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Data Management and Statistics Core

Goals and Activities:

  • Provide coordinated and integrated data management capabilities for the ADRC across the Duke/UNC ADRC allowing easy access, tracking, and reporting capabilities for all of the cores.
  • Provide datasets to the National Alzheimer’s Disease Coordinating Center (NACC) and to other collaborative efforts in Alzheimer’s disease discovery and prevention.
  • Provide statistical expertise, consultation and collaboration for data analysis, study design, statistical power calculations, sampling strategies, and statistical prediction or modelling capabilities for the AD grants, projects, and pilots undertaken by the Center’s faculty, as well as for its cores and development projects.
  • Provide bioinformatics consultation to the Center cores and Duke/UNC scientists for management, integration and analysis of large datasets (omic, neuroimaging).
  • Develop innovative statistical and bioinformatics methodologies to meet the needs of the Center.
  • Provide educational opportunities and mentoring of investigators, trainees and junior faculty in collaboration with the Research Education Component on statistical and bioinformatics methodologies for Alzheimer’s disease research.DMS Core data flow diagram

    Available Resources:

    Statistical consulting expertise for scientists working on Alzheimer’s disease research. Contact: Sheng Luo, PhD.

    Bioinformatics consulting expertise for scientists working on Alzheimer’s disease research. Contact: Michael W. Lutz, PhD.

    The ARENA Gateway database for the Duke/UNC ADRC. A web-based graphical query interface displaying aggregated data on current ARENA participants. Contact: Michael W. Lutz, PhD or Syam Gadde.

    ADRC imaging data. Contact: Syam Gadde.


    Core Leaders:

    Sheng Luo, PhD

    Sheng Luo, PhD

    Professor of Biostatistics and Bioinformatics, Duke University
    Sheng.Luo@duke.edu
    (919) 668-8038

     

    Michael Lutz, PhD

    Michael W. Lutz, PhD

    Associate Professor in Neurology, Duke University
    Michael.Lutz@duke.edu
    (919) 660-7621

    Staff:


    Recent Publications:

    Han B, Chen H, Yao Y, Liu X, Nie C, Min J, Zeng Y, Lutz MW. Genetic and non-genetic factors associated with the phenotype of exceptional longevity & normal cognition. Sci Rep. 2020 Nov 5;10(1):19140. doi: 10.1038/s41598-020-75446-2. PMID: 33154391; PMCID: PMC7645680.

    Lutz MW, Luo S, Williamson DE, Chiba-Falek O. Shared genetic etiology underlying late-onset Alzheimer’s disease and posttraumatic stress syndrome. Alzheimers Dement. 2020 Sep;16(9):1280-1292. doi: 10.1002/alz.12128. Epub 2020 Jun 26. PMID: 32588970.

    Lutz MW, Sprague D, Barrera J, Chiba-Falek O. Shared genetic etiology underlying Alzheimer’s disease and major depressive disorder. Transl Psychiatry. 2020 Mar 9;10(1):88. doi: 10.1038/s41398-020-0769-y. PMID: 32152295; PMCID: PMC7062839.

    Lutz MW, Sprague D, Chiba-Falek O. Bioinformatics strategy to advance the interpretation of Alzheimer’s disease GWAS discoveries: The roads from association to causation. Alzheimers Dement. 2019 Aug;15(8):1048-1058. doi: 10.1016/j.jalz.2019.04.014. Epub 2019 Jun 28. PMID: 31262699; PMCID: PMC6699885.

    Lutz MW, Casanova R, Saldana S, Kuchibhatla M, Plassman BL, Hayden KM. Analysis of pleiotropic genetic effects on cognitive impairment, systemic inflammation, and plasma lipids in the Health and Retirement Study. Neurobiol Aging. 2019 Aug;80:173-186. doi: 10.1016/j.neurobiolaging.2018.10.028. Epub 2019 Mar 6. PMID: 31201950; PMCID: PMC7233428.

    Lin J, Li K, Luo S. Functional survival forests for multivariate longitudinal outcomes: Dynamic prediction of Alzheimer’s disease progression. Stat Methods Med Res. 2020 Jul 29:962280220941532. doi: 10.1177/0962280220941532. Epub ahead of print. PMID: 32726189.

    Li K, Luo S. Dynamic prediction of Alzheimer’s disease progression using features of multiple longitudinal outcomes and time-to-event data. Stat Med. 2019 Oct 30;38(24):4804-4818. doi: 10.1002/sim.8334. Epub 2019 Aug 6. PMID: 31386218; PMCID: PMC6800781.

    Li K, Luo S. Bayesian Functional Joint Models for Multivariate Longitudinal and Time-to-Event Data. Comput Stat Data Anal. 2019 Jan;129:14-29. doi: 10.1016/j.csda.2018.07.015. Epub 2018 Aug 16. PMID: 30559575; PMCID: PMC6294314.

    Wang J, Luo S. Joint modeling of multiple repeated measures and survival data using multidimensional latent trait linear mixed model. Stat Methods Med Res. 2019 Oct-Nov; 28(10-11):3392-3403. doi: 10.1177/0962280218802300. Epub 2018 Oct PMID: 30306833; PMCID: PMC6478574.

    Li K, O’Brien R, Lutz M, Luo S; Alzheimer’s Disease Neuroimaging Initiative. A prognostic model of Alzheimer’s disease relying on multiple longitudinal measures and time-to-event data. Alzheimers Dement. 2018 May;14(5):644-651. doi: 10.1016/j.jalz.2017.11.004. Epub 2018 Jan 4. PMID: 29306668; PMCID: PMC5938096.

    Zhang Y, Jin X, Lutz MW, Ju S, Liu K, Guo G, Zeng Y, Yao Y. Interaction between APOE ε4 and dietary protein intake on cognitive decline: A longitudinal cohort study. Clinical Nutrition. 2021 March; in press. https://doi.org/10.1016/j.clnu.2021.03.004 

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