Asymptotic distribution of likelihood ratio test statistics for variance components in nonlinear mixed effects models

A standardised set of images for judgements of proportion

In the present work, we present normative data for a set of 39 original clipart-style images that can be used as material in studies involving judgements of proportion. The original images are drawings that depict different day-to-day scenarios (e.g., lighted windows in a building; books on a shelf) and each has seven variants of different proportions (from 20% to 80%) belonging to different categories (discrete vs continuous; social vs non-social; natural vs artificial; stimuli physical dimensions; number of referents). Normative data for these images are presented in an interactive database (available at https://judgment-images-and-norms.shinyapps.io/estimates_interactive/ ), corresponding to the means of proportion estimates (in percentage form), the perceived ease of making such estimates, the perceived level of familiarity and liking for each image, and the relationships between these variables. In the paper, we analyse the data at an individual level, addressing how the latter judgements are related to the proportion estimates, how those estimates are related to objective proportions, and how these relationships are moderated by image category. The analyses presented in this paper aim to aid readers in selecting images that enable them to better address specific influences on proportional estimates or to control for those influences in their studies.

Assessing exposure-time treatment effect heterogeneity in stepped-wedge cluster randomized trials

A stepped-wedge cluster randomized trial (CRT) is a unidirectional crossover study in which timings of treatment initiation for clusters are randomized. Because the timing of treatment initiation is different for each cluster, an emerging question is whether the treatment effect depends on the exposure time, namely, the time duration since the initiation of treatment. Existing approaches for assessing exposure-time treatment effect heterogeneity either assume a parametric functional form of exposure time or model the exposure time as a categorical variable, in which case the number of parameters increases with the number of exposure-time periods, leading to a potential loss in efficiency. In this article, we propose a new model formulation for assessing treatment effect heterogeneity over exposure time. Rather than a categorical term for each level of exposure time, the proposed model includes a random effect to represent varying treatment effects by exposure time. This allows for pooling information across exposure-time periods and may result in more precise average and exposure-time-specific treatment effect estimates. In addition, we develop an accompanying permutation test for the variance component of the heterogeneous treatment effect parameters. We conduct simulation studies to compare the proposed model and permutation test to alternative methods to elucidate their finite-sample operating characteristics, and to generate practical guidance on model choices for assessing exposure-time treatment effect heterogeneity in stepped-wedge CRTs.

Mixed-type multivariate response regression with covariance estimation

We propose a new method for multivariate response regression and covariance estimation when elements of the response vector are of mixed types, for example some continuous and some discrete. Our method is based on a model which assumes the observable mixed-type response vector is connected to a latent multivariate normal response linear regression through a link function. We explore the properties of this model and show its parameters are identifiable under reasonable conditions. We impose no parametric restrictions on the covariance of the latent normal other than positive definiteness, thereby avoiding assumptions about unobservable variables which can be difficult to verify in practice. To accommodate this generality, we propose a novel algorithm for approximate maximum likelihood estimation that works "off-the-shelf" with many different combinations of response types, and which scales well in the dimension of the response vector. Our method typically gives better predictions and parameter estimates than fitting separate models for the different response types and allows for approximate likelihood ratio testing of relevant hypotheses such as independence of responses. The usefulness of the proposed method is illustrated in simulations; and one biomedical and one genomic data example.

A minimal yet flexible likelihood framework to assess correlated evolution

An evolutionary process is reflected in the sequence of changes of any trait (e.g. morphological or molecular) through time. Yet, a better understanding of evolution would be procured by characterizing correlated evolution, or when two or more evolutionary processes interact. Previously developed parametric methods often require significant computing time as they rely on the estimation of many parameters. Here we propose a minimal likelihood framework modelling the joint evolution of two traits on a known phylogenetic tree. The type and strength of correlated evolution is characterized by a few parameters tuning mutation rates of each trait and interdependencies between these rates. The framework can be applied to study any discrete trait or character ranging from nucleotide substitution to gain or loss of a biological function. More specifically, it can be used to 1) test for independence between two evolutionary processes, 2) identify the type of interaction between them and 3) estimate parameter values of the most likely model of interaction. In the current implementation, the method takes as input a phylogenetic tree with discrete evolutionary events mapped on its branches. The method then maximizes the likelihood for one or several chosen scenarios. The strengths and limits of the method, as well as its relative power compared to a few other methods, are assessed using both simulations and data from 16S rRNA sequences in a sample of 54 γ-enterobacteria. We show that, even with datasets of fewer than 100 species, the method performs well in parameter estimation and in evolutionary model selection.

A functional mixed model for scalar on function regression with application to a functional MRI study

Motivated by a functional magnetic resonance imaging (fMRI) study, we propose a new functional mixed model for scalar on function regression. The model extends the standard scalar on function regression for repeated outcomes by incorporating subject-specific random functional effects. Using functional principal component analysis, the new model can be reformulated as a mixed effects model and thus easily fit. A test is also proposed to assess the existence of the subject-specific random functional effects. We evaluate the performance of the model and test via a simulation study, as well as on data from the motivating fMRI study of thermal pain. The data application indicates significant subject-specific effects of the human brain hemodynamics related to pain and provides insights on how the effects might differ across subjects.

The Association Between Inflammatory and Oxidative Stress Biomarkers and Plasma Metabolites in a Longitudinal Study of Healthy Male Welders

INTRODUCTION

Human metabolism and inflammation are closely related modulators of homeostasis and immunity. Metabolic profiling is a useful tool to understand the association between metabolism and inflammation at a systemic level.

OBJECTIVE

To investigate the longitudinal associations between the concentration of plasma metabolites and biomarkers related to inflammation and oxidative stress.

METHODS

We conducted a repeated cross-sectional analysis consisting of 8 short-term panels that included 88 healthy adult male welders in Massachusetts, USA. In each panel, we collected 1-6 repeated measurements of blood and urine. We used a human vascular injury panel assay and custom cytokine/chemokine assay to quantify inflammatory biomarker plasma levels, liquid chromatography-mass spectrometry to quantify the concentrations of 665 plasma metabolites, and a competitive enzyme-linked immunoassay to quantify urinary 8-OHdG and 8-isoprostane levels. We used linear mixed effects models to estimate the longitudinal association between each inflammatory and oxidative stress biomarker and each metabolite.

RESULTS

At a 5% FDR threshold, we detected ≥1metabolite association for 8 unique inflammatory and oxidative stress biomarkers: urinary 8-isoprostane, plasma C-reactive protein (CRP), serum amyloid A (SAA), intercellular adhesion molecule 1, circulating vascular cell adhesion molecule-1, interleukin 8 (IL-8), interleukin 10 (IL-10) and vascular endothelial growth factor. Specifically, 3 metabolites in the androgenic steroids pathway were negatively associated with SAA; 3 dihydrosphingomyelins metabolites were positively associated with 1 or more of CRP, SAA, IL-8 and IL-10; 4 metabolites in acyl choline metabolism pathways were negatively associated with IL-8; 7 lysophospholipid metabolites were negatively associated with 1 or more of CRP, SAA and IL-8; 4 sphingomyelins were positively associated with CRP and/or SAA; and 10 metabolites in the xanthine pathway were positively associated with urinary 8-isoprostane.

CONCLUSION

We found that metabolites in phospholipid groups had strong associations with multiple inflammatory biomarkers, especially CRP, SAA and IL-8. The mechanism of these associations warrants further investigation.

Therapeutic drug monitoring of anti-tumour necrosis factor-α agents in inflammatory bowel disease: Limits and improvements

AIMS

Since the publication of the American Gastroenterological Association's recommendations in 2017, there have been no significant changes in the biological monitoring recommendations in inflammatory bowel disease. Possible limitations are the lack of evidence to recommend proactive therapeutic drug monitoring (pTDM) over reactive TDM (rTDM), and the limited information about individualized dosing methods. This article aims to review the TDM strategy updates and the use of individualized dosing methods.

METHODS

For the analysis of the TDM strategies and individualized dosing method, a search was carried out in PubMed and Cochrane Central. In the TDM case, since August 2017.

RESULTS

A total of 263 publications were found, but only 7 related to proactive TDM. Five of these publications directly compared pTDM vs rTDM and 2 were randomized clinical trials. Six studies found benefits of pTDM and 1 found no differences. Regarding the individualized dosing method, 229 distinct results were found. Population pharmacokinetics was the most widely used method to develop individual dosage models and to analyse the influence of factors on drug concentrations (albumin concentration, weight, presence of anti-drug antibodies etc).

CONCLUSION

We have found no major changes in TDM strategies. There is a growing trend towards the use of pTDM because it has shown a longer duration of treatment response, lower rates of discontinuation and relapses. However, the available evidence is limited and of low quality. Despite the common use of population pharmacokinetic methods to analyse pharmacokinetic factors, they are not commonly used for personalized dosing.

An iterative algorithm for joint covariate and random effect selection in mixed effects models

We consider joint selection of fixed and random effects in general mixed-effects models. The interpretation of estimated mixed-effects models is challenging since changing the structure of one set of effects can lead to different choices of important covariates in the model. We propose a stepwise selection algorithm to perform simultaneous selection of the fixed and random effects. It is based on Bayesian Information criteria whose penalties are adapted to mixed-effects models. The proposed procedure performs model selection in both linear and nonlinear models. It should be used in the low-dimension setting where the number of ovariates and the number of random effects are moderate with respect to the total number of observations. The performance of the algorithm is assessed via a simulation study, which includes also a comparative study with alternatives when available in the literature. The use of the method is illustrated in the clinical study of an antibiotic agent kinetics.

Bayesian inference of natural selection from spatiotemporal phenotypic data

Spatiotemporal variations of natural selection may influence the evolution of various features of organisms such as local adaptation or specialisation. This article develops a method for inferring how selection varies between locations and between generations from phenotypic data. It is assumed that generations are non-overlapping and that individuals reproduce by selfing or asexually. A quantitative genetics model taking account of the effects of stabilising natural selection, the environment and mutation on phenotypic means and variances is developed. Explicit results on the evolution of populations are derived and used to develop a Bayesian inference method. The latter is applied to simulated data and to data from a wheat participatory plant breeding programme. It has some ability to infer evolutionary parameters, but estimates may be sensitive to prior distributions, for example when phenotypic time series are short and when environmental effects are large. In such cases, sensitivity to prior distributions may be reported or more data may be collected.