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A Primer on Inverse Probability of Treatment Weighting and Marginal Structural Models
Felix Thoemmes, Anthony D. Ong
Emerging Adulthood·2016·184 citations
Emerging adulthood researchers are often interested in the effects of developmental tasks. The majority of transitions that occur during the period of early/emerging adulthood are not randomized; therefore, their effects on developmental trajectories are subject to potential bias due to confounding. Traditionally, confounding has been addressed using regression adjustment; however, there are viable alternatives, such as propensity score matching and inverse probability of treatment weighting. Propensity scores are probabilities of selecting treatment given values on observed covariates. Inverse probability of treatment weights are also based on estimated probabilities of treatment selection and can be used to create so-called pseudo-populations in which confounders and treatment are unrelated to each other. In longitudinal models, such weighting can occur at multiple time points. This article provides a primer on these weighting methods and illustrates their application to studies of emerging adulthood. We provide annotated computer code for both SPSS and R, for both binary and continuous treatments.
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View paper →An Introduction to Linear Mixed-Effects Modeling in R
Violet A. Brown
Advances in Methods and Practices in Psychological Science·2021·350 citations
This Tutorial serves as both an approachable theoretical introduction to mixed-effects modeling and a practical introduction to how to implement mixed-effects models in R. The intended audience is researchers who have some basic statistical knowledge, but little or no experience implementing mixed-effects models in R using their own data. In an attempt to increase the accessibility of this Tutorial, I deliberately avoid using mathematical terminology beyond what a student would learn in a standard graduate-level statistics course, but I reference articles and textbooks that provide more detail for interested readers. This Tutorial includes snippets of R code throughout; the data and R script used to build the models described in the text are available via OSF at https://osf.io/v6qag/ , so readers can follow along if they wish. The goal of this practical introduction is to provide researchers with the tools they need to begin implementing mixed-effects models in their own research.
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View paper →Aging in Global Perspective: On the 50th Anniversary of the National Institute on Aging
Jinkook Lee
The Journals of Gerontology, Series B: Psychological Sciences and Social Sciences·2025
No abstract available
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View paper →Comparison of Statistical Models for Cross-over design
Yonggab Kim, Kamruzzaman Md, Yeni Lim, Oran Kwon, Taesung Park
2019 IEEE International Conference on Bioinformatics and Biomedicine (BIBM)·2019·1 citations
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