The AAPS Journal

, 22:4 | Cite as

Application of Item Response Theory to Model Disease Progression and Agomelatine Effect in Patients with Major Depressive Disorder

  • Marc CerouEmail author
  • Sophie Peigné
  • Emmanuelle Comets
  • Marylore Chenel
Research Article



In this paper, we studied the effect over time of agomelatine, an antidepressant drug administered in patient with major depressive disorder, through item response theory (IRT), taking into account a strong placebo effect and missing not at random. We also assessed the informativeness of the HAMD-17 scale’s item.

Materials and Methods

The data includes five phase III clinical trials sponsored by Servier Institute, totalling 1549 patients followed during a maximum of 1 year. At each observation, individual scores for the 17 items of the HAMD scale were recorded. The probability for each score was modelled with IRT. A non-linear mixed effects model was used to describe the evolution of the disease and was coupled with a time to event model to predict dropout. Clinical trial simulations were then used to compare placebo and active treatment. Informativeness of each item was evaluated using the Fisher information theory.


The best model combined an IRT model, a longitudinal model for underlying depression which describes the remission and then a possible relapse, and a hazard model for dropout depending on the evolution from baseline. The drug effect was best modelled as an effect on the remission and the relapse phases. The median predicted drop in HAMD between baseline and 6 weeks was 8.8 (90% PI, 8.3–9.2) when on placebo and 13.1 (90% PI, 12.8–13.4) when treated. Nine items were found to be the most informative.


The IRT framework allowed to characterise the evolution of depression with time and estimate the effect of agomelatine, as well as the link between symptoms and disease.

Key Words

Agomelatine IRT Major depressive disorder MNAR NLMEM 



The authors thank Valérie Olivier, Pierre-François Penelaud and Cécilia Gabriel Gracia for their clinical insight and challenging discussions. We would like to thank also Donato Teutonico and Karl Brendel for their valuable contribution to this work as well as Hervé Le Nagard and Lionel de la Tribouille for the use of the computer cluster services hosted on the “Centre de Biomodélisation UMR1137”. This work is also indebted to the investigators in the Goodwin et al. (36), Olie, Kennedy and Emsley (44), Kennedy et al. (45) and Heun (46) studies.

Funding Information

Marc Cerou received funding from Institut de Recherches Internationales Servier, as part of a PhD research fellowship programme.

Supplementary material

12248_2019_379_MOESM1_ESM.pdf (2.2 mb)
ESM 1 (PDF 2273 kb)


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Copyright information

© American Association of Pharmaceutical Scientists 2019

Authors and Affiliations

  1. 1.Université de Paris, IAME, INSERMParisFrance
  2. 2.Division of Clinical Pharmacokinetics and PharmacometricsInstitut de Recherches Internationales ServierSuresnesFrance
  3. 3.CIC 1414INSERMRennesFrance
  4. 4.Université Rennes-1RennesFrance

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