So Many Choices: A Guide to Selecting Among Methods to Adjust for Observed Confounders

ABSTRACT Non‐randomised studies (NRS) typically assume that there are no differences in unobserved baseline characteristics between the treatment groups under comparison. Traditionally regression models have been deployed to estimate treatment effects adjusting for observed confounders but can lead...

Celý popis

Uložené v:
Podrobná bibliografia
Vydané v:Statistics in medicine Ročník 44; číslo 5; s. e10336 - n/a
Hlavní autori: Keele, Luke, Grieve, Richard
Médium: Journal Article
Jazyk:English
Vydavateľské údaje: Hoboken, USA John Wiley & Sons, Inc 28.02.2025
Wiley Subscription Services, Inc
Predmet:
Bias
confounders
Confounding Factors, Epidemiologic
Data Interpretation, Statistical
Humans
Machine Learning
Models, Statistical
statistical adjustment
Tutorial in Biostatistics
machine learning
statistical adjustment
confounders
ISSN:0277-6715, 1097-0258, 1097-0258
On-line prístup:Získať plný text
Tagy: Pridať tag
Žiadne tagy, Buďte prvý, kto otaguje tento záznam!
Popis
Shrnutí:ABSTRACT Non‐randomised studies (NRS) typically assume that there are no differences in unobserved baseline characteristics between the treatment groups under comparison. Traditionally regression models have been deployed to estimate treatment effects adjusting for observed confounders but can lead to biased estimates if the model is missspecified, by making incorrect functional form assumptions. A multitude of alternative methods have been developed which can reduce the risk of bias due to model misspecification. Investigators can now choose between many forms of matching, weighting, doubly robust, and machine learning methods. We review key concepts related to functional form assumptions and how those can contribute to bias from model misspecification. We then categorize the three frameworks for modeling treatment effects and the wide variety of estimation methods that can be applied to each framework. We consider why machine learning methods have been widely proposed for estimation and review the strengths and weaknesses of these approaches. We apply a range of these methods in re‐analyzing a landmark case study. In the application, we examine how several widely used methods may be subject to bias from model misspecification. We conclude with a set of recommendations for practice.
Bibliografia:ObjectType-Article-1
SourceType-Scholarly Journals-1
ObjectType-Feature-2
content type line 14
content type line 23
We thank seminar participants at Princeton University and the Online Causal Inference Seminar for useful feedback.
Funding: The authors received no specific funding for this work.
ISSN:0277-6715
1097-0258
1097-0258
DOI:10.1002/sim.10336