When fixed and random effects mismatch: Implications for model comparisons

Mixed-effects models containing both fixed and random effects have become widely used in the cognitive sciences, as they are particularly appropriate for the analysis of clustered data. However, testing hypotheses in the presence of random effects is not completely straightforward, and a set of best practices for statistical inference in mixed-effects models is still lacking. Van Doorn et al. (2021) investigated how Bayesian hypothesis testing in mixed-effects models is impacted by particular model specifications. Here, we extend their work to the more complex case of models with three-level factorial predictors and, more generally, with multiple correlated predictors. We show how non-maximal models with correlated predictors contain 'mismatches' between fixed and random effects, in which the same predictor can refer to different effects in the fixed and random parts of a model. We then demonstrate though a series of Bayesian model comparisons that such mismatches can lead to inaccurate estimations of random variance, and in turn to biases in the assessment of evidence for the effect of interest. We present specific recommendations for how researchers can resolve mismatches or avoid them altogether: by fitting maximal models, eliminating correlations between predictors, or by residualising the random effects. Our results reinforce the observation that model comparisons with mixed-effects models can be surprisingly intricate and highlight that researchers should carefully and explicitly consider which hypotheses are being tested by each model comparison. Data and code are publicly available in an OSF repository at https://osf.io/njaup.

Minimal and Maximal Models to Quantitate Glucose Metabolism: Tools to Measure, to Simulate and to Run in Silico Clinical Trials

Several models have been proposed to describe the glucose system at whole-body, organ/tissue and cellular level, designed to measure non-accessible parameters (minimal models), to simulate system behavior and run in silico clinical trials (maximal models). Here, we will review the authors’ work, by putting it into a concise historical background. We will discuss first the parametric portrait provided by the oral minimal models—building on the classical intravenous glucose tolerance test minimal models—to measure otherwise non-accessible key parameters like insulin sensitivity and beta-cell responsivity from a physiological oral test, the mixed meal or the oral glucose tolerance tests, and what can be gained by adding a tracer to the oral glucose dose. These models were used in various pathophysiological studies, which we will briefly review. A deeper understanding of insulin sensitivity can be gained by measuring insulin action in the skeletal muscle. This requires the use of isotopic tracers: both the classical multiple-tracer dilution and the positron emission tomography techniques are discussed, which quantitate the effect of insulin on the individual steps of glucose metabolism, that is, bidirectional transport plasma-interstitium, and phosphorylation. Finally, we will present a cellular model of insulin secretion that, using a multiscale modeling approach, highlights the relations between minimal model indices and subcellular secretory events. In terms of maximal models, we will move from a parametric to a flux portrait of the system by discussing the triple tracer meal protocol implemented with the tracer-to-tracee clamp technique. This allows to arrive at quasi-model independent measurement of glucose rate of appearance (Ra), endogenous glucose production (EGP), and glucose rate of disappearance (Rd). Both the fast absorbing simple carbs and the slow absorbing complex carbs are discussed. This rich data base has allowed us to build the UVA/Padova Type 1 diabetes and the Padova Type 2 diabetes large scale simulators. In particular, the UVA/Padova Type 1 simulator proved to be a very useful tool to safely and effectively test in silico closed-loop control algorithms for an artificial pancreas (AP). This was the first and unique simulator of the glucose system accepted by the U.S. Food and Drug Administration as a substitute to animal trials for in silico testing AP algorithms. Recent uses of the simulator have looked at glucose sensors for non-adjunctive use and new insulin molecules.

Random effects structure for confirmatory hypothesis testing: Keep it maximal

Linear mixed-effects models (LMEMs) have become increasingly prominent in psycholinguistics and related areas. However, many researchers do not seem to appreciate how random effects structures affect the generalizability of an analysis. Here, we argue that researchers using LMEMs for confirmatory hypothesis testing should minimally adhere to the standards that have been in place for many decades. Through theoretical arguments and Monte Carlo simulation, we show that LMEMs generalize best when they include the maximal random effects structure justified by the design. The generalization performance of LMEMs including data-driven random effects structures strongly depends upon modeling criteria and sample size, yielding reasonable results on moderately-sized samples when conservative criteria are used, but with little or no power advantage over maximal models. Finally, random-intercepts-only LMEMs used on within-subjects and/or within-items data from populations where subjects and/or items vary in their sensitivity to experimental manipulations always generalize worse than separate F1 and F2 tests, and in many cases, even worse than F1 alone. Maximal LMEMs should be the ‘gold standard’ for confirmatory hypothesis testing in psycholinguistics and beyond.