Results Summary

What was the research about?

Clinical trials study the effects of medical treatments, like how safe they are and how well they work. But most clinical trials don’t get all the data they need from patients. Patients may not answer all questions on a survey, or they may drop out of a study after it has started. The missing data can affect researchers’ ability to detect the effects of treatments.

To address the problem of missing data, researchers can make different guesses based on why and how data are missing. Then they can look at results for each guess. If results based on different guesses are similar, researchers can have more confidence that the study results are accurate. In this study, the research team created new methods to do these tests and developed software that runs these tests.

What were the results?

The research team created software and tested it by analyzing missing data from three clinical trials. For example, one clinical trial looked at a medicine for bipolar disorder. More than half of patients withdrew from the trial, creating missing data. Using the software to run tests, the team found that missing data didn’t change the results of the trial.

What did the research team do?

The research team created new software to run the tests and made the software available online for free. The team also used this software on data from previous clinical trials to see if study results would differ depending on how the team handles missing data.

An advisory panel of 15 people in the fields of statistics, software development, and medicine helped solve technical problems when creating the software.

What were the limits of the study?

The software doesn’t work with data that have outliers. Outliers fall far outside the normal range of other data within a study. In addition, data from studies may not be available to the public, which makes it hard to test the software further.

Future research could create software that works with data with outliers.

How can people use the results?

Researchers can consider using the software to see if missing data affect their results.

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. The comments and responses included the following:

  • Reviewers expressed a concern about some of the methodological assumptions related to the statistical models developed in this study. The researchers disagreed that the assumptions could not be met, and therefore, there were major limitations on the study. However, they did acknowledge in the report that since these assumptions could impose restrictions on the data, they should be subjected to goodness-of-fit tests to make sure that the proposed approach is suitable.
  • Noting that the report lists advisory board members and gives selected examples of how they engaged in the study, reviewers suggested the authors also provide examples of  how each stakeholder group  engaged. The researchers indicated that the advisory board engaged on an ad hoc basis.  They reported successful and unsuccessful engagement in the report. For instance, the researchers worked with industry stakeholders on the advisory board to identify datasets that could be used in this study but ultimately used datasets provided through nonadvisory board industry connections.
  • Given that the authors received very little feedback about their new software, reviewers asked how the researchers planned to obtain feedback. The reviewers also asked the researchers to provide more support for their statements that the lack of uptake was due to a lack of incentives and a lack of understanding of new methods. The researchers explained that they were not planning on seeking further feedback at this time. They explained that their speculation about the reasons for lack of uptake came from a senior FDA official who indicated that staff did not have the time and resources to train to use the new software tool. The researchers also compared this lack of uptake to other recommended best practices in analyzing and reporting study results which have not been widely adopted, saying that these best practices would need to be requirements in order to improve the uptake.

Conflict of Interest Disclosures

Project Information

Daniel O. Scharfstein, ScD
Johns Hopkins University
$357,303 *
Sensitivity Analysis Tools for Clinical Trials with Missing Data

Key Dates

September 2013
February 2019

Study Registration Information

Final Research Report

View this project's final research report.

Journal Articles


Has Results
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Last updated: January 20, 2023