by the fact that many biomarker studies involve data from multiple centers. Depending upon the relationship between center, the biomarkers, and the target of prediction, care must be taken when constructing and evaluating combinations of biomarkers. We introduce a taxonomy to describe the role of center and consider how a biomarker combination should be constructed and evaluated. We show that ignoring center, which is frequently done by clinical researchers, is often not appropriate. The limited statistical literature proposes using random intercept logistic regression models, an approach that we demonstrate is generally inadequate and may be misleading. We instead propose using fixed intercept logistic regression, which appropriately accounts for center without relying on untenable assumptions. After constructing the biomarker combination, we recommend using performance measures that account for the multicenter nature of the data, namely the center-adjusted area under the receiver operating characteristic curve. We apply these methods to data from a multicenter study of acute kidney injury after cardiac surgery. Appropriately accounting for center, both in construction and evaluation, may increase the likelihood of identifying clinically useful biomarker combinations.

Original languageEnglish (US)
Pages (from-to)969-985
Number of pages17
JournalStatistical Methods in Medical Research
Issue number4
StatePublished - Apr 1 2019
Externally publishedYes


  • Biomarkers
  • combinations
  • diagnosis
  • multicenter
  • prognosis

ASJC Scopus subject areas

  • Epidemiology
  • Statistics and Probability
  • Health Information Management


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