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Beyond Composite Endpoints Analysis: Semicompeting Risks as an Underutilized Framework for Cancer Research.

ABSTRACT: Composite endpoints (CEP), such as progression-free survival, are commonly used in cancer research. Notwithstanding their popularity, however, CEP analyses suffer from a number of drawbacks, especially when death is combined with a nonterminal event (ie, progression or recurrence), exemplifying the semicompeting risks setting. We investigated the semicompeting risks framework as a complementary analysis strategy that avoids certain drawbacks of CEPs.The illness-death model under the semicompeting risks framework was compared with standard analysis approaches: CEP analyses and (separate) univariate analyses for each component endpoint. Data from a previously published phase III randomized clinical trial in metastatic colon cancer including 1419 participants in the N9741 trial (conducted between 1997 and 2003) were used to determine the impact of the loss of information associated with combining multiple endpoints, as well as of ignoring the potentially informative role of death. A simulation study was conducted to further explore these issues.Failure to account for critical features of semicompeting risks data can lead to potentially severely misleading conclusions. Advantages of semicompeting risks analyses include a clear delineation of treatment effects on both events, the ability to draw conclusions about a patient's joint risk of the two events, and an assessment of the dependence between the two event types.Embedding and analyzing component outcomes in the semicompeting risks framework, either as a supplement or alternative to CEP analyses, represents an important, underutilized, and feasible opportunity for cancer research.

SUBMITTER: Jazic I

PROVIDER: S-EPMC5241896 | biostudies-other | 2016 Dec

REPOSITORIES: biostudies-other

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Beyond Composite Endpoints Analysis: Semicompeting Risks as an Underutilized Framework for Cancer Research.

Jazić Ina I Schrag Deborah D Sargent Daniel J DJ Haneuse Sebastien S

Journal of the National Cancer Institute 20160705 12

<h4>Background</h4>Composite endpoints (CEP), such as progression-free survival, are commonly used in cancer research. Notwithstanding their popularity, however, CEP analyses suffer from a number of drawbacks, especially when death is combined with a nonterminal event (ie, progression or recurrence), exemplifying the semicompeting risks setting. We investigated the semicompeting risks framework as a complementary analysis strategy that avoids certain drawbacks of CEPs.<h4>Methods</h4>The illness ...[more]

PMID: 27381741

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Project description:BackgroundComposite endpoints are recommended in rare diseases to increase power and/or to sufficiently capture complexity. Often, they are in the form of responder indices which contain a mixture of continuous and binary components. Analyses of these outcomes typically treat them as binary, thus only using the dichotomisations of continuous components. The augmented binary method offers a more efficient alternative and is therefore especially useful for rare diseases. Previous work has indicated the method may have poorer statistical properties when the sample size is small. Here we investigate small sample properties and implement small sample corrections.MethodsWe re-sample from a previous trial with sample sizes varying from 30 to 80. We apply the standard binary and augmented binary methods and determine the power, type I error rate, coverage and average confidence interval width for each of the estimators. We implement Firth's adjustment for the binary component models and a small sample variance correction for the generalized estimating equations, applying the small sample adjusted methods to each sub-sample as before for comparison.ResultsFor the log-odds treatment effect the power of the augmented binary method is 20-55% compared to 12-20% for the standard binary method. Both methods have approximately nominal type I error rates. The difference in response probabilities exhibit similar power but both unadjusted methods demonstrate type I error rates of 6-8%. The small sample corrected methods have approximately nominal type I error rates. On both scales, the reduction in average confidence interval width when using the adjusted augmented binary method is 17-18%. This is equivalent to requiring a 32% smaller sample size to achieve the same statistical power.ConclusionsThe augmented binary method with small sample corrections provides a substantial improvement for rare disease trials using composite endpoints. We recommend the use of the method for the primary analysis in relevant rare disease trials. We emphasise that the method should be used alongside other efforts in improving the quality of evidence generated from rare disease trials rather than replace them.

Project description:BackgroundWhen assessing the efficacy of a treatment in any clinical trial, it is recommended by the International Conference on Harmonisation to select a single meaningful endpoint. However, a single endpoint is often not sufficient to reflect the full clinical benefit of a treatment in multifaceted diseases, which is often the case in rare diseases. Therefore, the use of a combination of several clinically meaningful outcomes is preferred. Many methodologies that allow for combining outcomes in a so-called composite endpoint are however limited in a number of ways, not in the least in the number and type of outcomes that can be combined and in the poor small-sample properties. Moreover, patient reported outcomes, such as quality of life, often cannot be integrated in a composite analysis, in spite of their intrinsic value.ResultsRecently, a class of non-parametric generalized pairwise comparisons tests have been proposed, which members do allow for any number and type of outcomes, including patient reported outcomes. The class enjoys good small-sample properties. Moreover, this very flexible class of methods allows for prioritizing the outcomes by clinical severity, allows for matched designs and for adding a threshold of clinical relevance. Our aim is to introduce the generalized pairwise comparison ideas and concepts for rare disease clinical trial analysis, and demonstrate their benefit in a post-hoc analysis of a small-sample trial in epidermolysis bullosa. More precisely, we will include a patient relevant outcome (Quality of life), in a composite endpoint. This publication is part of the European Joint Programme on Rare Diseases (EJP RD) series on innovative methodologies for rare diseases clinical trials, which is based on the webinars presented within the educational activity of EJP RD. This publication covers the webinar topic on composite endpoints in rare diseases and includes participants' response to a questionnaire on this topic.ConclusionsGeneralized pairwise comparisons is a promising statistical methodology for evaluating any type of composite endpoints in rare disease trials and may allow a better evaluation of therapy efficacy including patients reported outcomes in addition to outcomes related to the diseases signs and symptoms.

Project description:ObjectivesMany guidelines and regulations allow children and adolescents to be enrolled in research without the prospect of clinical benefit when it poses minimal risk. However, few systematic methods exist to determine when research risks are minimal. This situation has led to significant variation in minimal risk judgments, raising concern that some children are not being adequately protected. To address this concern, we describe a new method for implementing the widely endorsed "risks of daily life" standard for minimal risk. This standard defines research risks as minimal when they do not exceed the risks posed by daily life activities or routine examinations.MethodsThis study employed a conceptual and normative analysis, and use of an illustrative example.ResultsDifferent risks are composed of the same basic elements: Type, likelihood, and magnitude of harm. Hence, one can compare the risks of research and the risks of daily life by comparing the respective basic elements with each other. We use this insight to develop a systematic method, direct comparative analysis, for implementing the "risks of daily life" standard for minimal risk. The method offers a way of evaluating research procedures that pose the same types of risk as daily life activities, such as the risk of experiencing anxiety, stress, or other psychological harm. We thus illustrate how direct comparative analysis can be applied in practice by using it to evaluate whether the anxiety induced by a respiratory CO2 challenge poses minimal or greater than minimal risks in children and adolescents.ConclusionsDirect comparative analysis is a systematic method for applying the "risks of daily life" standard for minimal risk to research procedures that pose the same types of risk as daily life activities. It thereby offers a method to protect children and adolescents in research, while ensuring that important studies are not blocked because of unwarranted concerns about research risks.

Dataset Information

Beyond Composite Endpoints Analysis: Semicompeting Risks as an Underutilized Framework for Cancer Research.

Publications

Beyond Composite Endpoints Analysis: Semicompeting Risks as an Underutilized Framework for Cancer Research.

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