Rigorous quantitative analysis to reveal trends, test hypotheses, and inform decision-making
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:
Employing a wide array of state-of-the-art statistical tools, such as regression analysis, to solve complex quantitative problems.
Employing data analysis to explore issues related to attribution, causation, net impact, etc.
Statistical tools specifically designed to address biological and clinical issues, including odds ratios, hazard ratios, survival analysis, analysis of rates and proportions, epidemiology, and the design of clinical studies.
The study of the incidence and prevalence of diseases and conditions, and the associated determinants and causal factors.
Statistical methods applied to decision-making under uncertainty by assigning probabilities to potential outcomes, typically using statistical inference tools such as Monte Carlo simulations and Bayesian networks.
Determination of smallest sample size required to detect statistically significant differences among subgroups.
Cross-tabulations, analysis of rates and proportions, and narrative analysis of statistical information.
Machine learning driven by artificial intelligence creates smarter data analytics, models, and decision analysis.
Use of standard programming tools to enhance the capabilities and efficiencies of data analytics and decision modelling.
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:

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).
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