Analysis of exposure-time-response relationships using a spline weight function
Related Experiment Video
Updated: Oct 10, 2026

A Method of Trigonometric Modelling of Seasonal Variation Demonstrated with Multiple Sclerosis Relapse Data
Published on: December 9, 2015
M Hauptmann1, J Wellmann, J H Lubin
1GSF National Research Center for Environment and Health, Institute of Epidemiology, Ingolstädter Landstrasse 1, 85764 Neuherberg, Germany. hauptmann@nih.gov
Abstract:
To examine the time-dependent effects of exposure histories on disease, we estimate a weight function within a generalized linear model. The shape of the weight function, which is modeled as a cubic B-spline, gives information about the impact of exposure increments at different times on disease risk. The method is evaluated in a simulation study and is applied to data on smoking histories and lung cancer from a recent case-control study in Germany.
Related Concept Videos
Parametric Survival Analysis: Weibull and Exponential Methods
Weibull Distribution
The Weibull distribution is a flexible model used in parametric survival analysis. It can handle both increasing and decreasing hazard rates, depending on its shape parameter...
Dose-Response Relationship: Overview
Methods of Medium Optimization
Response Surface Methodology
The process of RSM involves several key steps:
Drug Concentration Versus Time Correlation
Two pivotal parameters are the minimum effective concentration (MEC) and the minimum toxic concentration (MTC). The MEC is the lowest drug...
Pharmacokinetic–Pharmacodynamic Relationship: Exposure, Response and Effect

