The primary objective of the Biostatistics and Data Management Core (BDMC) of the CCNMD is to provideCCNMD investigators with expertise in study design and planning, project implementation and datamanagement, statistical methods and statistical analysis, including interpretation and dissemination ofresults, and consultation and education. The BDMC draws on the expertise of its two Ph.D. biostatisticians,its data manager, and other biostatisticians elsewhere in the Center, Department, and the University. Studydesign and planning includes focus of the research questions, production of testable hypotheses involvingmeasurable variables of use in testing the hypotheses, design of the studies, plans for statistical analysis,and power and sample size calculations. Project implementation includes service of the core director on theSteering Committee and Data Management includes forms design, quality control and testing of existingforms, design and testing of the data entry and data management systems, continuing consultation with andmeeting with project directors, quality control and quality assurance, and interim administrative reports. Itfurther includes statistical and data consultation if new issues arise during the implementation of the projects.Statistical methods include the detailed design of the statistical analyses of the data in each project, as wellas the design of new hypotheses to consider in each project, which depend on results in other projects. Datafrom each project may suggest hypotheses to test using data from the other projects. It further includesplanned statistical analyses of data from all projects, as well as exploratory analyses suggested by the data,which need to be conducted in a rigorous manner, which controls for multiple comparisons. Interpretationand dissemination of results includes working with investigators to ensure that interpretation of results arewell supported by the data, authoring or coauthoring the results sections of reports and manuscripts, andworking with investigators in writing discussion sections. Consultation and teaching include ongoingconsultation with project investigators in order to ensure that data are collected and entered in a timely andaccurate manner, and educating investigators, residents, and fellows about statistical methodological issuesimportant to these projects, and to psychiatric and other medical research projects more generally. BDMCpersonnel will present a series of seminars on research methodology and statistical methods toinvestigators, residents, fellows, and students.
Lyu, Ilwoo; Kim, Sun Hyung; Girault, Jessica B et al. (2018) A cortical shape-adaptive approach to local gyrification index. Med Image Anal 48:244-258 |
Stephens, Rebecca L; Langworthy, Benjamin; Short, Sarah J et al. (2018) Verbal and nonverbal predictors of executive function in early childhood. J Cogn Dev 19:182-200 |
Girault, Jessica B; Langworthy, Benjamin W; Goldman, Barbara D et al. (2018) The Predictive Value of Developmental Assessments at 1 and 2 for Intelligence Quotients at 6. Intelligence 68:58-65 |
Tu, Liyun; Styner, Martin; Vicory, Jared et al. (2018) Skeletal Shape Correspondence Through Entropy. IEEE Trans Med Imaging 37:1-11 |
Jha, Shaili C; Xia, Kai; Schmitt, James Eric et al. (2018) Genetic influences on neonatal cortical thickness and surface area. Hum Brain Mapp 39:4998-5013 |
Wang, Yan; Ma, Guangkai; An, Le et al. (2017) Semisupervised Tripled Dictionary Learning for Standard-Dose PET Image Prediction Using Low-Dose PET and Multimodal MRI. IEEE Trans Biomed Eng 64:569-579 |
Xia, K; Zhang, J; Ahn, M et al. (2017) Genome-wide association analysis identifies common variants influencing infant brain volumes. Transl Psychiatry 7:e1188 |
Sadeghi, Neda; Gilmore, John H; Gerig, Guido (2017) Twin-singleton developmental study of brain white matter anatomy. Hum Brain Mapp 38:1009-1024 |
Wei, Lifang; Cao, Xiaohuan; Wang, Zhensong et al. (2017) Learning-based deformable registration for infant MRI by integrating random forest with auto-context model. Med Phys 44:6289-6303 |
Gao, Wei; Lin, Weili; Grewen, Karen et al. (2017) Functional Connectivity of the Infant Human Brain: Plastic and Modifiable. Neuroscientist 23:169-184 |
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