Objectives: Asthma is a complex and multi-factorial disease and the underlying physiopathological mechanism is not completely known. Therefore, empirical models are usually adopted to describe the evolution of the patient's health state. The first objective of this work is to develop a parsimonious population model to describe the time course of placebo response. The clinical response is measured by the Forced Expiratory Volume in the first second (FEV1). The second objective is to perform a model-based meta-analysis, in order to assess differences among studies and to estimate the inter-trial variability. Methods: Placebo FEV1 longitudinal data from 11 clinical trials in subjects with mild-to-moderate asthma were available. All studies lasted 12 weeks. A parametric first-order response model was developed and identified on each dataset. Based on a single-trial analysis, the proposed model was compared to the linear, polynomial, Inverse Bateman and Weibull-and-Linear models. All the models were implemented in WinBUGS 1.4.3 [1] and compared through the Deviance Information Criterion (DIC). The best model was then adopted to perform a meta-analysis on the 11 datasets together. In the meta-analysis model, each individual parameter was defined as the sum of a term relative to the subject and one relative to the study. For both the single-trial analysis and the meta-analysis, log-normal distribution was assumed for all the parameters. Graphical outputs were obtained through R 2.13.1 [2]. Results: In the single-trial analysis, the first-order parametric model here proposed yielded the best performance in terms of DIC in most cases. Good individual fittings and Visual Predictive Checks were obtained for all the 11 trials. Hence, meta-analysis was performed. The proposed model yielded good performances also when applied in a meta-analysis context. Moreover, it was found that the inter-individual variability in each study is higher than the inter-trial one (baseline: 24% vs 6%; maximal response: 148% vs 28%; time constant: 906% vs 71%). Conclusion: A parsimonious parametric model able to describe FEV1 data from different studies in mild-to-moderate asthma was developed. The proposed model performs well both in the single-trial analysis and meta-analysis context. Moreover, the model can be extended by including clinically relevant covariates which may affect the patient's health state. A further work is to assess the model capabilities in predicting long-term outcomes from short-term trials in placebo group. References: [1] D.J. Lunn, A. Thomas, N. Best and D. Spiegelhalter, WinBUGS A Bayesian modelling framework: concepts, structure and extensibility, Statistics and Computing 10, 325-337, 2000 [2] 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.
First-order longitudinal population model of FEV1 data: single-trial modeling and meta-analysis
MAROSTICA, ELEONORA;RUSSU, ALBERTO;DE NICOLAO, GIUSEPPE;
2013-01-01
Abstract
Objectives: Asthma is a complex and multi-factorial disease and the underlying physiopathological mechanism is not completely known. Therefore, empirical models are usually adopted to describe the evolution of the patient's health state. The first objective of this work is to develop a parsimonious population model to describe the time course of placebo response. The clinical response is measured by the Forced Expiratory Volume in the first second (FEV1). The second objective is to perform a model-based meta-analysis, in order to assess differences among studies and to estimate the inter-trial variability. Methods: Placebo FEV1 longitudinal data from 11 clinical trials in subjects with mild-to-moderate asthma were available. All studies lasted 12 weeks. A parametric first-order response model was developed and identified on each dataset. Based on a single-trial analysis, the proposed model was compared to the linear, polynomial, Inverse Bateman and Weibull-and-Linear models. All the models were implemented in WinBUGS 1.4.3 [1] and compared through the Deviance Information Criterion (DIC). The best model was then adopted to perform a meta-analysis on the 11 datasets together. In the meta-analysis model, each individual parameter was defined as the sum of a term relative to the subject and one relative to the study. For both the single-trial analysis and the meta-analysis, log-normal distribution was assumed for all the parameters. Graphical outputs were obtained through R 2.13.1 [2]. Results: In the single-trial analysis, the first-order parametric model here proposed yielded the best performance in terms of DIC in most cases. Good individual fittings and Visual Predictive Checks were obtained for all the 11 trials. Hence, meta-analysis was performed. The proposed model yielded good performances also when applied in a meta-analysis context. Moreover, it was found that the inter-individual variability in each study is higher than the inter-trial one (baseline: 24% vs 6%; maximal response: 148% vs 28%; time constant: 906% vs 71%). Conclusion: A parsimonious parametric model able to describe FEV1 data from different studies in mild-to-moderate asthma was developed. The proposed model performs well both in the single-trial analysis and meta-analysis context. Moreover, the model can be extended by including clinically relevant covariates which may affect the patient's health state. A further work is to assess the model capabilities in predicting long-term outcomes from short-term trials in placebo group. References: [1] D.J. Lunn, A. Thomas, N. Best and D. Spiegelhalter, WinBUGS A Bayesian modelling framework: concepts, structure and extensibility, Statistics and Computing 10, 325-337, 2000 [2] 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.I documenti in IRIS sono protetti da copyright e tutti i diritti sono riservati, salvo diversa indicazione.
