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Approving cancer treatments based on endpoints other than overall survival: an analysis of historical data using the PACE Continuous Innovation Indicators™ (CII).

ABSTRACT: There is an active debate about the role that endpoints other than overall survival (OS) should play in the drug approval process. Yet the term 'surrogate endpoint' implies that OS is the only critical metric for regulatory approval of cancer treatments. We systematically analyzed the relationship between U.S. Food and Drug Administration (FDA) approval and publication of OS evidence to understand better the risks and benefits of delaying approval until OS evidence is available.Using the PACE Continuous Innovation Indicators (CII) platform, we analyzed the effects of cancer type, treatment goal, and year of approval on the lag time between FDA approval and publication of first significant OS finding for 53 treatments approved between 1952 and 2016 for 10 cancer types (n = 71 approved indications).Greater than 59% of treatments were approved before significant OS data for the approved indication were published. Of the drugs in the sample, 31% had lags between approval and first published OS evidence of 4 years or longer. The average number of years between approval and first OS evidence varied by cancer type and did not reliably predict the eventual amount of OS evidence accumulated.Striking the right balance between early access and minimizing risk is a central challenge for regulators worldwide. We illustrate that endpoints other than OS have long helped to provide timely access to new medicines, including many current standards of care. We found that many critical drugs are approved many years before OS data are published, and that OS may not be the most appropriate endpoint in some treatment contexts. Our examination of approved treatments without significant OS data suggests contexts where OS may not be the most relevant endpoint and highlights the importance of using a wide variety of fit-for-purpose evidence types in the approval process.

SUBMITTER: Brooks N

PROVIDER: S-EPMC5699106 | biostudies-literature | 2017

REPOSITORIES: biostudies-literature

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Approving cancer treatments based on endpoints other than overall survival: an analysis of historical data using the PACE Continuous Innovation Indicators™ (CII).

Brooks Neon N Campone Mario M Paddock Silvia S Shortenhaus Scott S Grainger David D Zummo Jacqueline J Thomas Samuel S Li Rose R

Drugs in context 20171115

<h4>Background</h4>There is an active debate about the role that endpoints other than overall survival (OS) should play in the drug approval process. Yet the term 'surrogate endpoint' implies that OS is the only critical metric for regulatory approval of cancer treatments. We systematically analyzed the relationship between U.S. Food and Drug Administration (FDA) approval and publication of OS evidence to understand better the risks and benefits of delaying approval until OS evidence is availabl ...[more]

PMID: 29167693

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Project description:BackgroundEndpoint choice for randomized controlled trials of treatments for novel coronavirus-induced disease (COVID-19) is complex. Trials must start rapidly to identify treatments that can be used as part of the outbreak response, in the midst of considerable uncertainty and limited information. COVID-19 presentation is heterogeneous, ranging from mild disease that improves within days to critical disease that can last weeks to over a month and can end in death. While improvement in mortality would provide unquestionable evidence about the clinical significance of a treatment, sample sizes for a study evaluating mortality are large and may be impractical, particularly given a multitude of putative therapies to evaluate. Furthermore, patient states in between "cure" and "death" represent meaningful distinctions. Clinical severity scores have been proposed as an alternative. However, the appropriate summary measure for severity scores has been the subject of debate, particularly given the variable time course of COVID-19. Outcomes measured at fixed time points, such as a comparison of severity scores between treatment and control at day 14, may risk missing the time of clinical benefit. An endpoint such as time to improvement (or recovery) avoids the timing problem. However, some have argued that power losses will result from reducing the ordinal scale to a binary state of "recovered" versus "not recovered."MethodsWe evaluate statistical power for possible trial endpoints for COVID-19 treatment trials using simulation models and data from two recent COVID-19 treatment trials.ResultsPower for fixed time-point methods depends heavily on the time selected for evaluation. Time-to-event approaches have reasonable statistical power, even when compared with a fixed time-point method evaluated at the optimal time.DiscussionTime-to-event analysis methods have advantages in the COVID-19 setting, unless the optimal time for evaluating treatment effect is known in advance. Even when the optimal time is known, a time-to-event approach may increase power for interim analyses.

Dataset Information

Approving cancer treatments based on endpoints other than overall survival: an analysis of historical data using the PACE Continuous Innovation Indicators™ (CII).

Publications

Approving cancer treatments based on endpoints other than overall survival: an analysis of historical data using the PACE Continuous Innovation Indicators™ (CII).

Similar Datasets

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