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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. The comments and responses included the following:
- Reviewers requested additional information about the missingness models, simulations, and model assumptions. The reviewers noted that it was hard to keep track of the specifics of each simulation study and the results obtained. The researchers added an appendix including a table that summarizes the three main simulation studies, their assumptions, the methods evaluated, and the findings.
- Reviewers asked for additional details about the scenarios used to test the model simulations, to help readers better understand the limitations to the generalizability of these models. The researchers responded by adding discussion of the proportion of missing records and the missing data mechanism. However, they disagreed with the reviewers’ contention that the discrepancies between simulated and observed data limited the generalizability of their findings regarding performance of missing data methods.
- Reviewers noted that the simulations of time-dependent categorical variables did not appear to perform well and asked for additional discussion of the impact of this poor performance on conclusions that could be made from these simulations. The researchers acknowledged that the performance of the simulation tool was not optimal in generating covariates but did not consider this an important issue because the simulation of survival times was excellent. They also noted excellent recovery of true parameter estimates of the relationship between the time-dependent covariates and outcome, which are critical to gauging how well missing data methods handle time-varying covariates.
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