Assessing the impact of variance heterogeneity and misspecification in mixed-effects location-scale models
Vincent Jeanselme, Marco Palma, Jessica Barrett
TL;DR
This paper investigates how ignoring changing variability in data affects statistical models and shows that using mixed-effect location-scale models can improve accuracy.
Contribution
The study introduces a simulation framework to assess the impact of variance misspecification in mixed-effects models.
Findings
Ignoring heteroscedasticity in LMMs causes biased standard deviation estimates and reduced coverage.
MELSMs are robust to scale misspecification but sensitive to location misspecification.
Properly modeling variability is crucial for accurate inference in mixed-effect models.
Abstract
Linear Mixed Model (LMM) is a common statistical approach to model the relation between exposure and outcome while capturing individual variability through random effects. However, this model assumes the homogeneity of the error term’s variance. Breaking this assumption, known as homoscedasticity, can bias estimates and, consequently, may change a study’s conclusions. If this assumption is unmet, the mixed-effect location-scale model (MELSM) offers a solution to account for within-individual variability. Our work explores how LMMs and MELSMs behave when the homoscedasticity assumption is not met. Further, we study how misspecification affects inference for MELSM. To this aim, we propose a simulation study with longitudinal data and evaluate the estimates’ bias and coverage. Our simulations show that neglecting heteroscedasticity in LMMs leads to loss of coverage for the estimated…
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Taxonomy
TopicsStatistical Methods and Inference · Statistical Methods and Bayesian Inference · Advanced Causal Inference Techniques