Abstract
Multiple regression analyses have found widespread use in melanoma research. Such methods are natural candidates for prognostic factor studies, where it is desired to examine the joint influence of various clinical, pathologic, and demographic variables on indicators of disease. For example, in a recent article, Schuchter et al. (1996) consider a four-variable logistic regression model to study association with a binary indicator of survival at 10 years. A more complicated analysis using a Cox proportional hazards model with smooth (nonparametric) functions of prognostic risk factors was considered by Helfenstein et al. (1996) to study survival times over a 5-year period. The regression framework is also attractive because predicted disease probabilities for specific risk profiles can be obtained using simple arithmetic calculations (Harrell et al., 1985). Further, computer programs to fit these models are commercially available
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Natarajan, R., Weinstock, M.A. (2000). Prognostic Models in Melanoma. In: Schwindt, D.A., Maibach, H.I. (eds) Cutaneous Biometrics. Springer, Boston, MA. https://doi.org/10.1007/978-1-4615-1199-1_14
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DOI: https://doi.org/10.1007/978-1-4615-1199-1_14
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