Ask the Expert Webinar Series

Quantifying the Impacts of Clinical Intervention Programs Using Propensity Score Matching in SAS®

On-Demand • Cost: Complimentary

About the webinar

Population health analytics helps organizations better understand the impacts of their attempts to influence patient/member behaviors.

While randomized controlled trials are ideal to quantify the impacts of clinical intervention programs, often only historical clinical/claims data are available.

In this webinar, we’ll show you how propensity score matching methods can be applied within SAS to control for confounder biases often present in historical clinical/claims data.

This webinar is for those within health care payers and larger provider systems who want to quantify the impacts of novel clinical intervention programs.

It will be helpful to have a clear understanding of basic statistical concepts.

You should also understand how social determinants of health (SDoH) factors can lead to the presence of confounder biases.

You will learn:

  • The definition of propensity score matching.
  • How SAS tools can be used to help detect potential biases in cohort-based studies.
  • How PROC PSMATCH can be used to implement a propensity score matching algorithm.
  • How to determine the quality of your propensity score matching algorithm.
  • Beyond clinical intervention programs, what other applications in health care exist.

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About the Experts

Seth Lester
Senior Industry Consultant, SAS

Seth Lester specializes in actuarial methods for quantifying health care finance and risk, primarily for US-based commercial payer organizations. He is an Associate of the Society of Actuaries. Lester is passionate about evangelizing the risks and opportunities of modern-day analytics in the US health care space, as well as the importance of democratizing financial risk insights across all health care stakeholders – most importantly, patients.

Huiping Miao
Senior Analytics Software Tester, SAS

Huiping Miao is in the Statistics group at SAS, where she tests analytical products to ensure quality and numerical accuracy throughout the software development cycle. She also participates in education and consulting projects that solve real-world problems with analytical tools. Huiping holds a PhD in statistics from North Carolina State University. Her research background is in propensity score matching methods, generalized additive models, and variable selection. Huiping is an active member of the Caucus for Women in Statistics.