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Review of Meta-Analyses Evaluating Surrogate Endpoints for Overall Survival in Oncology OncoTargets and Therapy Dovepress open access to scientific and medical research Open Access Full Text Article REVIEW Review of meta-analyses evaluating surrogate endpoints for overall survival in oncology Beth Sherrill1 Abstract: Overall survival (OS) is the gold standard in measuring the treatment effect of new James A Kaye2 drug therapies for cancer. However, practical factors may preclude the collection of uncon- Rickard Sandin3 founded OS data, and surrogate endpoints are often used instead. Meta-analyses have been Joseph C Cappelleri4 widely used for the validation of surrogate endpoints, specifically in oncology. This research Connie Chen5 reviewed published meta-analyses on the types of surrogate measures used in oncology studies and examined the extent of correlation between surrogate endpoints and OS for different cancer 1RTI Health Solutions, Biometrics, types. A search was conducted in October 2010 to compile available published evidence in the Research Triangle Park, NC, USA; 2RTI Health Solutions, Epidemiology, English language for the validation of disease progression-related endpoints as surrogates of OS, Research Triangle Park, NC, USA; based on meta-analyses. We summarize published meta-analyses that quantified the correlation 3Pfizer, Global Outcomes Research Sollentuna, Sweden; 4Pfizer, between progression-based endpoints and OS for multiple advanced solid-tumor types. We also Biostatistics, Groton, CT, USA; discuss issues that affect the interpretation of these findings. Progression-free survival is the 5 Pfizer, Global Outcomes Research most commonly used surrogate measure in studies of advanced solid tumors, and correlation New York, NY, USA with OS is reported for a limited number of cancer types. Given the increased use of crossover in trials and the availability of second-/third-line treatment options available to patients after progression, it will become increasingly more difficult to establish correlation between effects on progression-free survival and OS in additional tumor types. Keywords: progression endpoints, correlation, cancer Introduction Rapid changes in our understanding of cancer biology and genetics, accompanied by the advent of newer targeted agents, are affecting every level of drug development, including molecule screening, development planning, study designs, regulatory deci- sion making, and reimbursement choices. Although overall survival (OS) remains the gold standard for assessing patient benefit from new drug therapies for cancer, practical factors may preclude the collection of unconfounded OS data. Showing a survival advantage of one treatment over another in cancer clinical trials can take years, and if patients take other treatments that improve survival after disease progression, attributing benefits confidently to a single agent or designing a feasible trial protocol with enough patients and duration of follow-up may not be possible. In addition, the length of survival post-progression may make it difficult to detect a survival advantage, even if one exists, due to the random variation associated with patient heterogeneity Correspondence: Beth Sherrill and the influences of subsequent therapy.1 An obvious need exists for well-defined 3040 Cornwallis Road, PO Box 12194, Research Triangle Park, NC, and valid measures of benefit from anticancer treatment that can be assessed earlier 27709-2194, USA in the course of the disease than patient death. Since approval and access to a new Tel +1 919 541 1233 Email [email protected] product hinges on successful Phase 3 clinical trial results, surrogate endpoints that submit your manuscript | www.dovepress.com OncoTargets and Therapy 2012:5 287–296 287 Dovepress © 2012 Sherrill et al, publisher and licensee Dove Medical Press Ltd. This is an Open Access article http://dx.doi.org/10.2147/OTT.S36683 which permits unrestricted noncommercial use, provided the original work is properly cited. Sherrill et al Dovepress could support earlier decision making would provide patients are somewhat atypical in that the objective is to establish the with new treatments sooner and reduce the costs of drug relationships between endpoints, rather than summariz- development, as has been seen in many other therapy areas ing treatment effects on a single endpoint. To accomplish (eg, HIV/AIDS and cardiovascular disease). this goal, investigators use a technique called meta-regression A surrogate endpoint in a clinical trial is “a substitute to model a treatment effect for survival against a treatment for a clinically meaningful endpoint that measures directly effect for the potential surrogate endpoint. For example, how a patient feels, functions or survives.”2 A surrogate based on individual-patient data or summary data from mul- endpoint must be clinically relevant, sensitive to treatment, tiple clinical trials, the hazard ratio (HR) for comparing two and measurable.3 Surrogates are particularly valuable for treatments on overall survival (HRos) can be regressed on drug development in diseases where increased patient sur- the hazard ratio for PFS (HRpfs), resulting in an equation vival is the goal of treatment, but a long time is required such as the following: to observe this endpoint directly. For example, in studies of antihypertensives, blood pressure reduction is gener- HRosi = µ + (ß × HRpfsi) + έ (1) ally accepted as a surrogate endpoint for the reduction of longer-term and more severe cardiovascular endpoints. In where µ represents an intercept, ß is the slope of the line general, justification for the use of a surrogate depends on showing the linear relationship of the hazard ratios, and έ is multiple considerations that vary depending on the disease the unexplained variance. In Equation (1), each study contrib- or specific cancer, drug mechanism of action, phase of utes one observation, typically weighted by the variance of development, patient subgroup, and availability of alternate the study-specific HR. Such an analysis expresses the rela- treatments. For example, response rate has a role in evalu- tionship between differences in effect sizes for progression ating the antitumor activity of new drugs in Phase 1 and and survival across multiple trials and gives an idea of how 2 studies, but it is not recognized as an endpoint showing strongly the endpoints are linked mathematically, assuming patient benefit in all tumors. This distinction is partly based a linear relationship. on the fact that the benefit of a partial tumor response is In other words, the meta-regression equation shows not necessarily outweighed by the toxicity associated with the predicted relationship between the hazard ratios for treatment; also, the proportion of patients responding is progression-free survival and overall survival, based on the not always a valid predictor of survival or other clinical studies included. If the slope (ß) of this equation equals 1, benefits.4 Time to progression (TTP), an endpoint that assuming a negligible intercept, the treatment effects on evaluates disease progression but censors deaths rather survival are expected to be of similar magnitude to effects than counting them as events, has fallen out of favor in on PFS. Models may address covariates or factors that can contemporary Phase 3 trials. Progression-free survival influence the endpoint relationship, and sometimes the meta- (PFS) is considered a more realistic assessment of treat- analysis is repeated on different patient subgroups or subsets ment efficacy, since it counts both progression and deaths of studies. Meta-regression equations take many different as part of the endpoint.5 forms in the published literature, depending on factors such For any stage in the drug development process, use of as which endpoint was evaluated, whether a transformation a surrogate endpoint rather than the target endpoint may (logarithm) was used, what statistical model was imple- shorten clinical trials but increase the chance of false positive mented, and how study weights were derived. Some authors results.6 Validation of surrogate endpoints is typically based model the difference between treatments in median months on the Prentice criterion,7 a set of conditions that specify to the event as the treatment effect, or analyze data from the the relationship between the treatment and endpoints under study arms separately. consideration. Changes in a surrogate endpoint that are Typically, authors present the simple correlation r between induced by a therapy are expected to reflect changes in a the treatment effect measures across trials. Correlation values clinically meaningful target endpoint. are close to one if the treatment effects tend to go in the same During the past decade, a body of work has devel- direction. In other words, correlation is high if the hazard oped that uses meta-analytic techniques to investigate ratios for PFS and OS are similar across trials; correlation progression-related endpoints as possible surrogates for over- is low if the hazard ratios are unrelated or in opposite direc- all survival in patients with solid tumors.8–12 The meta-analy- tions. A related measure (R2 or R-squared) is derived from ses conducted for surrogate endpoint validation in oncology the meta-regression equation to indicate how much variance 288 submit your manuscript | www.dovepress.com OncoTargets and Therapy 2012:5 Dovepress Dovepress Review of meta-analyses evaluating surrogate endpoints for overall survival in oncology in OS is explained by the potential surrogate PFS. In the very • TTP
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