Development and performance of npde for the evaluation of time-to-event models.

PURPOSE: Normalised prediction distribution errors (npde) are used to graphically and statistically evaluate mixed-effect models for continuous responses. In this study, our aim was to extend npde to time-to-event (TTE) models and evaluate their performance. METHODS: Let V denote a dataset with censored TTE observations. The null hypothesis (H0) is that observations in V can be described by model M. We extended npde to TTE models using imputations to take into account censoring. We then evaluated their performance in terms of type I error and power to detect model misspecifications for TTE data by means of a simulation study with different sample sizes. RESULTS: Type I error was found to be close to the expected 5% significance level for all sample sizes tested. The npde were able to detect misspecifications in the baseline hazard as well as in the link between the longitudinal variable and the survival function. The ability to detect model misspecifications increased as the difference in the shape of the survival function became more apparent. As expected, the power also increased as the sample size increased. Imputing the censored events tended to decrease the percentage of rejections. CONCLUSIONS: We have shown that npde can be readily extended to TTE data and that they perform well with an adequate type I error.

MatVPC: A User-Friendly MATLAB-Based Tool for the Simulation and Evaluation of Systems Pharmacology Models.

Quantitative systems pharmacology (QSP) models are progressively entering the arena of contemporary pharmacology. The efficient implementation and evaluation of complex QSP models necessitates the development of flexible computational tools that are built into QSP mainstream software. To this end, we present MatVPC, a versatile MATLAB-based tool that accommodates QSP models of any complexity level. MatVPC executes Monte Carlo simulations as well as automatic construction of visual predictive checks (VPCs) and quantified VPCs (QVPCs).

Pharmacometrics Markup Language (PharmML): Opening New Perspectives for Model Exchange in Drug Development.

The lack of a common exchange format for mathematical models in pharmacometrics has been a long-standing problem. Such a format has the potential to increase productivity and analysis quality, simplify the handling of complex workflows, ensure reproducibility of research, and facilitate the reuse of existing model resources. Pharmacometrics Markup Language (PharmML), currently under development by the Drug Disease Model Resources (DDMoRe) consortium, is intended to become an exchange standard in pharmacometrics by providing means to encode models, trial designs, and modeling steps.

Pharmacodynamic response modelling of arterial blood pressure in adult volunteers during propofol anaesthesia.

BACKGROUND: Concentration effect relationships are commonly described with a direct response model as for example the sigmoid E(max) model with one effect compartment as site of action. In this study we investigated whether models with more than one effect site, or indirect response, or counter-regulatory response models may be more appropriate for modelling the propofol effect on arterial blood pressure. METHODS: Nine young healthy volunteers received propofol as target controlled infusion with predefined increasing and decreasing plasma target concentrations. Propofol concentrations were determined from arterial blood samples. Arterial blood pressure was measured invasively at radial artery site. Pharmacokinetic/-pharmacodynamic modelling was performed by population analysis with MONOLIX, testing different direct, indirect and counter-regulatory response models. RESULTS: Propofol plasma concentrations were well described by a three-compartment model. The propofol effect on arterial blood pressure was best described by a direct sigmoid E(max) model with two effect site compartments. CONCLUSIONS: Two effect sites were needed to describe the propofol effect on arterial blood pressure. This may reflect different pathways of arterial blood pressure response to propofol.

Prediction of Response to Temozolomide in Low-Grade Glioma Patients Based on Tumor Size Dynamics and Genetic Characteristics.

Both molecular profiling of tumors and longitudinal tumor size data modeling are relevant strategies to predict cancer patients' response to treatment. Herein we propose a model of tumor growth inhibition integrating a tumor's genetic characteristics (p53 mutation and 1p/19q codeletion) that successfully describes the time course of tumor size in patients with low-grade gliomas treated with first-line temozolomide chemotherapy. The model captures potential tumor progression under chemotherapy by accounting for the emergence of tissue resistance to treatment following prolonged exposure to temozolomide. Using information on individual tumors' genetic characteristics, in addition to early tumor size measurements, the model was able to predict the duration and magnitude of response, especially in those patients in whom repeated assessment of tumor response was obtained during the first 3 months of treatment. Combining longitudinal tumor size quantitative modeling with a tumor''s genetic characterization appears as a promising strategy to personalize treatments in patients with low-grade gliomas.

A comprehensive hepatitis C viral kinetic model explaining cure.

We propose a model that characterizes and links the complexity and diversity of clinically observed hepatitis C viral kinetics to sustained virologic response (SVR)-the primary clinical end point of hepatitis C treatment, defined as an undetectable viral load at 24 weeks after completion of treatment)-in patients with chronic hepatitis C (CHC) who have received treatment with peginterferon alpha-2a +/- ribavirin. The new attributes of our hepatitis C viral kinetic model are (i) the implementation of a cure/viral eradication boundary, (ii) employment of all hepatitis C virus (HCV) RNA measurements, including those below the lower limit of quantification (LLOQ), and (iii) implementation of a population modeling approach. The model demonstrated excellent positive (99.3%) and negative (97.1%) predictive values for SVR as well as high sensitivity (96.6%) and specificity (99.4%). The proposed viral kinetic model provides a framework for mechanistic exploration of treatment outcome and permits evaluation of alternative CHC treatment options with the ultimate aim of developing and testing hypotheses for personalizing treatments in this disease.

Maximum a posteriori estimation of change points in the EEG.

A new approach for EEG segmentation is introduced. This is based on a methodology for optimal segmentation of non-stationary signals derived from the maximum a posteriori estimation principle. It is a model-based, not sequential approach that allows for segmentation at different resolution levels. The features of the methodology are illustrated by its application to EEG recordings containing several types of spectral changes due to normal and pathological variations of spontaneous brain rhythmic activities, as well as physiological artifacts.