BACKGROUND Smoothing methods are widely used to analyze epidemiologic data, particularly in the area of environmental health where non-linear relationships are not uncommon. This study focused on three different smoothing methods in Cox models: penalized splines, restricted cubic splines and fractional polynomials. OBJECTIVES The aim of this study was to assess the effects of prognostic facto...

This work stems from the need for accurate estimation of the curvature function of an artery, that emerged within ANEURISK Project, a research program that aims at investigating the role of vascular morphology and hemodynamics on the pathogenesis of cerebral aneurysms. We develop here a regression technique that exploits free knot splines in a novel setting, to estimate 3-dimensional curves, an...

AIMS To illustrate the contribution of smoothing methods to modelling exposure-response data, Cox models with penalised splines were used to reanalyse lung cancer risk in a cohort of workers exposed to silica in California's diatomaceous earth industry. To encourage application of this approach, computer code is provided. METHODS Relying on graphic plots of hazard ratios as smooth functions o...

Generalized Additive Models (GAMs) have been popularized by the work of Hastie and Tibshirani (1990) and the availability of user friendly GAM software in Splus. However, whilst it is flexible and efficient, the GAM framework based on backfitting with linear smoothers presents some difficulties when it comes to model selection and inference. On the other hand, the mathematically elegant work of...

A technique is proposed for smoothing a broken line fit, with known break points, to observational data. It will be referred to as “broken line smoothing”. The smoothness term is defined by means of the angles formed by the consecutive segments of the broken line, and is given an adjustable weight. The roughness of the resulting broken line can then be controlled by appropriately tuning the wei...

Abstract: Two existing approaches to functional principal components analysis (FPCA) are due to Rice and Silverman (1991) and Silverman (1996), both based on maximizing variance but introducing penalization in different ways. In this article we propose an alternative approach to FPCA using penalized rank one approximation to the data matrix. Our contributions are four-fold: (1) by considering i...