Study design, data collection, data management and development, and application of statistical methodology are all crucial elements of clinical and translational research. The Biostatistics, Epidemiology, & Research Design (BERD) program provides comprehensive biostatistical and epidemiological support. These services include support for protocol development, statistical analysis plans, and sample size/power calculations. Research faculty are also available to solve novel innovative problems in the areas of design of experiments, clinical trials, and in longitudinal cohort studies.

A key goal of the BERD program is to provide value-added assistance to markedly improve the quality of the translational and clinical research of ACTSI investigators. This is accomplished by having highly trained, service oriented BERD personnel available to assist investigators in a timely manner

Recently Dr. Robert Lyles gave a presentation explaining BERD's services at the Department of Medicine's Research Resources 101 Series. Click here to view the presentation.

Research Design

The ACTSI biostatistics program provides consultation and collaboration in developing NIH grant applications. Program faculty assist investigators in identifying and understanding the scope and nature of their statistical problems, collaborate on study design and framing of hypotheses, assist in planning and budgeting for statistical support, and whenever appropriate, engage specialists among faculty and students of biostatistics as collaborators. Additionally, all investigators' protocols requesting Clinical Research Network (CRN) support receive a biostatistical consult during the CRN Scientific Advisory Council review. BERD can also match biostatistical trainees with investigators. This program helps investigators receive faster service from enthusiastic and well-trained biostatistical trainees. Biostatistical reviews and consults are critical in improving the validity and chances for success.

Statistical Considerations Section Should Have Distinct Subsections Addressing Each of the Following Issues:

Design: Summarize the study design and the appropriate terms such as randomized, double blind, crossover, controlled, comparative, and observational

Hypothesis: Describe the corresponding outcome measures and restate the primary hypothesis from the Specific Aims section in terms of a testable statistical hypothesis (i.e., explain how the outcome measure is expected to be affected by the components of the study design); Similar descriptions regarding secondary hypotheses are welcomed

Statistical Methods: Specify the planned statistical methods that will be used to analyze the primary and secondary outcome measures; Include statistical references if a nonstandard method will be used to analyze the primary hypothesis

Randomization: Specify how any randomization will be done, especially if it involves blocking or stratification to control for possible confounding factors; Specify the software package that will be used to implement the randomization plan

Sample Size: State the proposed sample size and estimate the statistical power related to testing the primary hypothesis described above; Outline how you arrived at this estimate in terms of the primary outcome measure; If for any reason a power calculation can not be performed, a detailed explanation is required

Technical Support: Who will supervise and perform the necessary statistical work? What computing resources will be used? In addition, please note the name/s and affiliation/s of the author/s of the Statistical Considerations Section if it was not completed by the DSL staff

Resources:

The following biostatistical services are available:

  • Rapid response to your statistical question via email
  • Review of proposals
  • Sample size and statistical power calculations
  • Study design consultation
  • Review of questionnaire/data collection form design statistical analysis
  • Pre-submission critique of statistical methods in journal manuscripts
  • Statistical genetics, genomics, and high-throughput data analysis
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