Biostatistics & Statistical Programming
Deliver advanced statistical analysis and programming support using industry-leading methodologies and platforms.
Advanced Analytics for Healthcare Research
Reliable evidence depends on rigorous statistical planning and execution.
Methodological Expertise
Survival Analysis
- Kaplan-Meier Analysis
- Cox Regression
- Competing Risks Models
Comparative Effectiveness Methods
- Propensity Score Matching
- IPTW
- Matching Techniques
Longitudinal Analysis
- MMRM Models
- GEE Models
- Mixed Effects Methods
Advanced Analytics
- Predictive Modeling
- Machine Learning
- Risk Stratification
- Patient Segmentation
Programming Capabilities
- SAS
- R
- Python
- SQL
Whether you need support with evidence generation, epidemiology, outcomes research, health economics, or advanced analytics, York HEOR Group is ready to help.
Deliverables
- SAP Development
- TLF Production
- Validation Programming
- Statistical Reports
- Publication Tables & Figures
Related Services
Conduct scientifically rigorous retrospective and prospective observational studies.
Demonstrate clinical and economic value to payers, health technology assessment bodies, and healthcare decision-makers.
Identify, evaluate, and integrate the right data sources for your research objectives.
Frequently Asked Questions
What is propensity score matching?
It pairs treated and untreated patients with a similar probability of receiving treatment, so outcomes can be compared more fairly in observational data.
What is IPTW?
Inverse probability of treatment weighting weights each patient by how likely they were to receive their treatment, balancing groups while keeping all eligible patients.
What is survival analysis?
It analyzes the time until an event, such as death or progression, while accounting for patients still event-free at the end of follow-up. Kaplan-Meier and Cox regression are common methods.
What is a statistical analysis plan?
A document written before analysis that sets out the study populations, outcomes, statistical methods, missing data handling, and tables and figures to be produced.
What is causal inference in observational research?
It estimates the effect of a treatment rather than a simple association, using careful design and methods such as propensity scores, weighting, and sensitivity analyses.
Let's Discuss Your Research Goals
Schedule a Consultation Today and learn how York HEOR Group can transform your healthcare data into evidence-driven decisions.