Results Summary

What was the research about?

Sometimes, people with a certain trait respond differently to the same medicine. For example, women might, on average, benefit more from a medicine than men or have worse harmful side effects. If researchers group everyone together when they analyze study data, they may miss learning about which people are more likely to benefit or have harmful side effects from a medicine.

In this study, the research team grouped people based on their likelihood of having benefits or side effects from a certain medicine. The team used data from research studies of two medicines:

  • Pioglitazone treats diabetes in patients who have had a stroke but can increase the risk of breaking a bone.
  • Anthracycline treats women with breast cancer but can harm the heart.

What were the results?

Grouping data on people based on their likelihood of having benefits or side effects from each medicine showed differences in how the medicines affected them. The group of patients with a low risk of breaking a bone was more likely to benefit from pioglitazone. Patients in the group less likely to benefit from anthracycline had more heart problems.

What did the research team do?

The research team used statistical methods to analyze the data from two studies. The studies looked at benefits and side effects of medicines for different groups of people. The groups included patients with a

  • Low or high chance of benefitting from the medicines
  • Low or high chance of having side effects from the medicines

In each group, the team then looked at the actual benefits and side effects patients had from the medicines.

What were the limits of the study?

The research team used data from past studies. The studies didn’t include some health data, such as history of fracture and smoking, that could affect how well the medicines work or their side effects. Having this information might have changed how the team grouped the patients. Some groups of people may have been too small for the statistical methods to work well.

Future research could further explore ways of grouping patients to study differences in the effects of medicines.

How can people use the results?

Researchers can use the statistical methods to better understand how likely patients are to have benefits or harms from medicines.

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:

  • The reviewers generally found the report to be comprehensive and mostly requested clarifications on the statistical methods used and about the text in the figures. The researchers added details to the text and to figure captions.
  • A reviewer questioned the need to demonstrate that treatment would reduce risk less for a low-risk group than for a high-risk group. The researchers responded that high-risk patients are often but not always more likely to benefit from treatment.  They strongly disagreed with the suggestion that they did not need any analysis to assess how absolute treatment effects vary in relation to initial risk. The researchers added a section to the introduction to discuss the concept of heterogeneity of treatment effects. They also  added that there are strong theoretical reasons and good empirical evidence that treatment effects are not necessarily proportional across levels of risk. Therefore, the researchers argued that risk-stratified analyses are necessary to evaluate harm-benefit trade-offs.
  • A reviewer argued that the study’s stratification of risk, a continuous measure, into discrete groups was arbitrary. Each stratum encompassed a great deal of heterogeneity of risk. They stated that patients with risk scores closer to the middle might be more like patients in the other stratum, rather than patients within their own stratum, but at the other end of the risk score continuum. The researchers responded that it is customary to categorize patients into subgroups when analyzing heterogeneity of treatment effects in clinical trials. The researchers agreed that dividing patients into discrete groups has some disadvantages, but even so, using risk strata can help illustrate the clinical importance of differences in treatment effects. In the report, the researchers acknowledged the lack of consensus on how to optimally group continuous measures and suggested that this is an area warranting future methodological research.

Conflict of Interest Disclosures

Project Information

David Kent, MD, MS
Tufts Medical Center, Inc.
$493,086
10.25302/05.2020.RR.17050001
Predictive Analytics Pilot Study: Assessment of Heterogeneity of Treatment Effects in Two Major Clinical Trials

Key Dates

April 2017
June 2019
2017
2019

Study Registration Information

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Health Conditions Health Conditions These are the broad terms we use to categorize our funded research studies; specific diseases or conditions are included within the appropriate larger category. Note: not all of our funded projects focus on a single disease or condition; some touch on multiple diseases or conditions, research methods, or broader health system interventions. Such projects won’t be listed by a primary disease/condition and so won’t appear if you use this filter tool to find them. View Glossary
Populations Populations PCORI is interested in research that seeks to better understand how different clinical and health system options work for different people. These populations are frequently studied in our portfolio or identified as being of interest by our stakeholders. View Glossary
Intervention Strategy Intervention Strategies PCORI funds comparative clinical effectiveness research (CER) studies that compare two or more options or approaches to health care, or that compare different ways of delivering or receiving care. View Glossary
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Last updated: April 11, 2024