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Please remember to cite the Shared Resources!

Research reported in this publication was supported by The Ohio State University Comprehensive Cancer Center and the National Institutes of Health under grant number P30 CA016058.

We thank the XX Shared Resource at The Ohio State University Comprehensive Cancer Center, Columbus, OH for (XX)

About Us: (back to top)

The Biostatistics Shared Resource (BSR) enables OSUCCC – James researchers to collaborate on all aspects of experimental design; grant proposal development; data management and statistical analysis of clinical, epidemiological, public health and laboratory research data; and manuscript preparation

250 Lincoln Tower
1800 Cannon Dr.
Columbus, OH 43210

Phone: 614-292-4778
Fax: 614-688-6600
biostatistics@osumc.edu

Availability: Monday-Friday, 8 a.m.-5 p.m.

For more information, please contact us at biostatistics@osumc.edu. If you need assistance with a specific project, please use our support request form.

Meet the Team: (back to top)

Soledad Fernandez, PhD - Director Soledad.Fernandez@osumc.edu

Dr. Fernandez manages and coordinates all services provided to OSUCCC members and establishes unit policies and priorities. Dr. Fernandez is the director of the Center for Biostatistics and distinguished professor in the Department of Biomedical Informatics, College of Medicine. She is the biostatistics director of one NCI P01 grant and serves as the co-director of the Biostatistics Program in Ohio State’s Center for Clinical and Translational Science. Dr. Fernandez has 18 years’ experience in collaborating with biomedical investigators on large studies, translational laboratory experiments and statistical genetics. Dr. Fernandez has been a part of the Ohio State community since 2001, serving as a visiting assistant professor in the Department of Statistics, where she taught undergraduate and graduate courses in the area of mathematical statistics and regression analysis for the engineering sciences. Prior to joining the university community, Dr. Fernandez received a MS in Statistics in 1998 and a joint PhD in Statistics and Animal Breeding and Genetics in 2001 from Iowa State University. She works in the planning, design and preparation of large programmatic grant and other OSUCCC proposals, and mentors new biostatisticians. She is an expert in statistical genetics, as well as in basic science research support.

Available Services: (back to top)

• Design and Analysis
• Clinical Trials
• High Dimensional Data Analysis Support
• New Methods

Click here for full list of services and fees

Design and Analysis

Center biostatisticians focus on study design and planning as their most critical contribution to collaboration. This requires frequent interactions that result in improvements in experimental design, convincing conclusions and revealing data analyses.

Our expertise in design and analysis includes:

• Multiple hypothesis testing strategies
• Laboratory experimental design
• Bioassay experiments
• Robust mixed modeling for experimental data with dependency structure
• Cancer control design and analysis
• Propensity score matching for observational study designs
• Complex modeling for longitudinal and other observational studies
• Re-estimation of sample size
• Evaluation of diagnostic techniques

Clinical Trials

Center biostatisticians work with OSUCCC – James principal investigators on clinical trials conducted in cancer prevention, detection, diagnosis and treatment.

Our expertise in clinical trials includes:

• Bayesian designs
• Interim analyses and monitoring
• Development of study design and decision rules
• Choice of outcome measures
• Sample size and power calculations
• Development of case report/data collection forms
• Analysis of correlative data including pathway analysis
• Chemoprevention trials
• Re-estimation of sample size
• Repeated measures designs including crossover and semi-crossover

High Dimensional Data Analysis Support

There are seven biostatisticians in the Center who have expertise in the design and analysis of studies with high dimensional data.

Our expertise in high dimensional data analysis support includes:

Statistical Methods:

• Technical Noise Filtering
• Sample normalization
• Robust variance estimation via hierarchal modeling
• Moderated T-test for differential expression
• Dimension reduction
• Data visualization
• Integration of multiple data types
• Clustering and classification
• Prognostic and diagnostic multivariate modeling
• Gene ontology and pathway analysis

Design Issues:

• Sample size to control power distribution
• Methods of controlling false discoveries
• Feature selection
• Biomarker discovery and validation

Data Type Experience:

• Bulk RNA-Seq
• Single-Cell RNA-Seq
• ChIP-Seq
• NanoString
• SNP/CNV
• GWAS
• Proteomics
• Microbiome
• Methylation array
• Affymetrix array
• Customized array

New Methods

Early Phase Clinical Trial Adaptive Design: Barriers with implementation of adaptive designs and development of statistical methods for adaptive phase I/II designs

Adaptive Sample Size:

Developed a re-estimation method that retains blinding of group assignment and allows for an increase in enrollment when the event rate (recurrence for one trial, death for the other) is lower than initially anticipated (Clinical Trials, 2010). This method provides blinded re-estimation and bootstrapped sample size distributions, which provides confidence in settling on a sample size near the end of planned accrual.

Adaptive Design for Laboratory Experiments:

Adapted the group sequential design to develop a novel approach for two-stage adaptive laboratory experiments for small sample sizes (Statistics in Biosciences, 2015). This method allows small sample size approach to designing a subsequent experiment that controls overall type I error and achieves sufficient conditional power.

Methods for Donor Cell Experiments:

Donor cell experiments involve repeated measures of the cells from the same donor receiving different treatments. The BSR has developed a method of robustly testing hypotheses in these experiments by using concepts from conditional mixed models to ensure unbiased estimation of variance and precise type I error control. This method avoids estimating a saturated co-variance structure, which is often computationally difficult due to small sample size, by defining appropriate variance components for specific contrasts of the fixed effects.

Mechanism Hypothesis Testing:

Many cell experiments involve up-and-down regulation to validate direct control of the target outcome. This requires significant differences for both up-and-down conditions vs. control. The BSR has identified a method of determining sample size that ensures, with chosen power, significance for both comparisons simultaneously. The same method is used when testing a combination of conditions against each condition alone. Similarly, the BSR is researching methods to draw broader conclusions from a series of experiments that add confidence to a hypothesis about pathway mechanisms compared to more limited conclusions for each experiment in a series.

Power Analysis for RNA-Seq Differential Expression Studies:

We developed a simulation-based procedure for power estimation using the negative binomial distribution for RNA-seq studies. This approach assumes a generalized linear model (at the gene level) that considers the dependence between gene expression level and its variance (dispersion) and also allows equal or unequal dispersion across conditions (BMC Bioinformatics, 2017).

Please remember to cite the Shared Resources!

Research reported in this publication was supported by The Ohio State University Comprehensive Cancer Center and the National Institutes of Health under grant number P30 CA016058.

We thank the XX Shared Resource at The Ohio State University Comprehensive Cancer Center, Columbus, OH for (XX)

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