SCCM Resource Library-Causal Inference From Observational Data

Video Transcription

Video Summary

Dr. Michael Harhay gives a webcast on causal inference from observational data. He emphasizes the importance of having a clear causal question and understanding the subject matter. He introduces target trial emulation as a way to design observational studies that replicate randomized controlled trials. Confounding is discussed as differences between individuals in a study that can impact the outcome. Statistical modeling approaches to address confounding include multivariable regression, propensity score methods, and quasi-experimental methods. Dr. Harhay highlights the limitations, assumptions, and potential biases associated with each approach. He stresses the need for careful consideration of confounding variables and the use of tools like directed acyclic graphs to guide analyses. The transcript also mentions the challenges of including too many variables, the similarities and differences between propensity scores and multivariable regression, and the use of instrumental variables and difference-in-differences to control confounding. Sensitivity analysis, such as the E-value, is recommended to account for unmeasured confounding. Overall, the webcast provides an overview of causal inference methods and their application to observational data, emphasizing the importance of careful analysis and consideration of biases.

Asset Subtitle

Research, 2020

Asset Caption

Although observational studies are very common in the critical care literature, they are susceptible to many types of bias, making it difficult for critical care researchers and clinicians to interpret and apply the results to practice. This webinar will highlight the key steps involved in conducting rigorous observational critical care research. Topics to be covered include: an introduction to the potential outcomes framework and how this framework can be used to design an observational study, common sources of bias, strategies to control confounding (e.g., covariate adjustment, matching, propensity scores), approaches to confounder variable selection, including the use of directed acyclic graphs, and potential approaches to sensitivity analysis.

Meta Tag

Content Type Webcast

Knowledge Area Research

Knowledge Level Intermediate

Knowledge Level Advanced

Membership Level Select

Membership Level Professional

Membership Level Associate

Tag Outcomes Research

Year 2020

Keywords

causal inference

observational data

causal question

target trial emulation

confounding

multivariable regression

propensity score methods

quasi-experimental methods

directed acyclic graphs

sensitivity analysis

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