In this software review, we provide a brief overview of four R functions to estimate nonlinear mixed-effects programs: nlme (linear and nonlinear mixed-effects model), nlmer (from the lme4 package, ...linear mixed-effects models using Eigen and S4), saemix (stochastic approximation expectation maximization), and brms (Bayesian regression models using Stan). We briefly describe the approaches used, provide a sample code, and highlight strengths and weaknesses of each.
Mixed-effects models are being used ever more frequently in the analysis of experimental data. However, in the lme4 package in R the standards for evaluating significance of fixed effects in these ...models (i.e., obtaining
p
-values) are somewhat vague. There are good reasons for this, but as researchers who are using these models are required in many cases to report
p
-values, some method for evaluating the significance of the model output is needed. This paper reports the results of simulations showing that the two most common methods for evaluating significance, using likelihood ratio tests and applying the
z
distribution to the Wald
t
values from the model output (
t
-as-
z
), are somewhat anti-conservative, especially for smaller sample sizes. Other methods for evaluating significance, including parametric bootstrapping and the Kenward-Roger and Satterthwaite approximations for degrees of freedom, were also evaluated. The results of these simulations suggest that Type 1 error rates are closest to .05 when models are fitted using REML and
p
-values are derived using the Kenward-Roger or Satterthwaite approximations, as these approximations both produced acceptable Type 1 error rates even for smaller samples.
•A one-compartment model with first-order absorption and linear elimination was used to characterize the pharmacokinetics of nirmatrelvir in COVID-19 patients.•The drug exposure (peak and trough ...concentrations) observed in this study was higher than that reported in previous literature.•Creatinine clearance was found to have a significant impact on the clearance of nirmatrelvir.•The validated PopPK model can be utilized to predict nirmatrelvir plasma concentration-time profiles and to guide dosing adjustments in patients with renal impairment.
To establish a population pharmacokinetics (PopPK) model of nirmatrelvir in Chinese COVID-19 patients and provide reference for refining the dosing strategy of nirmatrelvir in patients confirmed to be infected with SARS-CoV-2.
A total of 80 blood samples were obtained from 35 mild to moderate COVID-19 patients who were orally administered nirmatrelvir/ritonavir tablets. The PopPK model of nirmatrelvir was developed using a nonlinear mixed effects modelling approach. The stability and prediction of the final model were assessed through a combination of goodness-of-fit and bootstrap method. The exposure of nirmatrelvir across various clinical scenarios was simulated using Monte Carlo simulations.
The pharmacokinetics of nirmatrelvir was well characterised by a one-compartment model with first-order absorption, and with creatinine clearance (Ccr) as the significant covariate. Typical population parameter estimates of apparent clearance and distribution volume for a patient with a Ccr of 95.5 mL·min−1were 3.45 L·h−1 and 48.71 L, respectively. The bootstrap and visual predictive check procedures demonstrated satisfactory predictive performance and robustness of the final model.
The final model was capable of offering an early prediction of drug concentration ranges for different nirmatrelvir dosing regimens and optimise the dose regimen of nirmatrelvir in individuals with confirmed SARS-CoV-2 infection.
As any real-life data, data modeled by linear mixed-effects models often contain outliers or other contamination. Even little contamination can drive the classic estimates far away from what they ...would be without the contamination. At the same time, datasets that require mixed-effects modeling are often complex and large. This makes it difficult to spot contamination. Robust estimation methods aim to solve both problems: to provide estimates where contamination has only little influence and to detect and flag contamination. We introduce an R package, robustlmm, to robustly fit linear mixed-effects models. The package's functions and methods are designed to closely equal those offered by lme4, the R package that implements classic linear mixed-effects model estimation in R. The robust estimation method in robustlmm is based on the random effects contamination model and the central contamination model. Contamination can be detected at all levels of the data. The estimation method does not make any assumption on the data's grouping structure except that the model parameters are estimable. robustlmm supports hierarchical and non-hierarchical (e.g., crossed) grouping structures. The robustness of the estimates and their asymptotic efficiency is fully controlled through the function interface. Individual parts (e.g., fixed effects and variance components) can be tuned independently. In this tutorial, we show how to fit robust linear mixed-effects models using robustlmm, how to assess the model fit, how to detect outliers, and how to compare different fits.
Second language acquisition researchers often face particular challenges when attempting to generalize study findings to the wider learner population. For example, language learners constitute a ...heterogeneous group, and it is not always clear how a study's findings may generalize to other individuals who may differ in terms of language background and proficiency, among many other factors. In this paper, we provide an overview of how mixed‐effects models can be used to help overcome these and other issues in the field of second language acquisition. We provide an overview of the benefits of mixed‐effects models and a practical example of how mixed‐effects analyses can be conducted. Mixed‐effects models provide second language researchers with a powerful statistical tool in the analysis of a variety of different types of data.
The anova to mixed model transition Boisgontier, Matthieu P.; Cheval, Boris
Neuroscience and biobehavioral reviews,
September 2016, 2016-Sep, 2016-09-00, 20160901, Volume:
68
Journal Article
Peer reviewed
A transition towards mixed models is underway in science. This transition started up because the requirements for using analyses of variances are often not met and mixed models clearly provide a ...better framework. Neuroscientists have been slower than others in changing their statistical habits and are now urged to act.
Ecological restoration aims at recovering biodiversity in degraded ecosystems, and it is commonly assessed via species richness. However, it is unclear whether increasing species richness in a site ...also recovers its functional diversity (FD), which has been shown to be a better representation of ecosystem functioning. We conducted a quantitative synthesis of 30 restoration projects and tested whether restoration improves FD. We compared actively and passively restored sites with degraded and reference sites with respect to four key measures of FD (functional richness, evenness, dispersion, and turnover) and two measures of species diversity (richness and evenness). We separately analyzed longitudinal studies (which monitor degraded, reference, and restored sites through time) and space‐for‐time substitutions (which compare at one point in time degraded and reference sites with restored sites of different ages). Space‐for‐time studies suggested that species diversity and FD improved over time. However, replicated longitudinal data showed no sustained benefits of active or passive restoration for FD measures, relative to degraded sites. This could suggest that the positive results in space‐for‐time designs may have been unreliable, but the relatively short duration of longitudinal studies suggests a need for longer‐term longitudinal research to robustly demonstrate the absence of any effect. These differences across study designs may explain the variable results found in recent studies directly measuring the response of FD to restoration. We recommend that future assessments of ecological community dynamics include control sites in monitoring, to ensure that the consequences of treatments, including but not limited to restoration, are correctly partitioned from unassisted temporal changes.
Aphids represent a significant challenge to food production. The Rothamsted Insect Survey (RIS) runs a network of 12·2‐m suction‐traps throughout the year to collect migrating aphids. In 2014, the ...RIS celebrated its 50th anniversary. This paper marks that achievement with an extensive spatiotemporal analysis and the provision of the first British annotated checklist of aphids since 1964. Our main aim was to elucidate mechanisms that advance aphid phenology under climate change and explain these using life‐history traits. We then highlight emerging pests using accumulation patterns. Linear and nonlinear mixed‐effect models estimated the average rate of change per annum and effects of climate on annual counts, first and last flights and length of flight season since 1965. Two climate drivers were used: the accumulated day degrees above 16 °C (ADD16) indicated the potential for migration during the aphid season; the North Atlantic Oscillation (NAO) signalled the severity of the winter before migration took place. All 55 species studied had earlier first flight trends at rate of β = −0·611 ± SE 0·015 days year⁻¹. Of these species, 49% had earlier last flights, but the average species effect appeared relatively stationary (β = −0·010 ± SE 0·022 days year⁻¹). Most species (85%) showed increasing duration of their flight season (β = 0·336 ± SE 0·026 days year⁻¹), even though only 54% increased their log annual count (β = 0·002 ± SE <0·001 year⁻¹). The ADD16 and NAO were shown to drive patterns in aphid phenology in a spatiotemporal context. Early in the year when the first aphids were migrating, the effect of the winter NAO was highly significant. Further into the year, ADD16 was a strong predictor. Latitude had a near linear effect on first flights, whereas longitude produced a generally less‐clear effect on all responses. Aphids that are anholocyclic (permanently parthenogenetic) or are monoecious (non‐host‐alternating) were advancing their phenology faster than those that were not. Climate drives phenology and traits help explain how this takes place biologically. Phenology and trait ecology are critical to understanding the threat posed by emerging pests such as Myzus persicae nicotianae and Aphis fabae cirsiiacanthoidis, as revealed by the species accumulation analysis.
Introduction
Cognitive resilience (CR) has been defined as the continuum of better (or worse) than expected cognition, given the degree of neuropathology. To quantify this concept, existing ...approaches focus on either cognitive level at a single time point or slopes of cognitive decline.
Methods
In a prospective study of 1215 participants, we created a continuous measure of CR defined as the mean of differences between estimated person‐specific and marginal cognitive levels over time, after accounting for neuropathologies.
Results
Neuroticism and depressive symptoms were associated with all CR measures (P‐values < .012); as expected, cognitive activity and education were only associated with the cognitive‐level approaches (P‐values < .0002). However, compared with the existing CR measures focusing on a single measure or slopes of cognition, our new measure yielded stronger relations with risk factors.
Discussion
Defining CR based on the longitudinal differences between person‐specific and marginal cognitive levels is a novel and complementary way to quantify CR.
PDF
