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European Journal of Clinical Pharmacology

, Volume 68, Issue 8, pp 1157–1166 | Cite as

Placebo effect model in asthma clinical studies: longitudinal meta-analysis of forced expiratory volume in 1 second

  • Xipei Wang
  • Dewei Shang
  • Jakob Ribbing
  • Yupeng Ren
  • Chenhui Deng
  • Tianyan Zhou
  • Feng Guo
  • Wei Lu
Clinical Trial

Abstract

Objective

Our objective was to describe the time course of the placebo effect in asthma and quantitatively investigate the affective factors of the placebo effect for the placebo response simulation during the asthma clinical study design.

Methods

We conducted a systemic search of public data sources for the study-level forced expiratory volume in 1 second (FEV1) to build the placebo effect model for studies by oral or inhaled administrations simultaneously. The administration routes, types of inhalation device, mean patient age, mean male proportion, baseline FEV1, disease severity, year of publication, inhaled corticosteroid status during the treatment, and dropout rate were tested as covariates.

Results

There are 34 literature sources containing 178 mean values for FEV1 presenting the individual observations from about 3,703 patients. The exponential models adequately described the time course of placebo effect with the typical value of the maximum placebo effect (Pmax) of 0.060 L. Dropout rate incorporated in the residual error model and the disease severity (mild to moderate and moderate to severe) at baseline were covariates that remained in the final model.

Conclusions

The placebo effect is adequately described by an exponential model over time. By incorporating the dropout rate in the residual error model, the estimation precision was improved. The model could predict the placebo response profile in mild to severe asthmatic patients for the asthma clinical study design and could also be a structure model of the placebo effect for the pure drug effect evaluation in the asthma clinical trials.

Keywords

Asthma Placebo effect Longitudinal Meta-analysis FEV1 

Notes

Acknowledgements

The authors gratefully acknowledge support from Dr. Shanshan Bi (Peking University) and Dr. Guangli Ma [Pfizer (China) Research & Development Center]. This work was supported by the Ministry of Science and Technology of the People’s Republic of China (Peking University New Drug Research & Development Platform, grant number 2009ZX09301-010).

Conflict of interest

The authors declare that they have no conflict of interest.

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Copyright information

© Springer-Verlag 2012

Authors and Affiliations

  • Xipei Wang
    • 1
    • 2
  • Dewei Shang
    • 3
  • Jakob Ribbing
    • 4
    • 5
  • Yupeng Ren
    • 2
  • Chenhui Deng
    • 2
  • Tianyan Zhou
    • 1
    • 2
  • Feng Guo
    • 6
  • Wei Lu
    • 1
    • 2
  1. 1.The State Key Laboratory of Natural and Biomimetic DrugsPeking UniversityBeijingPeople’s Republic of China
  2. 2.Department of Pharmaceutics, School of Pharmaceutical SciencePeking UniversityBeijingPeople’s Republic of China
  3. 3.Laboratory of Clinical Psychopharmacology, Beijing Anding HospitalCapital Medical UniversityBeijingPeople’s Republic of China
  4. 4.Pharmacometrics, Clinical PharmacologyPfizer Global Research and DevelopmentSollentunaSweden
  5. 5.Pharmacometrics, Clinical PharmacologyPfizer Global Research and DevelopmentSandwichUK
  6. 6.Asia Clinical Pharmacology and PharmacometricsPfizer (China) Research & Development CenterShanghaiPeople’s Republic of China

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