Abstract
Recent developments in the collection and modeling of intensive longitudinal data have enabled us to fit dynamic twin models, in which within-person processes are separated into genetic and environmental components. A well-known dynamic twin model is the genetic simplex model, which is fitted to a few repeated measures for many twins. A more recently developed model is the iFACE model, which is fitted to many repeated measures for a single pair of twins. In this paper, we introduce a missing link between these two models–a multilevel extension that allows for making both population-level and twin-level inferences. We provide a proof-of-principle simulation study for this model, and apply it to an experience sampling data set on 148 monozygotic and 88 dizygotic twins. We use the multilevel model to examine the overlap and differences between the dynamic genetic twin models and the classic twin models, as well as their interpretation.
| Original language | English |
|---|---|
| Pages (from-to) | 101-121 |
| Number of pages | 21 |
| Journal | Structural Equation Modeling |
| Volume | 29 |
| Issue number | 1 |
| Early online date | 6 Jul 2021 |
| DOIs | |
| Publication status | Published - 2022 |
Bibliographical note
Funding Information:This work was partly supported by the Natural Sciences and Engineering Research Council of Canada (Discovery Grant RGPIN-2020-04458 and Discovery Launch Supplement DGECR-2020-00077 to Yao Zheng). The authors are grateful to Marieke Wichers, Nele Jacobs, Jim van Os, Catherine Derom, and Evert Thiery for sharing the data for the empirical example. The authors are grateful to Maria Bolsinova, Ellen Hamaker and Karianne Schuurman for insightful conversations on this work.
Publisher Copyright:
© 2021 The Author(s). Published with license by Taylor & Francis Group, LLC.
Keywords
- dynamic multilevel modeling
- Genetic simplex model
- genetics
- iFACE model
- intensive longitudinal data