Negative variance components and intercept‐slope correlations greater than one in magnitude: How do such “non‐regular” random intercept and slope models arise, and what should be done when they do?

Abstract

Statistical models with random intercepts and slopes (RIAS models) are commonly used to analyze longitudinal data. Fitting such models sometimes results in negative estimates of variance components or estimates on parameter space boundaries. This can be an unlucky chance occurrence, but can also occur because certain marginal distributions are mathematically identical to those from RIAS models with negative intercept and/or slope variance components and/or intercept‐slope correlations greater than one in magnitude. We term such parameters “pseudo‐variances” and “pseudo‐correlations,” and the models “non‐regular.” We use eigenvalue theory to explore how and when such non‐regular RIAS models arise, showing: (i) A small number of measurements, short follow‐up, and large residual variance increase the parameter space for which data (with a positive semidefinite marginal variance‐covariance matrix) are compatible with non‐regular RIAS models. (ii) Non‐regular RIAS models can arise from model misspecification, when non‐linearity in fixed effects is ignored or when random effects are omitted. (iii) A non‐regular RIAS model can sometimes be interpreted as a regular linear mixed model with one or more additional random effects, which may not be identifiable from the data. (iv) Particular parameterizations of non‐regular RIAS models have no generality for all possible numbers of measurements over time. Because of this lack of generality, we conclude that non‐regular RIAS models can only be regarded as plausible data‐generating mechanisms in some situations. Nevertheless, fitting a non‐regular RIAS model can be acceptable, allowing unbiased inference on fixed effects where commonly recommended alternatives such as dropping the random slope result in bias.