title: Keeping data continuous when analyzing the prognostic impact of a tumor marker: an example with cathepsin D in breast cancer.
abstract: The prognostic value of cathepsin D has been recently recognized, but as many quantitative tumor markers, its clinical use remains unclear partly because of methodological issues in defining cut-off values. Guidelines have been proposed for analyzing quantitative prognostic factors, underlining the need for keeping data continuous, instead of categorizing them. Flexible approaches, parametric and non-parametric, have been proposed in order to improve the knowledge of the functional form relating a continuous factor to the risk. We studied the prognostic value of cathepsin D in a retrospective hospital cohort of 771 patients with breast cancer, and focused our overall survival analysis, based on the Cox regression, on two flexible approaches: smoothing splines and fractional polynomials. We also determined a cut-off value from the maximum likelihood estimate of a threshold model. These different approaches complemented each other for (1) identifying the functional form relating cathepsin D to the risk, and obtaining a cut-off value and (2) optimizing the adjustment for complex covariate like age at diagnosis in the final multivariate Cox model. We found a significant increase in the death rate, reaching 70% with a doubling of the level of cathepsin D, after the threshold of 37.5 pmol mg(-1). The proper prognostic impact of this marker could be confirmed and a methodology providing appropriate ways to use markers in clinical practice was proposed.
authors.affiliation: Service de Biostatistique des Hospices Civils de Lyon, Université Claude Bernard, Centre Hospitalier Lyon Sud, Pierre-Benite, France. nadine.bossard@chu-lyon.fr
authors.forename: N
authors.initials: N
authors.lastname: Bossard
authors.forename: F
authors.initials: F
authors.lastname: Descotes
authors.forename: A G
authors.initials: AG
authors.lastname: Bremond
authors.forename: Y
authors.initials: Y
authors.lastname: Bobin
authors.forename: P
authors.initials: P
authors.lastname: De Saint Hilaire
authors.forename: F
authors.initials: F
authors.lastname: Golfier
authors.forename: A
authors.initials: A
authors.lastname: Awada
authors.forename: P M
authors.initials: PM
authors.lastname: Mathevet
authors.forename: L
authors.initials: L
authors.lastname: Berrerd
authors.forename: Y
authors.initials: Y
authors.lastname: Barbier
authors.forename: J
authors.initials: J
authors.lastname: Estève
mesh_terms: D000368:Aged; D014408:Biomarkers, Tumor / Q000032:analysis*; D001943:Breast Neoplasms / Q000378:metabolism* / Q000401:mortality* / Q000473:pathology; D002402:Cathepsin D / Q000032:analysis*; D015331:Cohort Studies; D005260:Female; D006801:Humans; D008207:Lymphatic Metastasis; D008875:Middle Aged; D015999:Multivariate Analysis; D011379:Prognosis; D011960:Receptors, Estrogen / Q000032:analysis; D011980:Receptors, Progesterone / Q000032:analysis; D012189:Retrospective Studies; D016019:Survival Analysis
chemical_list: D014408:Biomarkers, Tumor; D011960:Receptors, Estrogen; D011980:Receptors, Progesterone; D002402:Cathepsin D