Updates

Data Science Hub Biostatistics

We’re a team of statisticians, biostatisticians, data analysts, and researchers who apply rigorous statistical techniques to a wide variety of biological, medical, and public health research. We ensure the reliability and integrity of research findings by accounting for confounding factors and assessing the significance of observed effects, and we can also develop novel statistical methodologies as needed.

Meet our team

A recent case study

We recently developed and validated a Gastrointestinal Health Scale (GHS) tailored for MECP2 Duplication Syndrome (MDS), a rare neurogenetic disorder. The scale was created based on surveys from parents of MDS patients and refined through our statistical methods to ensure reliability and validity. The final GHS consists of 38 items across seven factors, providing a comprehensive tool for clinical trials and research. This validated scale is significant for its potential use in both clinical settings and translational studies that will drive disease-modifying treatments. The study was published in the Orphanet Journal of Rare Diseases in 2024.

Our tools

R (R Studio) — a powerful open-source statistical programming language commonly used for data analysis, visualization, and statistical modeling in biostatistics research.

SAS (Statistical Analysis System) — a software suite used for data management, advanced analytics, and statistical modeling. It’s widely employed in clinical trials and epidemiological studies within the field of biostatistics.

SPSS (Statistical Package for the Social Sciences) — user-friendly software for statistical analysis and data management.

STATA — a statistical software package favored for its efficiency in data management, analysis, and graphics. It’s particularly well-suited for handling large datasets common in biostatistics research.

Python — a versatile programming language with extensive libraries, such as pandas, NumPy, and SciPy. It’s widely used in biostatistics for data manipulation, statistical analysis, and machine learning applications.

G*Power — a software tool for statistical power analysis and sample size calculation. It helps in designing studies with appropriate statistical power to detect meaningful effects.

Minitab — a statistical software package with a comprehensive set of statistical tools for data analysis, quality improvement, and experimental design.

JASP —free and open-source statistical software offering a user-friendly interface for both frequentist and Bayesian analyses.

JAMOVI — open-source statistical software that’s frequently used in biostatistics for its simplicity and flexibility in data analysis and visualization.

Get in touch with us

Texas Children's Hospital researchers can submit a ticket here.

Baylor College of Medicine researchers can submit a ticket here.

Researchers from other institutions can contact us at researchdata@texaschildrens.org