26 papers · ranked by Valyu relevance
Lutfiyya N. Muhammad
Introduction A study design with repeated measures data occurs when each experimental unit has multiple dependent variable observations collected at several time points. Often longitudinal data (1, 2) are used interchangeably to describe repeated measures data. This study design also includes two or more experimental…
Jessica I. Murphy, Nicholas E. Weaver, Audrey E. Hendricks
Longitudinal studies are commonly used to examine possible causal factors associated with human health and disease. However, the statistical models, such as two-way ANOVA, often applied in these studies do not appropriately model the experimental design, resulting in biased and imprecise results. Here, we describe the…
Jessica I. Murphy, Nicholas E. Weaver, Audrey E. Hendricks
Longitudinal mouse models are commonly used to study possible causal factors associated with human health and disease. However, the statistical models, such as two-way ANOVA, often applied in these studies do not appropriately model the experimental design, resulting in biased and imprecise results. Here, we describe…
Benjamin Säfken, David Rügamer, Thomas Kneib, Sonja Greven
Model selection in mixed models based on the conditional distribution is appropriate for many practical applications and has been a focus of recent statistical research. In this paper we introduce the R-package cAIC4 that allows for the computation of the conditional Akaike Information Criterion (cAIC). Computation of…
Bingkai Wang, Michael O. Harhay, Dylan S. Small, Tim P. Morris + 1 more
'Fan Li'] In the analyses of cluster-randomized trials, mixed-model analysis of covariance (ANCOVA) is a standard approach for covariate adjustment and handling withincluster correlations. However, when the normality, linearity, or the random-intercept assumption is violated, the validity and efficiency of the…
Samuel Müller, Janice L. Scealy, A. H. Welsh
Linear mixed effects models are highly flexible in handling a broad range of data types and are therefore widely used in applications. A key part in the analysis of data is model selection, which often aims to choose a parsimonious model with other desirable properties from a possibly very large set of candidate…
Eldin Dzubur, Aditya Ponnada, Rachel Nordgren, Chih-Hsiang Yang + 3 more
'Stephen Intille' 'Genevieve Dunton' 'Donald Hedeker'] The use of intensive sampling methods, such as ecological momentary assessment (EMA), is increasingly prominent in medical research. However, inferences from such data are often limited to the subject-specific mean of the outcome and between-subject variance (i.e.…
Keith R. Lohse, Allan J. Kozlowski, Michael Strube
Mixed-effect models are flexible tools for researchers in a myriad of fields, but that flexibility comes at the cost of complexity and if users are not careful in how their model is specified, they could be making faulty inferences from their data. We argue that there is significant confusion around appropriate random…
Johannes Oberpriller, Melina de Souza Leite, Maximilian Pichler
Biological data are often intrinsically hierarchical. Due to their ability to account for such dependencies, mixed-effects models have become a common analysis technique in ecology and evolution. While many questions around their theoretical foundations and practical applications are solved, one fundamental question is…
Ethan M. McCormick, Michelle L. Byrne, John C. Flournoy, Kathryn L. Mills + 1 more
Longitudinal data are becoming increasingly available in developmental neuroimaging. To maximize the promise of this wealth of information on how biology, behavior, and cognition change over time, there is a need to incorporate broad and rigorous training in longitudinal methods into the repertoire of developmental…
Dylan G.E. Gomes
As (generalized) linear mixed-effects models (GLMMs) have become a widespread tool in ecology, the need to guide the use of such tools is increasingly important. One common ‘rule of thumb’ is that one needs at least five levels of a random effect. Having such few levels makes the estimation of the variance of random…
Bernard G. Francq, Dan Lin, Walter Hoyer
The literature about Prediction Interval (PI) and Tolerance Interval (TI) in linear mixed models is usually developed for specific designs, which is a main limitation to their use. This paper proposes to reformulate the two-sided PI to be generalizable under a wide variety of designs (one random factor, nested and…
Sofie Pødenphant, Kasper Kristensen, Per B. Brockhoff
Multiplicative mixed models can be applied in a wide range of scientific disciplines, since they are relevant in every situation where an interaction between a fixed effect and a random effect is present. Until now, no R package has been published, which can fit this type of models. The lack of user-friendly open…
Hidetoshi Matsui
We consider constructing model selection criteria for evaluating nonlinear mixed effects models via basis expansions. Mean functions and random functions in the mixed effects model are expressed by basis expansions, then they are estimated by the maximum likelihood method. In order to select numbers of basis we derive…
George Leckie
Multilevel models (mixed-effect models or hierarchical linear models) are now a standard approach to analysing clustered and longitudinal data in the social, behavioural and medical sciences. This review article focuses on multilevel linear regression models for continuous responses (outcomes or dependent variables).…
Laurent Billot, Andrew Copas, Clemence Leyrat, Andrew Forbes + 1 more
In cluster randomized trials, individuals from the same cluster tend to have more similar outcomes than individuals from different clusters. This correlation must be taken into account in the analysis of every cluster trial to avoid incorrect inferences. In this paper, we describe the principles guiding the analysis of…
Conrad Hübler
A novel application to determine stability constants from supramolecular titration experiments is presented. The focus lies on NMR titration and ITC experiments for pure 1:1 systems, as well as mixed 2:1/1:1, 1:1/1:2 and 2:1/1:1/1:2 systems. SupraFit provides global and local fitting and a global search tool.…
Ewa Wieczorek, Joshua W. Sin, Matthew T. O. Holland, Liam Wilbraham + 5 more
Heterocycles are important scaffolds in medicinal chemistry that can be used to modulate the binding mode as well as pharmacokinetic properties of drugs. The importance of heterocycles has been exemplified by the publication of numerous datasets containing heterocyclic rings and their properties. However, those…
Ariel I. Mundo, John R. Tipton, Timothy J. Muldoon
In biomedical research, the outcome of longitudinal studies has been traditionally analyzed using the repeated measures analysis of variance (rm-ANOVA) or more recently, linear mixed models (LMEMs). Although LMEMs are less restrictive than rm-ANOVA in terms of correlation and missing observations, both methodologies…
Axel Mayer, Benjamin Nagengast, John Fletcher, Rolf Steyer
Conventionally, multilevel analysis of covariance (ML-ANCOVA) has been the recommended approach for analyzing treatment effects in quasi-experimental multilevel designs with treatment application at the cluster-level. In this paper, we introduce the generalized ML-ANCOVA with linear effect functions that identifies…
Ludwig A. Hothorn
In bio-medical studies, the p-values of the F-tests in ANOVA are usually interpreted independently as measures of the significance of the associated factors. This ’hidden multiplicity’ effect increases the false positive rate. Therefore, Cramer et al. (2016) proposed the Bonferroni adjustment of the p-values to control…
Thomas Lynn, Julio Ottino, Richard Lueptow, Paul Umbanhowar
Cut-and-shuffle mixing is an instructive candidate system with which to assess the potential of machine learning (ML) as an approach to solve difficult mixing problems. We focus on a specific subset of cut-and-shuffle systems, the one-dimensional interval exchange transform. This class of mixing operations is well…
Mirjam Moerbeek, Sander van Schie
Background The number of clusters in a cluster randomized trial is often low. It is therefore likely random assignment of clusters to treatment conditions results in covariate imbalance. There are no studies that quantify the consequences of covariate imbalance in cluster randomized trials on parameter and standard…
Authors not listed
We developed OpenStats, a user-friendly web application that brings the power of the R language to researchers through a high-level interface and broad support for statistical methods such as t-tests and ANOVA. OpenStats was integrated into our electronic lab notebook Chemotion ELN via its third-party API, enabling…
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This paper presents the Multi Cell-line Kinetic Model (MCKM), a novel generalised kinetic mechanistic model specifically tailored for Ambr15™ fed-batch cultivations of multiple Chinese Hamster Ovary (CHO) cell lines producing different recombinant monoclonal antibodies (mAbs). Unlike traditional models that requires…
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Experimental design plays an important role in efficiently acquiring informative data for system characterization and deriving robust conclusions under resource limitations. Recent advancements in high-throughput experimentation coupled with machine learning have notably improved experimental procedures. While Bayesian…