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How to Access

The best way to work with the DCC is to establish a long-term collaboration with a biostatistical co- investigator. Another common way to access the DCC for pre-award assistance is through the RCMI CC Research Development Team at 601-979-0332

For more information about our services, please email us at BOISTATS@ RCMI CC .COM or contact:
Jae Eun Lee Dr. PH.
DCC Biostatistics & Research Development Director,
Research Professor of Biostatistics at Jackson State University

Description of Services



Supporting grant proposal development

We help you to create grant proposal by providing description or consulting on study design, statistical plan, sample size calculation and justification, randomization scheme, and blinding plan.

We will provide a full-range of services in the early phase of the trial which include trial

conception (feasibility test), study design, sample size calculation and power analysis, randomization scheme, blinding scheme, matching scheme if needed, statistical analysis plan, and protocol/proposal development and/or review.


Supporting Research Implementation

The DCC B&RD will perform various efforts to prepare valid data for statistical analyses from the pre-stage of database building to the post-data collection session. Before building the database, critical variables will be decided and the annotated CRFs, data validation plan and database training plan will be reviewed and approved. During the research implementation, statistical quality control activities will be conducted by applying high level statistical methods to the accumulating data. The tools to be used are, but are not limited to, descriptive statistics, Shewart charts, plots, breakpoint regression, and recursive residuals with CUSUM and V-charts, which will be applied to measurement and event data. SAS macros will allow us to examine the quality of data through graphical outputs and various statistical outputs. The DCC statistician will play a leading role on the quality control committee (if applicable). All procedures for statistical quality control will be governed by the DCC SOP B-606 Statistical Quality Control. Before conducting data analyses, the entire data collected will be reviewed using the methods described above for the data locking and freezing processes. The reviewed data will be delivered to the PI(s) to obtain their final approval. Only data approved by the PI(s) will be analyzed for the final analyses.


Training Programs in statistics and study design

DCC B&RD has provided various biostatistical training programs including webinar seminars, on-site workshops and on- and off-site consulting programs.

More than 520 scientists and students have been benefited through these training programs.

Especially, the DTCC Biostatistical Mobile Clinic Collaborative Exchange (BMCCE) program which was offered at six RCMI CC sites (Howard University, University of Puerto Rico, University of Central Caribbean, Ponce School of Medicine and Howard University) on May-June, 2011 was a good opportunity for DTCC and sites to mutually understand what sites needed from DTCC and to develop potential collaboration for the future studies.


Statistical Support for Genotype Endpoints

The DCC will support statistical genetics and bioinformatics research with services ranging from primer design to phylogenetic analysis (e.g., migration, selection, and recombination). Analyses also include quantitative genetics modeling (e.g., linkage analysis, segregation analysis, identity-by-descent, additive polygenic models, linkage disequilibrium, and haplotype phase prediction and inference). Additionally, the DCC will assist in preparation of Illumina SAM/BAM files for GATK analysis (e.g., queue script for Next Generation Sequencing (NGS) processing, Iindel cleaning, duplicate marking and base score recalibration), generate volcano plots to identify under/over represented pathways, analyze copy number and loss of heterozygosity, perform multidimensional scaling for ordering markers within linkage groups, determine haplotype diversity, and create kernel density MA plots for microarray data. Statistical analysis includes basic inferential statistics, multivariate analysis, binary and Poisson regression models, non-parametric methods, group sequential boundary points, permutation-based micro-array analysis, and K-means clustering.