title: Improving patient prostate cancer risk assessment: Moving from static, globally-applied to dynamic, practice-specific risk calculators.
abstract: Clinical risk calculators are now widely available but have generally been implemented in a static and one-size-fits-all fashion. The objective of this study was to challenge these notions and show via a case study concerning risk-based screening for prostate cancer how calculators can be dynamically and locally tailored to improve on-site patient accuracy. Yearly data from five international prostate biopsy cohorts (3 in the US, 1 in Austria, 1 in England) were used to compare 6 methods for annual risk prediction: static use of the online US-developed Prostate Cancer Prevention Trial Risk Calculator (PCPTRC); recalibration of the PCPTRC; revision of the PCPTRC; building a new model each year using logistic regression, Bayesian prior-to-posterior updating, or random forests. All methods performed similarly with respect to discrimination, except for random forests, which were worse. All methods except for random forests greatly improved calibration over the static PCPTRC in all cohorts except for Austria, where the PCPTRC had the best calibration followed closely by recalibration. The case study shows that a simple annual recalibration of a general online risk tool for prostate cancer can improve its accuracy with respect to the local patient practice at hand.
authors.affiliation: TU München, Department of Mathematics, Munich, Germany; HelmholtzZentrum München, Institute of Computational Biology, Munich, Germany. Electronic address: a.strobl@tum.de.
authors.forename: Andreas N
authors.initials: AN
authors.lastname: Strobl
authors.affiliation: Memorial Sloan-Kettering Cancer Center, Department of Epidemiology and Biostatistics, New York City, NY, USA.
authors.forename: Andrew J
authors.initials: AJ
authors.lastname: Vickers
authors.affiliation: KU Leuven, Department of Development and Regeneration, Leuven, Belgium.
authors.forename: Ben
authors.initials: B
authors.lastname: Van Calster
authors.affiliation: Erasmus MC, Department of Public Health, Rotterdam, The Netherlands.
authors.forename: Ewout
authors.initials: E
authors.lastname: Steyerberg
authors.affiliation: University of Texas Health Science Center at San Antonio, Department of Cellular and Structural Biology, San Antonio, TX, USA; University of Texas Health Science Center at San Antonio, Department of Urology, San Antonio, TX, USA.
authors.forename: Robin J
authors.initials: RJ
authors.lastname: Leach
authors.affiliation: University of Texas Health Science Center at San Antonio, Department of Urology, San Antonio, TX, USA.
authors.forename: Ian M
authors.initials: IM
authors.lastname: Thompson
authors.affiliation: TU München, Department of Mathematics, Munich, Germany; HelmholtzZentrum München, Institute of Computational Biology, Munich, Germany; University of Texas Health Science Center at San Antonio, Department of Urology, San Antonio, TX, USA; University of Texas Health Science Center at San Antonio, Department of Epidemiology and Biostatistics, San Antonio, TX, USA.
authors.forename: Donna P
authors.initials: DP
authors.lastname: Ankerst
mesh_terms: D000328:Adult; D000368:Aged; D000369:Aged, 80 and over; D000465:Algorithms; D001317:Austria; D001499:Bayes Theorem; D001706:Biopsy; D002138:Calibration; D015331:Cohort Studies; D016208:Databases, Factual; D004739:England; D006801:Humans; D016015:Logistic Models; D008297:Male; D008875:Middle Aged; D017430:Prostate-Specific Antigen / Q000097:blood; D011471:Prostatic Neoplasms / Q000175:diagnosis* / Q000453:epidemiology; D015203:Reproducibility of Results; D018570:Risk Assessment / Q000379:methods*; D014481:United States
chemical_list: D017430:Prostate-Specific Antigen
keywords: Calibration; Discrimination; Logistic regression; Prediction; Prostate cancer; Revision