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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

Abstract

Introduction

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.

Results

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.

Conclusion

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 

Notes

Acknowledgements

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)

References

  1. 1.
    WHO. World Health Organisation; 2018. Available from: https://www.who.int/en/news-room/fact-sheets/detail/depression. Accessed 13 Sept 2019.
  2. 2.
    Solomon DA, Keller MB, Leon AC, Mueller TI, Lavori PW, Shea MT, et al. Multiple recurrences of major depressive disorder. Am J Psychiatry. 2000;157(2):229–33.CrossRefGoogle Scholar
  3. 3.
    Kessler RC, Bromet EJ. The epidemiology of depression across cultures. Annu Rev Public Health. 2013;34:119–38.CrossRefPubMedPubMedCentralGoogle Scholar
  4. 4.
    Weissman MM, Bland RC, Canino GJ, Faravelli C, Greenwald S, Hwu HG, et al. Cross-national epi-demiology of major depression and bipolar disorder. JAMA. 1996 Jul;276(4):293–9.CrossRefGoogle Scholar
  5. 5.
    Van de Velde S, Bracke P, Levecque K. Gender differences in depression in 23 European countries. Cross-national variation in the gender gap in depression. Soc Sci Med. 2010 Jul;71:305–13.CrossRefGoogle Scholar
  6. 6.
    Bentley SM, Pagalilauan GL, Simpson SA. Major depression. Med Clin N Am. 2014;98(5):981–1005.CrossRefGoogle Scholar
  7. 7.
    De Bodinat C, Guardiola-Lemaitre B, Mocaër E, Renard P, Muñoz C, Millan MJ. Agomelatine, the first melatonergic antidepressant: discovery, characterization and development. Nat Rev Drug Discov. 2010;9(8):628.CrossRefGoogle Scholar
  8. 8.
    Racagni G, Riva MA, Molteni R, Musazzi L, Calabrese F, Popoli M, et al. Mode of action of agomelatine: synergy between melatonergic and 5-HT2C receptors. World J Biol Psychiatry. 2011;12(8):574–87.CrossRefGoogle Scholar
  9. 9.
    Cipriani A, Furukawa TA, Salanti G, Chaimani A, Atkinson LZ, Ogawa Y, et al. Comparative efficacy and acceptability of 21 antidepressant drugs for the acute treatment of adults with major depressive disorder: a systematic review and network meta-analysis. Lancet. 2018;391(10128):1357–66.CrossRefPubMedPubMedCentralGoogle Scholar
  10. 10.
    Taylor D, Sparshatt A, Varma S, Olofinjana O. Antidepressant efficacy of agomelatine: meta-analysis of published and unpublished studies. Br Med J. 2014;348:g1888.CrossRefGoogle Scholar
  11. 11.
    Khan A, Detke M, Khan SR, Mallinckrodt C. Placebo response and antidepressant clinical trial outcome. J Nerv Ment Dis. 2003;191(4):211–8.PubMedGoogle Scholar
  12. 12.
    Iovieno N, Papakostas GI. Correlation between different levels of placebo response rate and clinical trial outcome in major depressive disorder: a meta-analysis. J Clin Psychiatry. 2012;73(10):1300–6.CrossRefGoogle Scholar
  13. 13.
    Hamilton M. A rating scale for depression. J Neurol Neurosurg Psychiatry. 1960;23(1):56–62.CrossRefPubMedPubMedCentralGoogle Scholar
  14. 14.
    Faries D, Herrera J, Rayamajhi J, DeBrota D, Demitrack M, Potter WZ. The responsiveness of the Hamilton depression rating scale. J Psychiatr Res. 2000;34(1):3–10.CrossRefGoogle Scholar
  15. 15.
    Bagby RM, Ryder AG, Schuller DR, Marshall MB. The Hamilton depression rating scale: has the gold standard become a lead weight? Am J Psychiatry. 2004;161(12):2163–77.CrossRefGoogle Scholar
  16. 16.
    Bech P, Rafaelsen O. The use of rating scales exemplified by a comparison of the Hamilton and the Bech-Rafaelsen Melancholia Scale. Acta Psychiatr Scand. 1980;62(S285):128–32.CrossRefGoogle Scholar
  17. 17.
    Gibbons RD, Clark DC, Kupfer DJ. Exactly what does the Hamilton depression rating scale measure? J Psychiatr Res. 1993;27(3):259–73.CrossRefGoogle Scholar
  18. 18.
    Montgomery SA, Åsberg M. A new depression scale designed to be sensitive to change. Br J Psychiatry. 1979;134(4):382–9.CrossRefGoogle Scholar
  19. 19.
    Maier W, Philipp M. Improving the assessment of severity of depressive states: a reduction of the Hamilton Depression Scale. Pharmacopsychiatry. 1985;18(01):114–5.CrossRefGoogle Scholar
  20. 20.
    McIntyre R, Kennedy S, Bagby RM, Bakish D. Assessing full remission. J Psychiatry Neurosci. 2002;27(4):235.PubMedPubMedCentralGoogle Scholar
  21. 21.
    Ueckert S, Plan EL, Ito K, Karlsson MO, Corrigan B, Hooker AC, et al. Improved utilization of ADAS-cog assessment data through item response theory based pharmacometric modeling. Pharm Res. 2014;31(8):2152–65.CrossRefPubMedPubMedCentralGoogle Scholar
  22. 22.
    Buatois S, Retout S, Frey N, Ueckert S. Item response theory as an efficient tool to describe a heterogeneous clinical rating scale in de novo idiopathic Parkinson’s disease patients. Pharm Res. 2017;34(10):2109–18.CrossRefGoogle Scholar
  23. 23.
    Bock RD. A brief history of item theory response. J Educ Meas. 1997;16(4):21–33.CrossRefGoogle Scholar
  24. 24.
    Verma N, Beretvas SN, Pascual B, Masdeu JC, Markey MK. New scoring methodology improves the sensitivity of the Alzheimer’s Disease Assessment Scale-Cognitive subscale (ADAS-Cog) in clinical trials. Alzheimers Res Ther. 2015;7(1):64.CrossRefPubMedPubMedCentralGoogle Scholar
  25. 25.
    Schneider LS, Kennedy RE, Wang G, Cutter GR. Differences in Alzheimer disease clinical trial outcomes based on age of the participants. Neurology. 2015;84(11):1121–7.CrossRefPubMedPubMedCentralGoogle Scholar
  26. 26.
    Dowling NM, Bolt DM, Deng S. An approach for estimating item sensitivity to within-person change over time: an illustration using the Alzheimer’s Disease Assessment Scale-Cognitive subscale (ADAS-Cog). Psychol Assess. 2016;28(12):1576.CrossRefPubMedPubMedCentralGoogle Scholar
  27. 27.
    Novakovic AM, Krekels EH, Munafo A, Ueckert S, Karlsson MO. Application of item response theory to modeling of expanded disability status scale in multiple sclerosis. AAPS J. 2017;19(1):172–9.CrossRefGoogle Scholar
  28. 28.
    Vandemeulebroecke M, Bornkamp B, Krahnke T, Mielke J, Monsch A, Quarg P. A longitudinal item response theory model to characterize cognition over time in elderly subjects. CPT Pharmacometrics Syst Pharmacol. 2017;6(9):635–41.CrossRefPubMedPubMedCentralGoogle Scholar
  29. 29.
    Välitalo P, Krekels E, van Dijk M, Simons S, Tibboel D, Knibbe C. Morphine pharmacodynamics in mechanically ventilated preterm neonates undergoing endotracheal suctioning. CPT Pharmacometrics Syst Pharmacol. 2017;6(4):239–48.CrossRefPubMedPubMedCentralGoogle Scholar
  30. 30.
    Gottipati G, Karlsson MO, Plan EL. Modeling a composite score in Parkinson’s disease using item response theory. AAPS J. 2017;19(3):837–45.CrossRefGoogle Scholar
  31. 31.
    Krekels E, Novakovic AM, Vermeulen A, Friberg LE, Karlsson MO. Item response theory to quantify longitudinal placebo and paliperidone effects on PANSS scores in schizophrenia. CPT Pharmacometrics Syst Pharmacol. 2017;6(8):543–51.CrossRefPubMedPubMedCentralGoogle Scholar
  32. 32.
    Guk J, Chae D, Son H, Yoo J, Heo JH, Park K. Model-based assessment of the benefits and risks of recombinant tissue plasminogen activator treatment in acute ischaemic stroke. Br J Clin Pharmacol. 2018;84(11):2586–99.CrossRefPubMedPubMedCentralGoogle Scholar
  33. 33.
    Chae D, Park K. An item response theory based integrated model of headache, nausea, photophobia, and phonophobia in migraine patients. J Pharmacokinet Pharmacodyn. 2018;45(5):721–31.CrossRefGoogle Scholar
  34. 34.
    Baker FB. The basics of item response theory. ERIC; 2001.Google Scholar
  35. 35.
    Gomeni R, Lavergne A, Merlo-Pich E. Modelling placebo response in depression trials using a longitu-dinal model with informative dropout. Eur J Pharm Sci. 2009;36(1):4–10.CrossRefGoogle Scholar
  36. 36.
    Goodwin GM, Emsley R, Rembry S, Rouillon F. Agomelatine prevents relapse in patients with major depressive disorder without evidence of a discontinuation syndrome: a 24-week randomized, double-blind, placebo-controlled trial. J Clin Psychiatry. 2009;70(8):1128–37.CrossRefGoogle Scholar
  37. 37.
    Claghorn JL, Feighner JP. A double-blind comparison of paroxetine with imipramine in the long-term treatment of depression. J Clin Psychopharmacol. 1993;13:23S–7S.CrossRefGoogle Scholar
  38. 38.
    Mitsikostas DD, Mantonakis L, Chalarakis N. Nocebo in clinical trials for depression: a meta-analysis. Psychiatry Res. 2014;215(1):82–6.CrossRefGoogle Scholar
  39. 39.
    Mazumdar S, Tang G, Houck PR, Dew MA, Begley AE, Scott J, et al. Statistical analysis of longitudinal psychiatric data with dropouts. J Psychiatr Res. 2007;41(12):1032–41.CrossRefPubMedPubMedCentralGoogle Scholar
  40. 40.
    Little RJ. Modeling the drop-out mechanism in repeated-measures studies. J Am Stat Assoc. 1995;90(431):1112–21.CrossRefGoogle Scholar
  41. 41.
    Hu C, Sale ME. A joint model for nonlinear longitudinal data with informative dropout. J Pharmacokinet Pharmacodyn. 2003;30(1):83–103.CrossRefGoogle Scholar
  42. 42.
    Björnsson MA, Friberg LE, Simonsson US. Performance of nonlinear mixed effects models in the presence of informative dropout. AAPS J. 2015;17(1):245–55.CrossRefGoogle Scholar
  43. 43.
    Pierre Olié J, Kasper S. Efficacy of agomelatine, a MT1/MT2 receptor agonist with 5-HT2C antagonistic properties, in major depressive disorder. Int J Neuropsychopharmacol. 2007;10(5):661–73.CrossRefGoogle Scholar
  44. 44.
    Kennedy S, Emsley R. Placebo-controlled trial of agomelatine in the treatment of major depressive disorder. Eur Neuropsychopharmacol. 2006;16(2):93–100.CrossRefGoogle Scholar
  45. 45.
    Kennedy SH, Avedisova A, Giménez-Montesinos N, Belaïdi C, agomelatine study group, et al. A placebo-controlled study of three agomelatine dose regimens (10 mg, 25 mg, 25-50 mg) in patients with major depressive disorder. Eur Neuropsychopharmacol. 2014;24(4):553–63.CrossRefGoogle Scholar
  46. 46.
    Heun R, Ahokas A, Boyer P, Giménez-Montesinos N, Pontes-Soares F, Olivier V. The efficacy of agome-latine in elderly patients with recurrent major depressive disorder: a placebo-controlled study. J Clin Psychiatry. 2013;74(6):587–94.CrossRefGoogle Scholar
  47. 47.
    Jacqmin P, Snoeck E, Van Schaick E, Gieschke R, Pillai P, Steimer JL, et al. Modelling response time profiles in the absence of drug concentrations: definition and performance evaluation of the K-PD model. J Pharmacokinet Pharmacodyn. 2007;34(1):57–85.CrossRefGoogle Scholar
  48. 48.
    Cohen J. Statistical power analysis for the behavioral sciences. 2nd ed. Hillsdale: Erlbaum Associates; 1988.Google Scholar
  49. 49.
    Kjellsson MC, Zingmark PH, Jonsson EN, Karlsson MO. Comparison of proportional and differ-ential odds models for mixed-effects analysis of categorical data. J Pharmacokinet Pharmacodyn. 2008;35(5):483.CrossRefGoogle Scholar
  50. 50.
    Ueckert S. Modeling composite assessment data using item response theory. CPT Pharmacometrics Syst Pharmacol. 2018;7(4):205–18.CrossRefPubMedPubMedCentralGoogle Scholar
  51. 51.
    Gomeni R, Merlo-Pich E. Bayesian modelling and ROC analysis to predict placebo responders using clinical score measured in the initial weeks of treatment in depression trials. Br J Clin Pharmacol. 2007;63(5):595–613.CrossRefGoogle Scholar
  52. 52.
    Holford N, Li J, Benincosa L, Birath M. Population disease progress models for the time course of HAMD score in depressed patients receiving placebo in anti-depressant clinical trials. Abstracts of the XI annual meeting of the population approach group in Europe. 2002;Abstr. 311. Available from: www.page-meeting.org/?abstract=311. Accessed 13 Sept 2019.
  53. 53.
    Mould DR. Developing models of disease progression. In: Ette EI, Williams PJ, editors. Pharmacometrics: the science of quantitative pharmacology; 2007. p. 547–81.CrossRefGoogle Scholar
  54. 54.
    Shang EY, Gibbs MA, Landen JW, Krams M, Russell T, Denman NG, et al. Evaluation of struc-tural models to describe the effect of placebo upon the time course of major depressive disorder. J Pharmacokinet Pharmacodyn. 2009;36(1):63–80.CrossRefGoogle Scholar
  55. 55.
    Bauer JR, ICON S Development. NONMEM users guide: introduction to NONMEM 7.3.0. Maryland; 2013.Google Scholar
  56. 56.
    Russu A, Marostica E, De Nicolao G, Hooker AC, Poggesi I, Gomeni R, et al. Joint modeling of efficacy, dropout, and tolerability in flexible-dose trials: a case study in depression. Clin Pharmacol Ther. 2012;91(5):863–71.CrossRefGoogle Scholar
  57. 57.
    Papp M, Gruca P, Boyer PA, Mocaër E. Effect of agomelatine in the chronic mild stress model of depression in the rat. Neuropsychopharmacology. 2003;28(4):694.CrossRefGoogle Scholar
  58. 58.
    Singh SP, Singh V, Kar N. Efficacy of agomelatine in major depressive disorder: meta-analysis and appraisal. Int J Neuropsychopharmacol. 2012;15(3):417–28.CrossRefGoogle Scholar
  59. 59.
    Koesters M, Guaiana G, Cipriani A, Becker T, Barbui C. Agomelatine efficacy and acceptability revisited: systematic review and meta-analysis of published and unpublished randomised trials. Br J Psychiatry. 2013;203(3):179–87.CrossRefGoogle Scholar
  60. 60.
    Cusin C, Yang H, Yeung A, Fava M. Rating scales for depression. In: Handbook of clinical rating scales and assessment in psychiatry and mental health. Springer; 2009. p. 7–35.Google Scholar

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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