Recent Statistical Development for Comparative Effectiveness Research Beyond Propensity-Score Methods

Yixin Fang ()
Additional contact information
Yixin Fang: AbbVie, Data and Statistical Sciences

A chapter in Real-World Evidence in Medical Product Development, 2023, pp 193-210 from Springer

Abstract: Abstract Causal inference plays an important role in comparative effectiveness research using real-world studies. In this chapter, we review causal inference methods for deriving real-world evidence from the analysis of real-world data. We start with a brief review of four propensity-score methods. One of them, the inverse probability treatment weighting method, is a G-method that can be used to estimate the parameters in marginal structural models. G-methods (“G” stands for “generalized”) are methods that can be generalized to longitudinal studies with time-dependent confounders. Furthermore, we review two major classes of G-methods: one class utilizing the weighting strategy and the other class utilizing the standardization strategy. We first describe them for point-exposure studies and then generalize them to longitudinal studies. We conclude the chapter with some discussion on the application of these methods to real-world studies with intercurrent events.

Keywords: Confounding bias; Double robustness; Estimand; Estimator; Intercurrent events; G-methods; Propensity score (search for similar items in EconPapers)
Date: 2023
References: Add references at CitEc
Citations:

There are no downloads for this item, see the EconPapers FAQ for hints about obtaining it.

Related works:
This item may be available elsewhere in EconPapers: Search for items with the same title.

Export reference: BibTeX RIS (EndNote, ProCite, RefMan) HTML/Text

Persistent link: https://EconPapers.repec.org/RePEc:spr:sprchp:978-3-031-26328-6_11

Ordering information: This item can be ordered from
http://www.springer.com/9783031263286

DOI: 10.1007/978-3-031-26328-6_11

Access Statistics for this chapter

More chapters in Springer Books from Springer
Bibliographic data for series maintained by Sonal Shukla () and Springer Nature Abstracting and Indexing ().