Combining endpoints that reflect memory, executive functioning, and language could capture cognitive deficits more completely. Yet problems in interpreting composite endpoints still arise, says Wessels. And data comparison or results comparison is very difficult if everyone is doing something different. Many researchers create their own composite endpoints by picking and choosing different items out of the three scales.
In , Schwartz analyzed a collection of studies from various fields that used composite endpoints BMJ , c Only one of 40 papers included a discussion of how the authors chose components of the endpoint; 13 of the papers had inconsistent definitions of their composite, making it unclear what outcomes were included. Moreover, among the 16 trials that had a statistically significant composite at the end, 11 misleadingly used language implying that the intervention affected an individual component of the composite.
Interested in reading more? Become a Member of. Receive full access to digital editions of The Scientist , as well as TS Digest , feature stories , more than 35 years of archives , and much more! However, the purported benefits must be diligently weighed against the inherent challenges in interpretation. Furthermore, the larger the gradient in importance, frequency, or results between the component endpoints, the less informative the composite endpoint becomes, thereby decreasing its utility for medical-decision making.
Conflicts of Interest : By the West JEM article submission agreement, all authors are required to disclose all affiliations, funding sources and financial or management relationships that could be perceived as potential sources of bias.
No author has professional or financial relationships with any companies that are relevant to this study. There are no conflicts of interest or sources of funding to declare. National Center for Biotechnology Information , U. West J Emerg Med. Published online Jun 4. Author information Article notes Copyright and License information Disclaimer.
Corresponding author. Address for Correspondence: C. Email: ude. This article has been cited by other articles in PMC. Abstract Clinicians, institutions, healthcare networks, and policymakers use outcomes reported in clinical trials as the basis for medical decision-making when managing individual patients or populations. Composite Endpoint Defined A composite endpoint consists of at least two or more distinct endpoints, called component endpoints.
Rationale for Using Composite Endpoints in Clinical Trials Benefits Combining two or more study outcomes into a single composite measure typically results in an increase in the incidence rates of the composite endpoint and improves the ability to detect differences in the primary endpoint. Challenges Interpretation of composite endpoints remains difficult, as there are no generally accepted standardized approaches to interpretation, and evaluating a composite endpoint as if it were a single primary endpoint is an inadequate strategy.
Interpreting Composite Endpoints The ultimate question that clinicians must answer when evaluating studies that use composite endpoints is whether or not the composite endpoint should be used as a basis for medical decision-making. Open in a separate window. The confidence clinicians can have regarding the similarity in relative risk reductions among the component endpoints can be evaluated with two questions: 1. SUMMARY Composite endpoints in clinical trials are composed of primary endpoints that contain two or more distinct component endpoints.
Bunker JP. The role of medical care in contributing to health improvements within societies. Int J Epidemiol. The Losartan Intervention For Endpoint reduction LIFE trial was a prospective double-blinded, randomized study that evaluated the effectiveness of a losartan-based vs. They also reported a decreased risk in the primary composite endpoint in the losartan group risk ratio [RR] [0.
Of the component endpoints, only the risk of stroke had a statistically significant reduction RR [0. The authors concluded that losartan prevents more cardiovascular morbidity and death than atenolol for a similar reduction in blood pressure, despite the lack of significant difference in death rates between the groups. The challenges for composite endpoint interpretation as well as the potential for widespread distribution of misleading study results is evidenced by the U. Food and Drug Administration restricting the regulatory labeling of the use of losartan for reduction of nonfatal stroke, as opposed to the original triple endpoint of death, MI, or stroke in the LIFE trial.
Composite endpoints in clinical trials are composed of primary endpoints that contain two or more distinct component endpoints.
The purported benefits include increased statistical efficiency, decrease in sample-size requirements, shorter trial duration, and decreased cost. However, the purported benefits must be diligently weighed against the inherent challenges in interpretation. Furthermore, the larger the gradient in importance, frequency, or results between the component endpoints, the less informative the composite endpoint becomes, thereby decreasing its utility for medical-decision making.
Address for Correspondence: C. Email: cmccoy uci. Conflicts of Interest : By the West JEM article submission agreement, all authors are required to disclose all affiliations, funding sources and financial or management relationships that could be perceived as potential sources of bias.
No author has professional or financial relationships with any companies that are relevant to this study. There are no conflicts of interest or sources of funding to declare. Bunker JP. The role of medical care in contributing to health improvements within societies. Int J Epidemiol. Causes of international increases in older age life expectancy. Finally, we should mention that the analysis of competing risk factors is not just confined to RCTs. In fact, this analysis strategy has been applied to cohort studies, for example in the field of AIDS.
As discussed previously, the use of CEPs doubtlessly has some advantages. However, if they are not analyzed in terms of the rationale behind their use, the interpretation of the effect of an intervention may be erroneous. The CEPs are, therefore, a double-edged sword that should be treated extremely carefully and with full awareness of the ambiguities which some studies fail to clarify.
Unfortunately, in the medical literature, it is often difficult to determine the rationale behind the use of CEPs in RCTs, particularly when the sponsor of a trial with a particular drug may prefer to focus on a positive result based on a CEP rather than to enter into debate about the precaution needed in the interpretation of the treatment effect. It is therefore up to the reader to evaluate the risk of spurious interpretation of the outcome of an intervention measured with a CEP.
The biggest risk occurs when a clearly positive effect is found for the CEP but when this effect is due mainly to a component of little clinical significance, whereas the effect for clinically significant components is null or even negative. Montori et al 5 have recently proposed guidelines for interpretation that aim to assess the risk of inaccurate interpretation of results based on CEPs. Although the reasons for using CEPs are not contemplated in these guidelines—a potential limitation to their use in some cases—they represent the first useful step forward for differentiating between clinical trials with a simple interpretation of results based on CEPs and those in which such an interpretation is more complex.
The guidelines pose 3 basic questions: Would the patients consider the components of the CEPs to be of similar importance? Were the frequencies of the different components similar? Were the effects of the intervention similar for each of the components? Our confidence in the assessment of the effect based on CEPs will be progressively eroded when we encounter larger differences in importance to the patients, frequency, and treatment effects.
We now present 2 illustrative examples of CEPs in which the risk of spurious interpretation is minimized first example and maximized second example. In this study, patients with cardiovascular risk factors were randomized to receive ramipril or placebo.
Table 1 shows the results of the intervention on the CEP and on each of its components. In the DREAM study, patients with no documented cardiovascular disease and glucose intolerance were randomized to rosiglitazone or placebo. The CEP "incident diabetes or death" was used. The results are presented in Table 2. The components included in a CEP should be of similar importance for the patients.
If this were not the case, erroneous conclusions could be reached through mixing very different results. If we analyze the heterogeneity of the CEP in terms of the importance of their components in previous examples, the difference is readily apparent. Whereas in the HOPE study there is a certain gradient in the importance of the components AMI or cerebrovascular accident, or cardiovascular death , this is much smaller than the one in the second example, where both components are very different in terms of importance to the patient incident diabetes or death.
Although this analysis is clearly subjective, it can serve as a first step towards classifying the most problematic cases which, it must be said, are not unheard of in the literature. The larger the variation in frequency of events of the different components in the control group, the greater the uncertainty about the applicability to these components of the effect of the intervention measured by CEP. While in the components with a high frequency of events, the precision of the estimator of effect will also be high, in those with low frequency of events, the uncertainty about that estimator will be much greater, and this will complicate the interpretation of the effect.
This strategy serves as a guide to distinguishing which situations are more problematic than others. The previous examples provide an illustrative example. While the distribution of events in the control group in the HOPE study varied between 4. It is important to examine the effect on the different components to look for the degree of variability among them. The degree of variability, if marked, indicates that the effects on the components of a CEP may vary greatly thereby bringing into question their combined evaluation.
As in the previous case, the estimator of the effect of the intervention on the components expressed in the form of relative risk or hazard ratio is relatively homogeneous in the HOPE study, ranging from 0.
Whereas in the first study we can affirm that the effect of the intervention on the CEP can be applied to the rest of its components, in the second study, this is not the case.
Combining the 3 previous questions, we can conclude that, whereas it is expected that the effect of the intervention on the CEP can be applied to its components in the HOPE study, in the DREAM study there is very strong uncertainty as to whether this applies. Furthermore, the most prudent inference that we can make with the DREAM study is that it is plausible that the intervention has a beneficial effect on the risk of incident diabetes.
In contrast, we cannot draw any conclusions about the "overall mortality" component. In order to explore the use of potentially problematic CEPs actually used in the cardiovascular field, a study was conducted of the RCTs published in high-impact journals that regularly include cardiovascular studies.
To do this, a systematic review of studies published in general medicine and cardiology journals with greatest impact factor in was conducted using the MEDLINE database. Journals covering the cardiovascular field but more centered on basic science were excluded for example, Circulation Research.
In addition, studies were excluded if a CEP was included but this was comprised solely of components related to the safety or toxicity of a drug or of paraclinical, or laboratory measures surrogate outcomes.
Likewise, group analyses that ignored random allocation were also excluded. The group of investigators involved in the classification resolved any discrepancies by discussion until a consensus was reached for the classification.
A total of potentially eligible RCTs were found. Of these, met the inclusion criteria and formed the sample for analysis. The same could be said for the frequency of events of the components. It was also shown that both the effect of the intervention and the frequency of events were dominated more often than not by less important components and that the effect on the most important components was clinically insignificant.
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