Combining patient-level and summary-level data for Alzheimer’s disease modeling and simulation: a beta regression meta-analysis
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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.
KeywordsAlzheimer’s disease Disease progression Beta regression Meta-analysis Logistic Longitudinal Bayesian CAMD ADNI
Critical Path Institutes Coalition Against Major Diseases is supported by grant No. U01FD003865 from the United States Food and Drug Administration and by Science Foundation Arizona under Grant No. SRG 0335-08. Data collection and sharing for the ADNI component of our combined data set was funded by the AD Neuroimaging Initiative (ADNI) (National Institutes of Health Grant U01 AG024904). ADNI is funded by the National Institute on Aging, the National Institute of Biomedical Imaging and Bioengineering, and through generous contributions from the following: Abbott, AstraZeneca AB, Bayer Schering Pharma AG, Bristol-Myers Squibb, Eisai Global Clinical Development, Elan Corporation, Genentech, GE Healthcare, GlaxoSmithKline, Innogenetics, Johnson and Johnson, Eli Lilly and Co., Medpace, Inc., Merck and Co., Inc., Novartis AG, Pfizer Inc, F. Hoffman-La Roche, Schering-Plough, Synarc, Inc., as well as non-profit partners the Alzheimer’s Association and Alzheimer’s Drug Discovery Foundation, with participation from the U.S. Food and Drug Administration. Private sector contributions to ADNI are facilitated by the Foundation for the National Institutes of Health (http://www.fnih.org). The grantee organization is the Northern California Institute for Research and Education, and the study is coordinated by the Alzheimer’s Disease Cooperative Study at the University of California, San Diego. ADNI data are disseminated by the Laboratory for Neuro Imaging at the University of California, Los Angeles. This research was also supported by NIH grants P30 AG010129, K01 AG030514, and the Dana Foundation. Data collection and sharing for this project was funded by the Alzheimer’s Disease Neuroimaging Initiative (ADNI) (National Institutes of Health Grant U01 AG024904). ADNI is funded by the National Institute on Aging, the National Institute of Biomedical Imaging and Bioengineering, and through generous contributions from the following: Abbott; Alzheimers Association; Alzheimers Drug Discovery Foundation; Amorfix Life Sciences Ltd.; AstraZeneca; Bayer HealthCare; BioClinica, Inc.; Biogen Idec Inc.; Bristol-Myers Squibb Company; Eisai Inc.; Elan Pharmaceuticals Inc.; Eli Lilly and Company; F. Hoffmann-La Roche Ltd and its affiliated company Genentech, Inc.; GE Healthcare; Innogenetics, N.V.; Janssen Alzheimer Immunotherapy Research & Development, LLC.; Johnson & Johnson Pharmaceutical Research & Development LLC.; Medpace, Inc.; Merck & Co., Inc.; Meso Scale Diagnostics, LLC.; Novartis Pharmaceuticals Corporation; Pfizer Inc.; Servier; Synarc Inc.; and Takeda Pharmaceutical Company. The Canadian Institutes of Health Research is providing funds to support ADNI clinical sites in Canada. Private sector contributions are facilitated by the Foundation for the National Institutes of Health (http://www.fnih.org). The grantee organization is the Northern California Institute for Research and Education, and the study is coordinated by the Alzheimer’s Disease Cooperative Study at the University of California, San Diego. ADNI data are disseminated by the Laboratory for Neuro Imaging at the University of California, Los Angeles. This research was also supported by NIH grants P30 AG010129, K01 AG030514, and the Dana Foundation.
- 1.Agresti A (1990) Categorical data analysis. Wiley, New YorkGoogle Scholar
- 5.Gelman A, Carlin JB, Stern HS, Rubin DB (2004) Bayesian data analysis. Chapman & Hall/CRC, New YorkGoogle Scholar
- 7.Gillespie WR, Rogers JA, Ito K, Gastonguay MR (2009) Population dose-response model for adas-cog scores in patients with alzheimers disease by meta-analysis of a mixture of summary and individual data. In: American Conference on Pharmacometrics. MashantucketGoogle Scholar
- 8.Holford NH, Peace KE (1992) Methodologic aspects of a population pharmacodynamic model for cognitive effects in alzheimer patients treated with tacrine. Proc Natl Acad Sci 89(23): 11,466–11,470 http://www.pnas.org/content/89/23/11466.abstract
- 10.Ito K, Corrigan B, Zhao Q, French J, Miller R, Soares H, Katz E, Nicholas T, Billing B, Anziano R, Fullerton T (2011) Alzheimer’s disease neuroimaging initiative: disease progression model for cognitive deterioration from alzheimer’s disease neuroimaging initiative database. Alzheimers Dement 7(2):151–160. doi: 10.1016/j.jalz.2010.03.018 CrossRefPubMedGoogle Scholar
- 14.Little RJA, Rubin DB (2002) Statistical analysis with missing data, 2nd edn. Wiley, New YorkGoogle Scholar
- 19.R Development Core Team (2008) R: a language and environment for statistical computing. R Foundation for Statistical Computing, Vienna. http://www.R-project.org. ISBN: 3-900051-07-0
- 21.Romero K, de Mars M, Frank D, Anthony M, Neville J, Kirby L, Smith K, Woosley RL (2009) The coalition against major diseases: developing tools for an integrated drug development process for Alzheimer’s and Parkinson’s diseases. Clin Pharmacol Ther 86(4):365–367. doi: 10.1038/clpt.2009.165 CrossRefPubMedGoogle Scholar
- 26.Williams-Faltaos D, Ying C, Wang Y, Gobburu J, Zhu H, Quantification of disease progression and drop-out for Alzheimer’s disease. Technical report, Food and Drug AdministrationGoogle Scholar