Just a moment, the page is loading...

Assessing and Reporting Heterogeneity of Treatment Effect in Randomized Clinical Trials

Assessing and Reporting Heterogeneity of Treatment Effect in Randomized Clinical Trials

David M. Kent, MD, MSc

Institute for Clinical Research and Health Policy Studies, Tufts Medical CentBoston, MAUSA

Patient-Centered Outcomes Research Institute (PCORI)


29 October 2013

A fundamental contradiction of evidence-based medicine is that evidence comes from groups, whereas medicine is applied to individuals. Making inferences to individuals based on effects measured in groups can sometimes be misleading. This project will directly address this issue in the context of randomized clinical trials, so that trials might more directly provide information to doctors and patients based on a patient's individual characteristics rather than the overall average effects seen in the summary results.

The patient will become better informed on treatment options and how they relate to health outcomes at a personal level based on their own individual characteristics. Consistent with the goals of PCORI, this research hopes to provide a framework for research that can help a patient better answer: “Given my personal characteristics, conditions and preferences, what should I expect will happen to me” both with and without the treatment?

We propose an empirical evaluation for assessing variation in treatment effects in randomized clinical trials, so called "heterogeneity of treatment effect." Whereas a good doctor or clinician seeks to combine their expertise and experience with their knowledge of an individual patient's circumstances and needs, "evidence" for clinical practice is often derived on the average effects of treatments in large groups of patients. This research will address the very real incongruence between the overall effects of a treatment in a study population (the summary result of a trial) and information regarding the anticipated risks and benefits in an individual patient necessary to support patient-centered decision making.

We hypothesize that mathematical risk models can be used to help better understand which patients might get the most benefit from a new treatment. This approach contrasts with the usual method of subgroup analysis, in which patients are divided one-variable-at-a-time—for example, men versus women; older patients versus younger patients; those with diabetes versus those without. Using risk models allows us to examine patient groups that differ in multiple characteristics simultaneously. We will apply these novel methods across several dozen large randomized clinical trials.

We plan to publish our results in high impact journals, periodic press releases, and quarterly newsletters. Results will be presented separately across risk groups, so that results can be applied to the individual patient based on his/her unique set of patient characteristics and presence (or absence) of attributable risk factors. Risk-based analyses from clinical trials will identify and report low-risk patients with minimal potential benefit.

[{ "PostingID": 48, "Title": "GSK-AR1108888", "Description": "FondaparinUx Trial with Unfractionated Heparin (UFH) during Revascularization in Acute Coronary Syndromes (ACS) (FUTURA). A prospective study evaluating the safety of two regimens of adjunctive intravenous UFH during PCI in high risk patients with Unstable Angina/Non ST segment elevation myocardial infarction (UA/NSTEMI) initially treated with subcutaneous fondaparinux and referred for early coronary angiography (OASIS 8)

Medicine: fondaparinux sodium, Condition: Acute Coronary Syndrome, Phase: 4, Clinic" }]

Statistical Analysis Plan

Kent DM, Nelson J, Dahabreh IJ, Rothwell PM, Altman DG, Hayward RA. Risk and treatment effect heterogeneity: re-analysis of individual participant data from 32 large clinical trials. Int J Epidemiol. 2016 Jul 3. pii: dyw118. [Epub ahead of print] PMID: 27375287