ORCID: https://orcid.org/0000-0003-2195-8130
(2018):
The impact of model assumptions in scalar‐on‐image regression.
In: Statistics in Medicine, Vol. 37, No. 28: pp. 4298-4317
Abstract
Complex statistical models such as scalar‐on‐image regression often require strong assumptions to overcome the issue of nonidentifiability. While in theory, it is well understood that model assumptions can strongly influence the results, this seems to be underappreciated, or played down, in practice. This article gives a systematic overview of the main approaches for scalar‐on‐image regression with a special focus on their assumptions. We categorize the assumptions and develop measures to quantify the degree to which they are met. The impact of model assumptions and the practical usage of the proposed measures are illustrated in a simulation study and in an application to neuroimaging data. The results show that different assumptions indeed lead to quite different estimates with similar predictive ability, raising the question of their interpretability. We give recommendations for making modeling and interpretation decisions in practice based on the new measures and simulations using hypothetic coefficient images and the observed data.
Item Type: | Journal article |
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Faculties: | Mathematics, Computer Science and Statistics > Statistics Mathematics, Computer Science and Statistics > Statistics > Chairs/Working Groups > Bioimaging Mathematics, Computer Science and Statistics > Statistics > Chairs/Working Groups > Biostatistics |
Subjects: | 500 Science > 510 Mathematics |
ISSN: | 1097-0258 |
Language: | English |
Item ID: | 59064 |
Date Deposited: | 16. Nov 2018, 10:13 |
Last Modified: | 04. Nov 2020, 13:38 |