Final Research Report

View this project's final research report.

Peer-Review Summary

Peer review of PCORI-funded research helps make sure the report presents complete, balanced, and useful information about the research. It also assesses how the project addressed PCORI’s Methodology Standards. During peer review, experts read a draft report of the research and provide comments about the report. These experts may include a scientist focused on the research topic, a specialist in research methods, a patient or caregiver, and a healthcare professional. These reviewers cannot have conflicts of interest with the study.

The peer reviewers point out where the draft report may need revision. For example, they may suggest ways to improve descriptions of the conduct of the study or to clarify the connection between results and conclusions. Sometimes, awardees revise their draft reports twice or more to address all of the reviewers’ comments. 

Peer reviewers commented and the researchers made changes or provided responses. Those comments and responses included the following:

  • The reviewers were generally laudatory about this methods-focused research project extending current statistical methods for causal inference when comparing two groups that were not randomly assigned.
  • Reviewers did question the researchers’ focus on linear models for predicting the effect of an intervention on treatment outcomes. They pointed out that in typical medical settings the relationship between the intervention and the outcomes is unlikely to be linear because of the effects of unmeasured variables on intervention effectiveness, outcome measurement, and other factors. The researchers explained that their use of generalized linear regression techniques in their models could capture certain aspects of nonlinearity in the relationship of two or more variables; extensions to models that are nonlinear in the parameters could be a subject for future investigations.
  • Reviewers noted that the simulation models do not appear to assess how violations in instrumental variable assumptions and the weakness of an instrumental variable affect study results. In particular, reviewers were concerned that weak instrumental variables (having low correlation with the intervention) that may be residually correlated with the outcome could increase bias in treatment effects. The researchers added language to the report to indicate that they would investigate weak instrumental variables in future research because it was beyond the scope of this project.

Conflict of Interest Disclosures

Project Information

Zhiqiang Tan, PhD
Rutgers, The State University of New Jersey, New Brunswick
Improving Causal Inference Methods via Statistical Learning with High-dimensional Data

Key Dates

July 2016
November 2021

Study Registration Information


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Last updated: April 13, 2022