SCCM Resource Library-Using Propensity Scores for Causal Inference in Critical Care

Video Transcription

Video Summary

In this webcast, Dr. Todd Miano, an epidemiology professor at the University of Pennsylvania, discussed the use of propensity scores for causal inference in critical care research. He began by highlighting the challenge of determining cause and effect in clinical research and the limitations of observational studies in establishing causality. Dr. Miano then explained the concept of confounding and the role of propensity scores in addressing confounding. Propensity scores are the probability of receiving treatment given a patient's characteristics and are used to balance the distribution of covariates between treatment groups. Dr. Miano discussed various methods for applying propensity scores, including matching, weighting, stratification, and covariate adjustment. He emphasized the importance of examining covariate overlap and balance to ensure the validity of propensity score analysis. Dr. Miano also compared the advantages and limitations of propensity score analysis with multivariable regression, noting that propensity scores are more flexible in handling a large number of covariates and can provide a transparent evaluation of confounding. However, he cautioned that neither method can control for unmeasured confounders. In conclusion, propensity score analysis can be a valuable tool for addressing confounding in observational studies and offers researchers a way to make causal inferences.

Asset Subtitle

Research, 2023

Asset Caption

The bias created by confounders significantly affects interpretation of outcome in observational studies. The webcast from Discovery, the Critical Care Research Network, will:

Analyze the potential impact of confounders on the interpretation of outcomes in observational studies
Evaluate the effectiveness of covariate adjustment using multivariable regression models in controlling for confounders in observational studies
Create a plan to apply propensity score methods to match individuals in the control group to the exposure group based on equal propensity scores

Meta Tag

Content Type Webcast

Knowledge Area Research

Membership Level Professional

Membership Level Select

Tag Research

Tag Clinical Research Design

Year 2023

Keywords

webcast

Dr. Todd Miano

epidemiology professor

University of Pennsylvania

propensity scores

causal inference

critical care research

observational studies

confounding

treatment groups

covariates

	Society of Critical Care Medicine 500 Midway Drive Mount Prospect, IL 60056 USA Phone: +1 847 827-6888 Fax: +1 847 439-7226 Email: support@sccm.org	Contact Us About SCCM Newsroom Advertising & Sponsorship DONATE	MySCCM LearnICU Patients & Families Surviving Sepsis Campaign Critical Care Societies Collaborative	GET OUR NEWSLETTER
© Society of Critical Care Medicine. All rights reserved. \| Privacy Statement \| Terms & Conditions The Society of Critical Care Medicine, SCCM, and Critical Care Congress are registered trademarks of the Society of Critical Care Medicine.