Composite Endpoints - Canny or cunning use of #healthoutcomes data?

You are on a guideline panel and you're at the PICO (Population - Intervention - Comparison - Outcome) stage of development. It's time to choose important outcomes.

A reminder - important outcomes are those important to the patients who are affected by the disease or condition. So for studies of diabetes, patient important outcomes are things like premature mortality or heart attack but not blood sugar levels. Lowering blood sugar levels is an intermediate step in the process to better health for diabetics, so would be considered a surrogate or intermediate outcome. This only indirectly measures what we are interested in, so the evidence wouldn't be rated as strong as the evidence for outcomes of direct importance.

So how do composite endpoints fit into this? What are composite endpoints? Composite endpoints (CEP) or composite outcomes are combined endpoints used in some clinical trials, particularly common in cardiology trials.

 According to a systematic review by Ferreira-Gonzalez et al, the most common reasons cited for using CEP are the smaller study size requirement and to evaluate the net effect of an intervention. Avoiding adjustments for multiple comparisons was also cited as a rationale for use. Disadvantages to using included misinterpretation when the components differed in patient importance or in size and direction of the effect.

A systematic review by Cordoba et al of 114 RCTs published in 2008 that used CEP found that changes in the definition of the composite outcome during the trials were common. Selection of components was often not pre-specified and definitions were inconsistently described throughout the study reports. Those trials also failed to report treatment effect for the individual components in a third of the publications. The less important components often had higher event rates and larger effects associated with treatment. Cordoba and colleagues recommended that "composite endpoints should generally be avoided, as their use leads to much confusion and bias. If composites are used, trialists should follow published guidance."

Fortunately, there is published guidance to direct decisions on how to create composite endpoints.  We can use this guidance to help us in determining whether or not composite endpoints may be valid and utilized in our guideline development.

Freemantle and colleagues use examples to demonstrate the problems with composite outcomes, including the presumption that the benefit described may be attributed to all the components when in fact, it is derived from only one component. The opposite also occurs; measures of a positive treatment effect for a critical outcome can be diluted by an outcome with no effect. And they provide data showing that CEP including clinician driven outcomes - where physicians order the intervention - were twice as likely to be associated with statistically significant results for the composite outcomes. Examples would include things like revascularization, hospitalization, and initiation of new therapy.

Montori and colleagues have produced an educational paper using examples to summarize three major considerations for evaluating the validity of composite endpoints. They are:

  1. Ensure that the component endpoints are of similar importance to patients. Most patients would not equate serious endpoints like death or heart attack with need for change in therapy.
  2. Ensure that the more and less important endpoints occur with similar frequency. If the more important events are uncommon (as is often the case for mortality) the composite measure is likely to be driven by the more common though less important events.
  3. Ensure that the component endpoints are likely to have similar risk reduction. Individual components should be similarly affected by the intervention.

There's another challenge when systematically collecting and summarizing the evidence on a given topic. Since CEP definitions frequently change, even within studies, it is very difficult to find standard definitions used across studies. This limits your ability to collect and combine the data from multiple studies for your guideline.

The easy answer for many guideline panels will be to simply exclude CEP from your outcome selections. But if you decide to consider their importance for your topic, you now have some guidance for evaluating that CEP.

And if you want to ponder that proposed benefit of using CEP to evaluate net effect by accounting for competing risks, I suggest you read this systematic review by Manja and colleagues

And though this very brief summary is directed at guideline developers, it wouldn't hurt trialists to learn a bit more about CEP.

TheEvidenceDoc August 7, 2017