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Non-linear transformations of age at diagnosis, tumor size, and number of positive lymph nodes in prediction of clinical outcome in breast cancer
Lund Univ, Sweden.
Odense Univ Hosp, Denmark.
Helsingborg Hosp, Sweden.
Lund Univ, Sweden.
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2018 (English)In: BMC Cancer, ISSN 1471-2407, E-ISSN 1471-2407, Vol. 18, article id 1226Article in journal (Refereed) Published
Abstract [en]

BackgroundPrognostic factors in breast cancer are often measured on a continuous scale, but categorized for clinical decision-making. The primary aim of this study was to evaluate if accounting for continuous non-linear effects of the three factors age at diagnosis, tumor size, and number of positive lymph nodes improves prognostication. These factors will most likely be included in the management of breast cancer patients also in the future, after an expected implementation of gene expression profiling for adjuvant treatment decision-making.MethodsFour thousand four hundred forty seven and 1132 women with primary breast cancer constituted the derivation and validation set, respectively. Potential non-linear effects on the log hazard of distant recurrences of the three factors were evaluated during 10years of follow-up. Cox-models of successively increasing complexity: dichotomized predictors, predictors categorized into three or four groups, and predictors transformed using fractional polynomials (FPs) or restricted cubic splines (RCS), were used. Predictive performance was evaluated by Harrells C-index.ResultsUsing FP-transformations, non-linear effects were detected for tumor size and number of positive lymph nodes in univariable analyses. For age, non-linear transformations did, however, not improve the model fit significantly compared to the linear identity transformation. As expected, the C-index increased with increasing model complexity for multivariable models including the three factors. By allowing more than one cut-point per factor, the C-index increased from 0.628 to 0.674. The additional gain, as measured by the C-index, when using FP- or RCS-transformations was modest (0.695 and 0.696, respectively). The corresponding C-indices for these four models in the validation set, based on the same transformations and parameter estimates from the derivation set, were 0.675, 0.700, 0.706, and 0.701.ConclusionsCategorization of each factor into three to four groups was found to improve prognostication compared to dichotomization. The additional gain by allowing continuous non-linear effects modeled by FPs or RCS was modest. However, the continuous nature of these transformations has the advantage of making it possible to form risk groups of any size.

Place, publisher, year, edition, pages
BMC , 2018. Vol. 18, article id 1226
Keywords [en]
Breast cancer; Prognostic; Categorical; Continuous; Fractional polynomials; Splines
National Category
Cancer and Oncology
Identifiers
URN: urn:nbn:se:liu:diva-153676DOI: 10.1186/s12885-018-5123-xISI: 000452726200002PubMedID: 30526533OAI: oai:DiVA.org:liu-153676DiVA, id: diva2:1276236
Note

Funding Agencies|Swedish Cancer Society; Swedish Research Council; Gunnar Nilsson Cancer Foundation; Swedish Breast Cancer Association; Swedish Cancer and Allergy Foundation; Mrs. Berta Kamprad Foundation; Anna and Edwin Bergers Foundation; Skane County Councils Research and Development Foundation; Governmental Funding of Clinical Research within the National Health Service

Available from: 2019-01-07 Created: 2019-01-07 Last updated: 2019-03-26

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