Premium
Statistical methods for building better biomarkers of chronic kidney disease
Author(s) -
Pencina Michael J.,
Parikh Chirag R.,
Kimmel Paul L.,
Cook Nancy R.,
Coresh Josef,
Feldman Harold I.,
Foulkes Andrea,
Gimotty Phyllis A.,
Hsu Chiyuan,
Lemley Kevin,
Song Peter,
Wilkins Kenneth,
Gossett Daniel R.,
Xie Yining,
Star Robert A.
Publication year - 2019
Publication title -
statistics in medicine
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 1.996
H-Index - 183
eISSN - 1097-0258
pISSN - 0277-6715
DOI - 10.1002/sim.8091
Subject(s) - biomarker , kidney disease , medicine , intensive care medicine , clinical practice , disease , data science , management science , computer science , pathology , family medicine , biochemistry , chemistry , economics
The last two decades have witnessed an explosion in research focused on the development and assessment of novel biomarkers for improved prognosis of diseases. As a result, best practice standards guiding biomarker research have undergone extensive development. Currently, there is great interest in the promise of biomarkers to enhance research efforts and clinical practice in the setting of chronic kidney disease, acute kidney injury, and glomerular disease. However, some have questioned whether biomarkers currently add value to the clinical practice of nephrology. The current state of the art pertaining to statistical analyses regarding the use of such measures is critical. In December 2014, the National Institute of Diabetes and Digestive and Kidney Diseases convened a meeting, “Toward Building Better Biomarker Statistical Methodology,” with the goals of summarizing the current best practice recommendations and articulating new directions for methodological research. This report summarizes its conclusions and describes areas that need attention. Suggestions are made regarding metrics that should be commonly reported. We outline the methodological issues related to traditional metrics and considerations in prognostic modeling, including discrimination and case mix, calibration, validation, and cost‐benefit analysis. We highlight the approach to improved risk communication and the value of graphical displays. Finally, we address some “new frontiers” in prognostic biomarker research, including the competing risk framework, the use of longitudinal biomarkers, and analyses in distributed research networks.