Developing and testing new methods for estimating treatment effectiveness in observational studies using high-dimension data / Zhiqiang Tan, Tobias Gerhard, Baoluo Sun.

Format:

Book

Author/Creator:

Tan, Zhiqiang, author.

Contributor:

Gerhard, Tobias, contributor.

Sun, Baoluo, contributor.

Series:

Final research report (Patient-Centered Outcomes Research Institute (U.S.))

Language:

English

Subjects (All):

Outcome assessment (Medical care).

Physical Description:

1 online resource (63 pages).

Place of Publication:

Washington, DC : Patient-Centered Outcomes Research Institute, 2022.

Summary:

BACKGROUND: Appropriate causal inference methods are required for comparative effectiveness research to produce valid and relevant findings from observational data. Two prominent classes of such methods are based on unconfoundedness or instrumental variable (IV) assumptions. Although extensive research has been done, it remains highly challenging to estimate propensity scores (PSs) and regression functions and to perform subsequent inference about average treatment effects (ATEs). The conventional approach employs an iterative process of model building and fitting, depending on ad hoc modeling choices, where statistical uncertainty is difficult to quantify. Recently, various methods have been proposed that apply off-the-shelf machine learning algorithms but either ignore statistical inference or invoke strong smoothness assumptions to justify consistent estimation of regression functions and subsequent statistical inference about treatment effects. OBJECTIVES: The objective of our research is to develop and evaluate a new set of statistically rigorous, numerically tractable, and pragmatic methods for drawing inferences about ATEs using PSs or IVs, while fitting PS and regression models with a large number of regressors. METHODS: We propose regularized calibrated estimation and model-assisted inferences about ATEs under the assumption of no unmeasured confounding or local ATEs under IV assumptions in high-dimensional settings. We derived numerical algorithms to implement the methods, conducted simulation studies to evaluate the methods, and investigated empirical applications of the proposed methods, compared with existing methods. RESULTS: The proposed methods are shown to yield valid statistical inference (ie, CIs and hypothesis testing) about treatment parameters under weak technical conditions in high-dimensional settings. Simulation studies and empirical applications demonstrate the advantages of the proposed methods compared with related methods. CONCLUSIONS: We developed new statistical methods and theory using PSs or IVs for causal inference. Using the proposed methods, PS and regression models can be fitted with a possibly large number of regressors, including main effects and interactions of the covariates, and CIs, and hypothesis tests can be obtained about treatment effects in a numerically tractable and statistically principled manner. Our methods are implemented in the publicly released R package RCAL. LIMITATIONS: Currently used methods handle cross-sectional studies and assume that the treatment and instrument are binary. It is desirable to extend our methods to handle multivalued treatments and instruments and to analyze longitudinal and survival data.

Contents:

Background

Patient and Stakeholder Engagement

Propensity Scores and Unconfounded Estimation

Instrumental Variables

Conclusions

References

Related Publications

Acknowledgment.

Notes:

Description based on publisher supplied metadata and other sources.