Asthma is an obstructive lung disease where the mechanism of disease progression is not fully understood hence motivating the use of empirical models to describe the evolution of the patient’s health state. With reference to placebo response, measured in terms of FEV1 (Forced Expiratory Volume in 1 s), a range of empirical models taken from the literature were compared at a single trial level. In particular, eleven GSK trials lasting 12 weeks in mild-to-moderate asthma were used for the modelling of longitudinal placebo responses. Then, the chosen exponential model was used to carry out an individual participant data meta-analysis on eleven trials. A covariate analysis was also performed to find relevant covariates in asthma to be accounted for in the meta-analysis model. Age, gender, and height were found statistically significant (e.g. the taller the patients the higher the FEV1, the older the patients the lower the FEV1, and females have lower FEV1). By truncating each trial at week 4, the predictive properties of the meta-analysis model were also investigated, showing its ability to predict long-term FEV1 response from truncated trials. Summarizing, the study suggests that: (i) the exponential model effectively describes the placebo response; (ii) the meta-analysis approach may prove helpful to simulate new trials as well as to reduce trial duration in view of its predictive properties; (iii) the inclusion of available covariates within the meta-analysis model provides a reduction of the inter-individual variability.

Population model of longitudinal FEV1 data in asthmatics: meta-analysis and predictability of placebo response

MAROSTICA, ELEONORA;RUSSU, ALBERTO;DE NICOLAO, GIUSEPPE;
2014-01-01

Abstract

Asthma is an obstructive lung disease where the mechanism of disease progression is not fully understood hence motivating the use of empirical models to describe the evolution of the patient’s health state. With reference to placebo response, measured in terms of FEV1 (Forced Expiratory Volume in 1 s), a range of empirical models taken from the literature were compared at a single trial level. In particular, eleven GSK trials lasting 12 weeks in mild-to-moderate asthma were used for the modelling of longitudinal placebo responses. Then, the chosen exponential model was used to carry out an individual participant data meta-analysis on eleven trials. A covariate analysis was also performed to find relevant covariates in asthma to be accounted for in the meta-analysis model. Age, gender, and height were found statistically significant (e.g. the taller the patients the higher the FEV1, the older the patients the lower the FEV1, and females have lower FEV1). By truncating each trial at week 4, the predictive properties of the meta-analysis model were also investigated, showing its ability to predict long-term FEV1 response from truncated trials. Summarizing, the study suggests that: (i) the exponential model effectively describes the placebo response; (ii) the meta-analysis approach may prove helpful to simulate new trials as well as to reduce trial duration in view of its predictive properties; (iii) the inclusion of available covariates within the meta-analysis model provides a reduction of the inter-individual variability.
2014
Pharmacology & Toxicology includes all aspects of pharmacology, toxicology, and pharmaceutics. Of particular importance are cellular and molecular pharmacology, drug design and metabolism, mechanisms of drug action, drug delivery, natural products, xenobiotics, and clinical therapeutics. Toxicology coverage considers cellular and molecular effects of harmful substances, environmental toxicology, occupational exposure, and clinical toxicology. Drug bulletins, drug updates, and pharmaceutical newsletters are excluded as are resources on pharmaceutical engineering. Medicinal chemistry, or synthesis and chemical analysis of pharmaceuticals are placed in the Chemistry & Analysis category.
Esperti anonimi
Inglese
Internazionale
STAMPA
41
553
569
17
Asthma; Covariate analysis; Meta-analysis; Population approach; Predictability analysis
http://www.scopus.com/inward/record.url?eid=2-s2.0-84912046280&partnerID=40&md5=1c51d547125e5648f18b08734da374e2
6
info:eu-repo/semantics/article
262
Marostica, Eleonora; Russu, Alberto; Yang, S.; DE NICOLAO, Giuseppe; Zamuner, S.; Beerahee, M.
1 Contributo su Rivista::1.1 Articolo in rivista
none
File in questo prodotto:
Non ci sono file associati a questo prodotto.

I documenti in IRIS sono protetti da copyright e tutti i diritti sono riservati, salvo diversa indicazione.

Utilizza questo identificativo per citare o creare un link a questo documento: https://hdl.handle.net/11571/1009192
Citazioni
  • ???jsp.display-item.citation.pmc??? ND
  • Scopus 5
  • ???jsp.display-item.citation.isi??? 5
social impact