1,720,990 research outputs found
A new, second-order indirect model of depression time course
No full textObjectives: The limited understanding of placebo effect and drug action in psychiatric diseases has led to a widespread use of ad-hoc empirical models [1,2] and simple indirect response models [3,4] to describe the time course of clinical scores (e.g. HAMD, PANSS) in psychiatric trials. Open issues include the ability to describe complex response profiles and the handling of different dosing schedules. This motivates the present work, where a new approach inspired by indirect response modelling is proposed.
Methods: A new, second-order indirect model was devised in order to capture the structural properties of the treatment response (initial improvement followed by relapse). We extended the methodology of indirect response modelling to incorporate a feedback mechanism [5]. The model includes a compartment representing the HAMD score, with zero-order response formation (k_in) and first-order dissipation (k_out). Decrease of the HAMD state causes a stimulation of the response rate and therefore a feedback action. Treatment effect was modelled as an inhibitory function on the response rate. The proposed model was applied to two Phase II randomized, double-blind, placebo-controlled trials relative to a GlaxoSmithKline investigational antidepressant. Both studies featured a flexible dosing scheme that allowed non-responding patients to be escalated to a higher dose level. Parameter identification was performed with NONMEM 6.2 [6].
Results: The proposed model was successfully fitted to data of both studies. Individual data were well described. In particular, the new second-order indirect model was able to capture different patterns of response profiles, e.g. patients who improve steadily, non-responders, or patients who relapse into a depressive state after an initial improvement. Additionally, the model was able to describe changes in the response time course due to dose escalations. Visual predictive checks confirmed a proper characterization of the population distribution.
Conclusions: Our results show the feasibility of a new modelling approach for longitudinal psychiatric data. In this work, we extended the well-known methodology of indirect response models to account for the complex patterns of response usually observed in psychiatric trials. This approach represents a step forward with respect to simple empirical models: the greater level of structure of the proposed model allows to describe complex response profiles and to perform simulations with different dosing schedules.
References:
[1] E.Y. Shang, M.A. Gibbs, J.W. Landen et al. (2009). Evaluation of structural models to describe the effect of placebo upon the time course of major depressive disorder. J Pharmacokinet Pharmacodyn 36:63-80.
[2] G. Nucci, R. Gomeni, I. Poggesi (2009). Model-based approaches to increase efficiency of drug development in schizophrenia: a can’t miss opportunity. Expert Opin Drug Discov 4:837-856.
[3] D.E. Mager, E. Wyska, W.J. Jusko (2003). Diversity of mechanism-based pharmacodynamic models. Drug Metab Dispos 31:510–519.
[4] I. Ortega Azpitarte, A. Vermeulen, V. Piotrovsky (2006). Concentration-response analysis of antipsychotic drug effects using an indirect response model. Population Approach Group in Europe 15th Meeting.
[5] K.P. Zuideveld, H.J. Maas, N. Treijtel et al. (2001). A set-point model with oscillatory behavior predicts the time course of 8-OH-DPAT-induced hypothermia. Am J Physiol Regulatory Integrative Comp Physiol 281:R2059–R2071.
[6] Beal, S.L., Sheiner, L.B., Boeckmann, A.J. (Eds.), 1989–2006. NONMEM Users Guides. Icon Development Solutions, Ellicott City, Maryland, USA
Integration of response, tolerability and dropout in flexible-dose trials: a case study in depression
No full textPrintable version
PAGE. Abstracts of the Annual Meeting of the Population Approach Group in Europe.
ISSN 1871-6032
Reference:
PAGE 20 (2011) Abstr 2131 [www.page-meeting.org/?abstract=2131]
PDF poster/presentation:
Click to openClick to open
Poster: Other topics - Methodology
IV-01 Alberto Russu Integration of response, tolerability and dropout in flexible-dose trials: a case study in depression
A. Russu(1), E. Marostica(1), G. De Nicolao(1), A.C. Hooker(2), I. Poggesi(3,*), R. Gomeni(4), S. Zamuner(5)
(1) Department of Computer Engineering and Systems Science, University of Pavia, Pavia, Italy; (2) Department of Pharmaceutical Biosciences, Uppsala University, Uppsala, Sweden; (3) Clinical Pharmacology / Modelling & Simulation, GlaxoSmithKline, Verona, Italy; (4) Pharmacometrics, GlaxoSmithKline, King of Prussia, PA, USA; (5) Clinical Pharmacology / Modelling & Simulation, GlaxoSmithKline, Stockley Park, UK; * Current address: Advanced Modeling&Simulation, Janssen Pharmaceutical Companies of Johnson & Johnson, Milan, Italy
Objectives: The difficulties arising when analyzing depression trials are manifold, as a comprehensive model, in addition to the efficacy endpoints, should account for: (i) flexible dosing schemes, (ii) dropout events, and (iii) drug-related adverse effects. Simplified modelling approaches that neglect some of the above aspects may yield biased results. In this work we investigate an integrated approach based on the joint population modelling of response, tolerability and dropout. The proposed methodology is used to analyse data from a flexible-dose, placebo-controlled, Phase II depression trial. As an extension of previous work [1,2], in this study we account for flexible dosage regimen and adverse events as covariates in the dropout model.
Methods: The time course of the HAMD score was described as the sum of a Weibull and a linear function [3]. The dose escalation was included in the model as a covariate on two of the four structural parameters. We investigated three different dropout mechanisms: missing completely at random (MCAR), at random (MAR) and not at random (MNAR) [4]. The dropout probability was modulated using three covariates: the time course of the clinical outcome, dose escalation, and the occurrence of clinically relevant adverse events in the drug arm. The population model was implemented in WinBUGS 1.4.3 [5].
Results: With respect to previous approaches [1,2], which used only the HAMD score as a covariate in the hazard model, the proposed method achieved comparable goodness-of-fit to HAMD data. However, the inclusion of dose escalation and drug-related adverse events in the hazard function yielded a substantial benefit in the description of the dropout process, as witnessed by the Deviance Information Criterion [6], parameter estimates, and the modified Cox-Snell residuals [4]. Comparison of the dropout mechanisms suggested a MNAR dropout process in both treatment arms. The ability of the proposed model to reproduce realistic dropout patterns was assessed via Kaplan-Meier visual predictive checks [7].
Conclusions: Our results show the feasibility of a joint model accounting for the HAMD time course, discontinuities in the dosing schedule, dropouts and adverse events. Indeed, in the study here analyzed, the dropout process was influenced by all the above aspects. Thorough modelling approaches that integrate all the relevant information are necessary to provide a more comprehensive assessment of antidepressant drug response.
References:
[1] Russu A, Marostica E, De Nicolao G, Hooker AC, Poggesi I, Gomeni R, Zamuner S (2010), Integrated model for clinical response and dropout in depression trials: a state-space approach, Population Approach Group Europe (PAGE) 19th Meeting, Abstract 1852
[2] Hooker C, Gomeni R, Zamuner S (2009), Time to event modeling of dropout events in clinical trials, Population Approach Group Europe (PAGE) 18th Meeting, Abstract 1656
[3] Gomeni R, Lavergne A, Merlo-Pich E (2009), Modelling placebo response in depression trials using a longitudinal model with informative dropout, European Journal of Pharmaceutical Sciences 36, pp. 4-10
[4] Hu C, Sale ME (2003), A joint model for longitudinal data with informative dropout, Journal of Pharmacokinetics and Pharmacodynamics 30, pp. 83-103
[5] Lunn DJ, Thomas A, Best N, Spiegelhalter D (2000), WinBUGS - A Bayesian modelling framework: concepts, structure and extensibility. Statistics and Computing 10, pp. 325-337
[6] Spiegelhalter DJ, Best NG, Carlin BP, van der Linde A (2002), Bayesian measures of model complexity and fit (with discussion), Journal of the Royal Statistics Society 64, pp. 583-639
[7] Holford N (2005), The visual predictive check: superiority to standard diagnostic (Rorschach) plots, Population Approach Group Europe (PAGE) 14th Meeting, Abstract 73
Population state-space modelling of patient responses in antidepressant studies
No full textObjectives: A major challenge posed by the analysis of the clinical scores used to assess the disease status in depression trials is the lack of "first principles" from which response models can be derived. The state-space framework, which is based on a set of differential (or difference) equations that describes the evolution of one or more variables characterizing the patient's health state [1], represents an appealing and more mechanistically driven approach to describe these data. In order to develop a comprehensive state-space approach, we address two main questions: (i) do state-space models give adequate descriptions of the clinical response? (ii) how should flexible dosing schedules be handled within a state-space framework?
Methods: A double-blind, randomized, placebo controlled, flexible dose depression trial was used as a benchmark for alternative state-space approaches. Discrete- and continuous-time stochastic processes (i.e. integrated random walks and integrated Wiener processes [2, 3]) were used to describe the time-course of the HAMD score, within the framework of population modelling. In particular, each individual curve was expressed as the sum of an average curve and an individual shift, both described as random processes whose statistics were specified through hyperparameters. Dose changes were modelled as impulses on the second derivative of the patient's score. According to an empirical Bayes paradigm, hyperparameters were estimated through Maximum Likelihood. Estimation and post-processing were carried out with R 2.10.0 [4].
Results: Even low-order discrete- and continuous-time state-space models were able to fit very satisfactorily the whole range of shapes observed in individual responses. Moreover, the explicit description of dose changes improved the performances in terms of residuals. The continuous-time model appears to be marginally superior to the discrete-time one.
Conclusions: The results demonstrate that state-space approaches not only provide adequate description of population responses but are also easily adapted to account for possible dose changes during the trial. Among the advantages, there is the possibility to model the presence of random perturbations that affect the patient's health state. A further step to explore is the development of an integrated response and dropout model within the state-space framework.
References:
[1] Russu A, Marostica E, De Nicolao G, Hooker AC, Poggesi I, Gomeni R, Zamuner S (2010), Integrated model for clinical response and dropout in depression trials: a state-space approach, Population Approach Group Europe (PAGE) 19th Meeting, Abstract 1852
[2] Magni P, Bellazzi R, De Nicolao G, Poggesi I, Rocchetti M (2002), Nonparametric AUC estimation in population studies with incomplete sampling: a Bayesian approach, Journal of Pharmacokinetics and Pharmacodynamics 29, pp. 445-471
[3] Neve M, De Nicolao G, Marchesi L (2007), Nonparametric identification of population models via Gaussian processes, Automatica 43, pp. 1134-1144
[4] R Development Core Team. R: A Language and Environment for Statistical Computing. R Foundation for Statistical Computing, Vienna, Austria (2010)
A data mining approach to evaluate the performance of recruitment centres in antidepressant drug clinical trials
No full textPCA-based modelling in antidepressant trials: a pre-mechanistic approach
No full textObjectives: For many diseases, such as psychiatric ones, mechanistic knowledge of the progression of the disease and the interaction between disease and drug action is often very limited or absent. The empirical models, adopted to describe the evolution of clinical endpoints [1], are characterized by arbitrarily chosen basis functions and are usually dataset-specific. This motivates the development of a flexible and general-purpose "pre-mechanistic" technique to be used for exploratory analysis and as a touchstone for subsequent mechanistic model building. Along this direction, the aim of this work is to introduce a method, based on Principal Component Analysis (PCA), that automatically provides regression functions reflecting the informative content of the data: the PCA-based approach [2].
Methods: Population analyses of simulated and experimental datasets were performed. Three parametric models were used to simulate 50 datasets (100 subjects per dataset): Weibull, Inverse Bateman and Weibull + linear models. The experimental dataset was obtained from a Phase II depression trial. The proposed approach provides the principal functions of the unobservable true signal through the singular value decomposition of the covariance matrix of data. The number of components was selected through either Mallows' Cp criterion [3] or random crossvalidation. The new PCA-based approach and the three parametric models were compared in simulation in terms of "denoising", i.e. the ability to reconstruct the true individual profiles. Moreover, we evaluated the crossvalidatory RMSE on both simulated and experimental data. Parameter estimation was carried out with R 2.13.1 [4], according to the empirical Bayes paradigm.
Results: The PCA-based approach provided satisfactory denoising perfomances and good predictive ones, in all the 150 simulated datasets. In the experimental scenario, the PCA-based model with 3 principal functions was chosen according to the order selection procedure. The proposed approach achieved very satisfactory individual fittings and crossvalidatory performances.
Conclusions: The proposed PCA-based approach can be valuable when the mechanistic knowledge of the disease is limited or absent. It automatically provides basis functions suitable to develop parsimonious population models and yields reliable reconstructions of individual profiles. This approach is useful for exploratory analysis and as a touchstone in order to benchmark the performances of mechanistic models.
References:
[1] Gomeni R, Lavergne A, Merlo-Pich E (2009), Modelling placebo response in depression trials using a longitudinal model with informative dropout, European Journal of Pharmaceutical Science 36, pp. 4-10.
[2] Abdi H, Williams Lynne J (2010), Principal component analysis, WIREs Computational Statistics 2, pp. 433-459
[3] Mallows C L (1973), Some comments on Cp, Technometrics 15, pp. 661-675
[4] R Development Core Team. R: A Language and Environment for Statistical Computing. R Foundation for Statistical Computing, Vienna, Austria (2011). http://www.r-project.org/
Second order Markov modelling of HAMD responses in depression trials
No full textObjectives: Longitudinal models describing the time course of the clinical endpoint in psychiatric trials are usually empirical. Moreover, conditional on the individual parameters the response model does not structurally account for random fluctuations on the disease. The first attempt to include these aspects, presented in [1] resorting to stochastic difference and differential equations, did not give a completely satisfactory description of inter-individual variability. We propose an extension of the previous work through a more sophisticated continuous-time dynamic model based on second order Markov processes [2]. The proposed model aims to describe appropriately the clinical response and handle flexible dosing schemes.
Methods: A Phase II, double-blind, randomized, placebo-controlled, flexible-dose depression trial was analyzed. We modelled the individual time series of HAMD scores within the framework of population modelling. The typical curve was modelled as an integrated Wiener process [3] whereas a second order Markov model was adopted to describe the individual shifts with respect to the population curve. Two Markov models were analyzed having either (i) two coincident poles or (ii) two distinct poles in the transfer function. Dose changes were accounted for by varying the trend of the response profile. Models statistics were specified through hyperparameters. A unique hyperparameter for the measurement error was considered in order to simultaneously identify the model on the four subpopulations (placebo and drug: non-escalating and escalating subjects). Software R 2.10.0 [4] was adopted according to the empirical Bayes paradigm.
Results: Both models were able to capture the shapes of individual responses. Moreover, good predictive performances in terms of VPCs were obtained. According to the Bayesian Information Criterion, the second order Markov model with two coincident poles in the transfer function should be preferred.
Conclusions: The results demonstrate the feasibility and effectiveness of second order Markov processes as an innovative modelling approach for longitudinal data, when mechanistic knowledge is poor or absent. We showed that the proposed models yield good individual fittings as well as a good estimate of the population response and an appropriate representation of the inter-individual variability. Interestingly, both models are able to easily handle dose changes and account for random perturbations with greater flexibility than previous approaches [1].
References:
[1] Marostica E, Russu A, De Nicolao G, Gomeni R (2011), Population state-space modelling of patient responses in antidepressant studies, Population Approach Group Europe (PAGE) 20th Meeting, Abstract 2133.
[2] Mortensen SB (2010), Markov and mixed models with applications, PhD Thesis, Technical University of Denmark (DTU), Kgs. Lyngby.
[3] Neve M, De Nicolao G, Marchesi L (2007), Nonparametric identification of population models via Gaussian processes, Automatica 43, pp. 1134-1144.
[4] R Development Core Team. R: A Language and Environment for Statistical Computing. R Foundation for Statistical Computing, Vienna, Austria (2010). http://www.r-project.org/
Joint Modeling of Efficacy, Dropout, and Tolerability in Flexible-Dose Trials: A Case Study in Depression
No full textMany difficulties may arise during the modeling of the time course of Hamilton Rating Scale for Depression (HAM D) scores in clinical trials for the evaluation of antidepressant drugs: (i) flexible designs, used to increase the chance of selecting more efficacious doses, (ii) dropout events, and (iii) adverse effects related to the experimental compound. It is crucial to take into account all these factors when designing an appropriate model of the HAM D time course and to obtain a realistic description of the dropout process. In this work, we propose an integrated approach to the modeling of a double-blind, flexible-dose, placebo-controlled, phase II depression trial that comprises response, tolerability, and dropout. We investigate three different dropout mechanisms in terms of informativeness. Goodness of fit is quantitatively assessed with respect to response (HAM D score) and dropout data. We show that dropout is a complex phenomenon that may be influenced by HAM D evolution, dose changes, and occurrence of drug-related adverse effects
- …
