Journal of Pharmacokinetics and Pharmacodynamics

, Volume 39, Issue 5, pp 479–498

Combining patient-level and summary-level data for Alzheimer’s disease modeling and simulation: a beta regression meta-analysis

  • James A. Rogers
  • Daniel Polhamus
  • William R. Gillespie
  • Kaori Ito
  • Klaus Romero
  • Ruolun Qiu
  • Diane Stephenson
  • Marc R. Gastonguay
  • Brian Corrigan
Original Paper

DOI: 10.1007/s10928-012-9263-3

Cite this article as:
Rogers, J.A., Polhamus, D., Gillespie, W.R. et al. J Pharmacokinet Pharmacodyn (2012) 39: 479. doi:10.1007/s10928-012-9263-3
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Abstract

Our objective was to develop a beta regression (BR) model to describe the longitudinal progression of the 11 item Alzheimer’s disease (AD) assessment scale cognitive subscale (ADAS-cog) in AD patients in both natural history and randomized clinical trial settings, utilizing both individual patient and summary level literature data. Patient data from the coalition against major diseases database (3,223 patients), the Alzheimer’s disease neruroimaging initiative study database (186 patients), and summary data from 73 literature references (representing 17,235 patients) were fit to a BR drug-disease-trial model. Treatment effects for currently available acetyl cholinesterase inhibitors, longitudinal changes in disease severity, dropout rate, placebo effect, and factors influencing these parameters were estimated in the model. Based on predictive checks and external validation, an adequate BR meta-analysis model for ADAS-cog using both summary-level and patient-level data was developed. Baseline ADAS-cog was estimated from baseline MMSE score. Disease progression was dependent on time, ApoE4 status, age, and gender. Study drop out was a function of time, baseline age, and baseline MMSE. The use of the BR constrained simulations to the 0–70 range of the ADAS-cog, even when residuals were incorporated. The model allows for simultaneous fitting of summary and patient level data, allowing for integration of all information available. A further advantage of the BR model is that it constrains values to the range of the original instrument for simulation purposes, in contrast to methodologies that provide appropriate constraints only for conditional expectations.

Keywords

Alzheimer’s disease Disease progression Beta regression Meta-analysis Logistic Longitudinal Bayesian CAMD ADNI 

Copyright information

© Springer Science+Business Media, LLC 2012

Authors and Affiliations

  • James A. Rogers
    • 1
  • Daniel Polhamus
    • 1
  • William R. Gillespie
    • 1
  • Kaori Ito
    • 2
  • Klaus Romero
    • 3
  • Ruolun Qiu
    • 2
  • Diane Stephenson
    • 3
  • Marc R. Gastonguay
    • 1
  • Brian Corrigan
    • 2
  1. 1.Metrum Research GroupTariffvilleUSA
  2. 2.Pfizer Global Research GroupGrotonUSA
  3. 3.Critical Path InstituteTucsonUSA

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