Item response theory analysis of daytime sleepiness as a symptom of obstructive sleep apnea.

Obstructive sleep apnea (OSA) is a sleep disorder which is linked to many health risks. The gold standard to evaluate OSA in clinical trials is the Apnea-Hypopnea Index (AHI). However, it is time-consuming, costly, and disregards aspects such as quality of life.

Evaluation of covariate effects in item response theory models.

Item response theory (IRT) models are usually the best way to analyze composite or rating scale data. Standard methods to evaluate covariate or treatment effects in IRT models do not allow to identify item-specific effects. Finding subgroups of patients who respond differently to certain items could be very important when designing inclusion or exclusion criteria for clinical trials, and aid in understanding different treatment responses in varying disease manifestations.

Imeglimin population pharmacokinetics and dose adjustment predictions for renal impairment in Japanese and Western patients with type 2 diabetes.

Imeglimin is an orally administered first-in-class drug to treat type 2 diabetes mellitus (T2DM) and is mainly excreted unchanged by the kidneys. The present study aimed to define the pharmacokinetic (PK) characteristics of imeglimin using population PK analysis and to determine the optimal dosing regimen for Japanese patients with T2DM and chronic kidney disease (CKD). Imeglimin plasma concentrations in Japanese and Western healthy volunteers, and patients with T2DM, including patients with mild to severe CKD with an estimated glomerular filtration rate (eGFR) greater than 14 ml/min/1.

An Item Response Theory-Informed Strategy to Model Total Score Data from Composite Scales.

Composite scale data is widely used in many therapeutic areas and consists of several categorical questions/items that are usually summarized into a total score (TS). Such data is discrete and bounded by nature. The gold standard to analyse composite scale data is item response theory (IRT) models.

Comparison of Precision and Accuracy of Five Methods to Analyse Total Score Data.

Total score (TS) data is generated from composite scales consisting of several questions/items, such as the Movement Disorder Society-Unified Parkinson's Disease Rating Scale (MDS-UPDRS). The analysis method that most fully uses the information gathered is item response theory (IRT) models, but these are complex and require item-level data which may not be available. Therefore, the TS is commonly analysed with standard continuous variable (CV) models, which do not respect the bounded nature of data.

Dose-Response Mixed Models for Repeated Measures - a New Method for Assessment of Dose-Response.

Purpose: In this paper we investigated a new method for dose-response analysis of longitudinal data in terms of precision and accuracy using simulations.

Methods: The new method, called Dose-Response Mixed Models for Repeated Measures (DR-MMRM), combines conventional Mixed Models for Repeated Measures (MMRM) and dose-response modeling. Conventional MMRM can be applied for highly variable repeated measure data and is a way to estimate the drug effect at each visit and dose, however without any assumptions regarding the dose-response shape.

A Bounded Integer Model for Rating and Composite Scale Data.

Rating and composite scales are commonly used to assess treatment efficacy. The two main strategies for modelling such endpoints are to treat them as a continuous or an ordered categorical variable (CV or OC). Both strategies have disadvantages, including making assumptions that violate the integer nature of the data (CV) and requiring many parameters for scales with many response categories (OC).

Using a semi-mechanistic model to identify the main sources of variability of metformin pharmacokinetics.

Metformin pharmacokinetics (PK) is highly variable, and researchers have for years tried to shed light on determinants of inter-individual (IIV) and inter-occasion variability (IOV) of metformin PK. We set out to identify the main sources of PK variability using a semi-mechanistic model. We assessed the influence of subject characteristics, including seven genetic variants.

Comparison of Power, Prognosis, and Extrapolation Properties of Four Population Pharmacodynamic Models of HbA1c for Type 2 Diabetes.

Reusing published models saves time; time to be used for informing decisions in drug development. In antihyperglycemic drug development, several published HbA1c models are available but selecting the appropriate model for a particular purpose is challenging. This study aims at helping selection by investigating four HbA1c models, specifically the ability to identify drug effects (shape, site of action, and power) and simulation properties.

A worldwide survey of genome sequence variation provides insight into the evolutionary history of the honeybee Apis mellifera.

The honeybee Apis mellifera has major ecological and economic importance. We analyze patterns of genetic variation at 8.3 million SNPs, identified by sequencing 140 honeybee genomes from a worldwide sample of 14 populations at a combined total depth of 634×.