- Library Home /
- Search Collections /
- Open Collections /
- Browse Collections /
- BIRS Workshop Lecture Videos /
- Optimal designs for longitudinal studies with fractional...
Open Collections
BIRS Workshop Lecture Videos
BIRS Workshop Lecture Videos
Optimal designs for longitudinal studies with fractional polynomial models Lopez-Fidalgo, Jesus
Description
Fractional polynomials (FP) have been shown to be much more flexible than polynomials for fitting continuous outcomes in the biological and health sciences. Despite their increasing popularity, design issues for FP models have never been addressed. D- and I-optimal experimental designs will be computed for prediction using FP models. Their properties will be evaluated and a catalogue of design points useful for FP models will be provided. As applications, we consider linear mixed effects models for longitudinal studies. To provide greater flexibility in modeling the shape of the response, we use fractional polynomials and not polynomials to approximate the mean response. An example using gene expression data will be considered comparing the designs used in practice. An additional an interesting problem is finding designs for effective model discrimination for FP models. This will be explored from the KL-optimality point of view.
Item Metadata
Title |
Optimal designs for longitudinal studies with fractional polynomial models
|
Creator | |
Publisher |
Banff International Research Station for Mathematical Innovation and Discovery
|
Date Issued |
2017-08-10T14:19
|
Description |
Fractional polynomials (FP) have been shown to be much more flexible than polynomials for fitting continuous outcomes in the biological and health sciences. Despite their increasing popularity, design issues for FP models have never been addressed. D- and I-optimal experimental designs will be computed for prediction using FP models. Their properties will be evaluated and a catalogue of design points useful for FP models will be provided. As applications, we consider linear mixed effects models for longitudinal studies. To provide greater flexibility in modeling the shape of the response, we use fractional polynomials and not polynomials to approximate the mean response. An example using gene expression data will be considered comparing the designs used in practice. An additional an interesting problem is finding designs for effective model discrimination for FP models. This will be explored from the KL-optimality point of view.
|
Extent |
42 minutes
|
Subject | |
Type | |
File Format |
video/mp4
|
Language |
eng
|
Notes |
Author affiliation: University of Navarre
|
Series | |
Date Available |
2018-02-07
|
Provider |
Vancouver : University of British Columbia Library
|
Rights |
Attribution-NonCommercial-NoDerivatives 4.0 International
|
DOI |
10.14288/1.0363436
|
URI | |
Affiliation | |
Peer Review Status |
Unreviewed
|
Scholarly Level |
Faculty
|
Rights URI | |
Aggregated Source Repository |
DSpace
|
Item Media
Item Citations and Data
Rights
Attribution-NonCommercial-NoDerivatives 4.0 International