Random effects diagonal metric multidimensional scaling models

By assuming a distribution for the subject weights in a diagonal metric (INDSCAL) multidimensional scaling model, the subject weights become random effects. Including random effects in multidimensional scaling models offers several advantages over traditional diagonal metric models such as those fitted by the INDSCAL, ALSCAL, and other multidimensional scaling programs. Unlike traditional models, the number of parameters does not increase with the number of subjects, and, because the distribution of the subject weights is modeled, the construction of linear models of the subject weights and the testing of those models is immediate. Here we define a random effects diagonal metric multidimensional scaling model, give computational algorithms, describe our experiences with these algorithms, and provide an example illustrating the use of the model and algorithms.Peer Reviewedhttp://deepblue.lib.umich.edu/bitstream/2027.42/45758/1/11336_2005_Article_BF02295730.pd

SAAM II: Simulation, Analysis, and Modeling Software for tracer and pharmacokinetic studies.

Kinetic analysis and integrated systems modeling have contributed substantially to our understanding of the physiology and pathophysiology of metabolic systems and the distribution and clearance of drugs in humans and animals. In recent years, many researchers have become aware of the usefulness of these techniques in the experimental design. With this has come the recognition that the discipline of kinetic analysis requires its own expertise. The expertise can impact experimental design in many ways, from the collaborative and service activities in which individuals interact in formal ways to the development of software tools to aid in kinetic analysis. The purpose of this report is to describe one such software tool, Simulation, Analysis, and Modeling Software II (SAAM II). In the first part, we describe in general how the user can take advantage of the capabilities of the software system, and in the second part, we give three specific examples using multicompartmental models found in lipoprotein (apolipoprotein B [apoB] kinetics) and diabetes (glucose minimal model) research

Accurately Achieving Target Busulfan Exposure in Children and Adolescents With Very Limited Sampling and the BestDose Software

International audienc

Pharmacodynamic Evaluation of RWJ-270201, a Novel Neuraminidase Inhibitor, in a Lethal Murine Model of Influenza Predicts Efficacy for Once-Daily Dosing

We examined RWJ-270201 in a lethal model of influenza in BALB/c mice. The aim was to delineate the pharmacodynamically linked variable for the drug. Challenge was performed with influenza virus A/Shongdong/09/93 (H3N2). Treatment was administered by gavage. Five doses (1 to 10 mg/kg of body weight) and three schedules (every 24, 12, and 8 h) were evaluated with 10 mice per group. There were 39 placebo-treated mice. Drug exposure was evaluated for infected mice. Exposures were calculated after population modeling of all the plasma concentration-time data simulataneously using the NPEM3 program. Evaluation of dose and schedule with Kaplan-Meier analysis and Cox proportional hazards modeling demonstrated that schedule offered no explanatory power relative to dose alone. Evaluation of peak concentration, trough concentration, and area under the concentration-time curve (AUC) by the same methods revealed that AUC was the dynamically linked variable. Again, schedule offered no further explanatory power when included in the model with AUC. This indicates that AUC is the linked variable and that the anti-influenza effect of RWJ-270201 is independent of schedule. We conclude that once-daily dosing of RWJ-270201 should be evaluated in clinical trials of influenza therapy