Biostatistics, Epidemiology, and Research Design (BERD) PAUL NIETERT, PHD SCTR LUNCH-N-LEARN...

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BERD: Aims for the Next 5 years  AIM 1: Provide methodological guidance to translational researchers.  AIM 2: Provide an innovative, specialized consultative service within the Clinical Trials Design Center, aimed at assisting investigators with the planning and design of multicenter trials.  AIM 3: Conduct research into novel biostatistical methodologies.

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Biostatistics, Epidemiology, and Research Design (BERD)PAUL NIETERT, PHDSCTR LUNCH-N-LEARNDECEMBER 9, 2015

The BERD TEAM

Madison Hyer, MS Beth Wolf, PhD Jeff Korte, PhD Paul Nietert, PhD Yuko Palesch, PhD Ramesh Ramakrishnan, PhD Amy Wahlquist, MS

We are all faculty member in the Department of Public Health Sciences.

BERD: Aims for the Next 5 years

AIM 1: Provide methodological guidance to translational researchers.

AIM 2: Provide an innovative, specialized consultative service within the Clinical Trials Design Center, aimed at assisting investigators with the planning and design of multicenter trials.

AIM 3: Conduct research into novel biostatistical methodologies.

BERD: Aims for the Next 5 years

AIM 4: Participate in the biostatistical educational development of trainees and junior investigators.

AIM 5: Review applications for internal SCTR funding for scientific merit, with emphasis on study design, statistical analysis plans and sample size justification.

AIM 1: Provide methodological guidance to translational researchers.

Investigators can make requests through SPARC.

We provide assistance with: Study design Sample size estimation / power analyses Statistical analysis plans Randomization

AIM 1: Provide methodological guidance to translational researchers.

We also provide assistance with: Data analyses*

Manuscript preparation*

* Priority given to SCTR trainees/scholars and pilot project awardees

AIM 2: Multisite Clinical Trials Design Consultative service

Investigators can make requests through SPARC. Consultations will be with M. Chimowitz and Y. Palesch We provide assistance with:

study design statistics and data management interaction with the government agencies involved in

clinical trials (NIH, CMS, and FDA) industry partners preparation of the trial budget implementation and coordination of funded trials

AIM 3: Conduct research into novel biostatistical methodologies.

Identifying relevant gene x gene and gene x environment interactions

Improving adoption/dissemination of novel biostatistical methods

AIM 4: Biostatistical educational development of trainees and junior investigators. Lectures within the MSCR and MD/PhD programs Lectures for clinicians covering basic concepts of

biostatistics Ad hoc lectures upon request One-on-one training upon request within our

biostatistics “clinics” Advice about on-line resources

AIM 5: Review applications for internal SCTR funding

Assist with the reviews of SCTR pilot projectsDoes the project have an adequate analysis

plan?Has the number of subjects been thoroughly

justified?Does the research team have the expertise

needed to conduct the analyses?Does the study design seem appropriate to

address the research question(s)?

SCTR Pilot Project Proposals – General Comments

Biostatisticians are, by nature, analytical. We like correct grammar .

When presenting error bars on graphs, make sure to provide a footnote that says what your error bars reflect (e.g. standard errors, standard deviations, confidence intervals).

Make sure to indicate how this project will help you plan the next project.

SCTR Pilot Project Proposals – Common Mistakes

Typical Errors in Analysis Plans:No analysis plan providedAnalysis plan is not appropriate for the proposed

studyToo generic (e.g. “Groups will be compared using

ANOVA.”)The required analyses are very complicated, but

the investigators don’t have the training to conduct the analyses.

SCTR Pilot Project Proposals – Common Mistakes

Typical Errors in Sample Size Justifications: No sample sizes are provided. No justification is provided. Statements like “We will use data from n=15 subjects,

because that is what is typically done with these types of studies.”

A power analysis is provided, but it is extremely vague and not reproducible.

A power analysis is provided, but it doesn’t match the study design.

Sample Size Justification

Keep in mind that generally speaking, there is more variability in humans than in mice.

Incorporate prior data into the power analysis if possible.

Consider whether your analyses and sample sizes need to account for multiple comparisons.

Key Take-Away Messages

We are here to help investigators get pilot projects and other grants funded.

We are happy to meet with investigators to help ensure their proposals are the best they can be.

Contact Information

Paul Nietert, PhD Director, SCTR Biostatistics, Epidemiology,

and Research Design Program nieterpj@musc.edu 843-876-1204

Questions?