our services

Data Science & Statistical Modelling

Rigorous quantitative analysis to reveal trends, test hypotheses, and inform decision-making

Data Science & Statistical Modelling

Capabilities

Increasingly, business decisions and outcomes are determined by data, and as data becomes more complex and multilayered, concise and rigorous analyses are essential. AHE’s service area in Data Science & Statistical Modelling is built on Avalon’s strength and experience in quantitative analysis across the health sciences, health economics, and health services research.  Data science and statistics are too often considered tools to test hypotheses. However, the analysis of data in a productive and meaningful way is more than just the application of the best tools; instead, it can be designed to reveal trends, patterns, and insights that can inform key clinical and managerial decision-making. The Avalon team has broad experience in study design, statistical modelling, hypothesis testing, and rigorous analysis of a wide variety of data, including clinical trial data, Medicare data, registry data, provider fee data, and claims data.

Specific capabilities include:

Statistical Modelling & Hypothesis Testing
Faq Icon
Causal Models & Attribution
Faq Icon
Biostatistics
Faq Icon
Epidemiology
Faq Icon
Probabilistic Decision Modelling
Faq Icon
Sampling & Sample Size Determination
Faq Icon
Descriptive Analysis
Faq Icon
Data Analysis with Machine Learning & Artificial Intelligence
Faq Icon
Advanced Statistical Programming in R, Stata, SAS, Python, etc
Faq Icon
Data Science & Statistical Modelling

Examples of Engagements

We serve a wide variety of clients throughout the U.S. and globally, incorporating advanced statistical modelling into engagements with life sciences companies, professional organizations, trade associations, law firms, non-profit organizations, and departments and agencies within city, state, and federal government. We have also applied our competencies in statistical modelling in litigation contexts.

Examples of engagements include:

  • Analysis of claims data to determine usual, customary, and reasonable (“UCR”)
  • Analysis of Medicare data to conduct epidemiological analysis of health conditions
  • Mendelian randomization analysis of health system and gene data to study causal factors in obesity
  • Instrumental variable analysis of the effects of market entry on healthcare expenditures
  • Panel data analysis of causal effects of events and key factors
  • Optimal use of R coding of models of economic evaluation
  • Panel data analysis of the effects of changes in regulatory policies on industry operating costs
  • Writing white papers and peer-reviewed publications
Data Science & Statistical Modelling

Mendelian Randomization: Estimation of Inpatient Hospital Costs Attributable to Obesity

An Avalon study by K. Dick, J. Schneider, A. Briggs and others (Health Economics Review, 2021) developed a cost attribution model using Mendelian randomization and instrumental variables (IV) depicting inherited genetic variants as instruments to estimate causal effects attributable to genetic factors. This study estimated the impact of obesity on annual inpatient healthcare costs in the UK using linked data from the UK Biobank and Hospital Episode Statistics (HES). UK Biobank data for 482,127 subjects was linked with HES inpatient admission records, and costs were assigned to episodes of care. A two-stage least squares (TSLS) IV model and a TSLS two-part cost model were compared to a naïve regression of inpatient healthcare costs on body mass index (BMI).

Read more...
Data Science & Statistical Modelling

Sample Publications