Modeling individual and relative accuracy of screening tools in geriatric oncology.
BACKGROUND
Classification probabilities reflect to what degree a screening test represents the true disease state and include true positive (TPF) and false positive fractions (FPF). With two tests, one can compare TPF and FPF using relative probabilities which offer advantages in terms of interpretation and statistical modeling. Our objective was to highlight how individual and relative TPF and FPF can be easily estimated and compared within a regression modeling framework. This allows the modeling of tests' accuracy while adjusting for multiple covariates, and thus provides valuable information in addition to the crude TPF and FPF. We illustrate our purpose with the G8 and VES-13 screening tests aimed at identifying elderly cancer patients in need for a comprehensive geriatric assessment (CGA).


METHODS
Prospective cohort with a paired design. TPF and FPF of each test, as well as relative TPF and FPF were modeled using log-linear models.


RESULTS
G8 detected patients in need for CGA better than VES-13 at the expense of misclassifying a large number of normal patients. Both tests had better TPF with older age and poorer performance status (PS), and for all cancer subtypes compared with prostate cancer. Effect of age and PS on TPF was more pronounced with VES-13. Age affected FPF, but not differentially.


CONCLUSIONS
Regression modeling helps provide a thorough assessment of the accuracy of diagnostic tests and should be used more frequently. In the context of screening, we encourage the use of G8 as failing to identify patients in need of a CGA might be more problematic than over-detection. Moreover, although we identified variables associated with the sensitivity of these tests, this association was less pronounced for the G8.
Clinical Research and Clinical Epidemiology Unit, Department of Clinical Research and Medical Information, Institut Bergonié, Comprehensive Cancer Centre, Bordeaux.|Clinical Epidemiology Unit, INSERM CIC 14.01, Bordeaux.|Team EPICENE, University of Bordeaux, INSERM, Bordeaux Population Health Research Center, UMR 1219, F-33000 Bordeaux.
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Bellera
Clinical Research and Clinical Epidemiology Unit, Department of Clinical Research and Medical Information, Institut Bergonié, Comprehensive Cancer Centre, Bordeaux.|Clinical Epidemiology Unit, INSERM CIC 14.01, Bordeaux.
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Artaud
Gerontology Service, Centre Hospitalier Universitaire, Bordeaux.|University of Bordeaux, Bordeaux.
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Rainfray
University of Bordeaux, Bordeaux.|Department of Medical Oncology, Institut Bergonié, Comprehensive Cancer Centre, Bordeaux, France.
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Soubeyran
Clinical Research and Clinical Epidemiology Unit, Department of Clinical Research and Medical Information, Institut Bergonié, Comprehensive Cancer Centre, Bordeaux.|Clinical Epidemiology Unit, INSERM CIC 14.01, Bordeaux.|Team EPICENE, University of Bordeaux, INSERM, Bordeaux Population Health Research Center, UMR 1219, F-33000 Bordeaux.
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Mathoulin-Pélissier
D000368:Aged; D000369:Aged, 80 and over; D015577:Geriatric Assessment / Q000379:methods*; D005853:Geriatrics / Q000379:methods*; D006801:Humans; D008297:Male; D008495:Medical Oncology / Q000379:methods*; D011471:Prostatic Neoplasms / Q000453:epidemiology* / Q000473:pathology; D011795:Surveys and Questionnaires
classification probabilities; diagnostic accuracy; geriatric oncology; log–linear model; neoplasms; screening