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Journal articles on the topic 'Surrogate endpoints'

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Author: Grafiati

Published: 4 June 2021

Last updated: 8 February 2022

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1

Banerjee, Buddhananda, and Atanu Biswas. "True endpoint reduction by surrogate endpoints." Communications in Statistics - Simulation and Computation 46, no. 8 (May 27, 2016): 6645–53. http://dx.doi.org/10.1080/03610918.2016.1171350.

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2

Ciani, Oriana, Bogdan Grigore, Hedwig Blommestein, Saskia de Groot, Meilin Möllenkamp, Stefan Rabbe, Rita Daubner-Bendes, and Rod S. Taylor. "Validity of Surrogate Endpoints and Their Impact on Coverage Recommendations: A Retrospective Analysis across International Health Technology Assessment Agencies." Medical Decision Making 41, no. 4 (March 10, 2021): 439–52. http://dx.doi.org/10.1177/0272989x21994553.

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Background Surrogate endpoints (i.e., intermediate endpoints intended to predict for patient-centered outcomes) are increasingly common. However, little is known about how surrogate evidence is handled in the context of health technology assessment (HTA). Objectives 1) To map methodologies for the validation of surrogate endpoints and 2) to determine their impact on acceptability of surrogates and coverage decisions made by HTA agencies. Methods We sought HTA reports where evaluation relied on a surrogate from 8 HTA agencies. We extracted data on the methods applied for surrogate validation. We assessed the level of agreement between agencies and fitted mixed-effects logistic regression models to test the impact of validation approaches on the agency’s acceptability of the surrogate endpoint and their coverage recommendation. Results Of the 124 included reports, 61 (49%) discussed the level of evidence to support the relationship between the surrogate and the patient-centered endpoint, 27 (22%) reported a correlation coefficient/association measure, and 40 (32%) quantified the expected effect on the patient-centered outcome. Overall, the surrogate endpoint was deemed acceptable in 49 (40%) reports ( k-coefficient 0.10, P = 0.004). Any consideration of the level of evidence was associated with accepting the surrogate endpoint as valid (odds ratio [OR], 4.60; 95% confidence interval [CI], 1.60–13.18, P = 0.005). However, we did not find strong evidence of an association between accepting the surrogate endpoint and agency coverage recommendation (OR, 0.71; 95% CI, 0.23–2.20; P = 0.55). Conclusions Handling of surrogate endpoint evidence in reports varied greatly across HTA agencies, with inconsistent consideration of the level of evidence and statistical validation. Our findings call for careful reconsideration of the issue of surrogacy and the need for harmonization of practices across international HTA agencies.

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3

Ciani, Oriana, Sarah Davis, Paul Tappenden, Ruth Garside, Ken Stein, Anna Cantrell, Everardo D. Saad, Marc Buyse, and Rod S. Taylor. "VALIDATION OF SURROGATE ENDPOINTS IN ADVANCED SOLID TUMORS: SYSTEMATIC REVIEW OF STATISTICAL METHODS, RESULTS, AND IMPLICATIONS FOR POLICY MAKERS." International Journal of Technology Assessment in Health Care 30, no. 3 (July 2014): 312–24. http://dx.doi.org/10.1017/s0266462314000300.

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Objectives: Licensing of, and coverage decisions on, new therapies should rely on evidence from patient-relevant endpoints such as overall survival (OS). Nevertheless, evidence from surrogate endpoints may also be useful, as it may not only expedite the regulatory approval of new therapies but also inform coverage decisions. It is, therefore, essential that candidate surrogate endpoints be properly validated. However, there is no consensus on statistical methods for such validation and on how the evidence thus derived should be applied by policy makers.Methods: We review current statistical approaches to surrogate-endpoint validation based on meta-analysis in various advanced-tumor settings. We assessed the suitability of two surrogates (progression-free survival [PFS] and time-to-progression [TTP]) using three current validation frameworks: Elston and Taylor's framework, the German Institute of Quality and Efficiency in Health Care's (IQWiG) framework and the Biomarker-Surrogacy Evaluation Schema (BSES3).Results: A wide variety of statistical methods have been used to assess surrogacy. The strength of the association between the two surrogates and OS was generally low. The level of evidence (observation-level versus treatment-level) available varied considerably by cancer type, by evaluation tools and was not always consistent even within one specific cancer type.Conclusions: Not in all solid tumors the treatment-level association between PFS or TTP and OS has been investigated. According to IQWiG's framework, only PFS achieved acceptable evidence of surrogacy in metastatic colorectal and ovarian cancer treated with cytotoxic agents. Our study emphasizes the challenges of surrogate-endpoint validation and the importance of building consensus on the development of evaluation frameworks.

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4

Ellenberg, SS. "Surrogate endpoints." British Journal of Cancer 68, no. 3 (September 1993): 457–59. http://dx.doi.org/10.1038/bjc.1993.369.

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5

Hughes, Michael D. "Evaluating surrogate endpoints." Controlled Clinical Trials 23, no. 6 (December 2002): 703–7. http://dx.doi.org/10.1016/s0197-2456(02)00264-7.

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6

Hahn, Andreas, Andreas Podbielski, Markus M. Heimesaat, Hagen Frickmann, and Philipp Warnke. "Binary surrogate endpoints in clinical trials from the perspective of case definitions." European Journal of Microbiology and Immunology 11, no. 1 (March 30, 2021): 18–22. http://dx.doi.org/10.1556/1886.2020.00031.

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AbstractIntroductionSurrogate endpoints are widely used in clinical trials, especially in situations where the endpoint of interest is not directly observable or to avoid long trial periods. A typical example for this case is frequently found in clinical trials in oncology, where overall survival (OS) as endpoint of interest and progression free survival (PFS) as surrogate endpoint are discriminated.MethodsBased on the perspective of case definitions on surrogate endpoints, we provide a formal definition of such endpoints followed by a description of the structure of surrogate endpoints.ResultsSurrogate endpoints can be considered as case definitions for the endpoint of interest. Therefore, the performance of surrogate endpoints can be described using the classical terminology of diagnostic tests including sensitivity and specificity. Since such endpoints always focus on sensitivity with necessarily reduced specificity, efficacy estimates based on such endpoints are in general biased.ConclusionThe abovementioned has to be taken into account while interpreting the results of clinical trials and should not be ignored while planning or conducting a study.

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7

Kuller, Lewis H. "Clinical trials: surrogate endpoints or hard endpoints?" American Journal of Cardiology 88, no. 2 (July 2001): 59–61. http://dx.doi.org/10.1016/s0002-9149(01)01786-6.

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8

&NA;. "Biomarkers and Surrogate Endpoints." American Journal of Therapeutics 6, no. 4 (July 1999): 179–80. http://dx.doi.org/10.1097/00045391-199907000-00001.

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9

Somberg, J. "Biomarker and Surrogate Endpoints." American Journal of Therapeutics 10, no. 4 (July 2003): 239–40. http://dx.doi.org/10.1097/00045391-200307000-00001.

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10

Furgerson, James L., William N. Hannah, and Jennifer C. Thompson. "Challenge of Surrogate Endpoints." Southern Medical Journal 105, no. 3 (March 2012): 156–60. http://dx.doi.org/10.1097/smj.0b013e318249891e.

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11

Aronson, J. K. "Biomarkers and surrogate endpoints." British Journal of Clinical Pharmacology 59, no. 5 (May 2005): 491–94. http://dx.doi.org/10.1111/j.1365-2125.2005.02435.x.

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12

Robb, Melissa A., Pamela M. McInnes, and Robert M. Califf. "Biomarkers and Surrogate Endpoints." JAMA 315, no. 11 (March 15, 2016): 1107. http://dx.doi.org/10.1001/jama.2016.2240.

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13

Srivastava, Sudhir, and John A. Wagner. "Surrogate Endpoints in Medicine." Disease Markers 18, no. 2 (2002): 39–40. http://dx.doi.org/10.1155/2002/182186.

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14

Rasnake, Crystal M., Paula R. Trumbo, and Therese M. Heinonen. "Surrogate endpoints and emerging surrogate endpoints for risk reduction of cardiovascular disease." Nutrition Reviews 66, no. 2 (February 4, 2008): 76–81. http://dx.doi.org/10.1111/j.1753-4887.2007.00010.x.

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15

Santosh Kumar, Rada, and Ganesh Sai Myneni. "SURROGATE ENDPOINT: ALTERNATIVE FOR EARLY ASSESSMENT OF A POTENTIAL TREATMENT EFFECT." Journal of Drug Delivery and Therapeutics 9, no. 4-s (August 29, 2019): 819–21. http://dx.doi.org/10.22270/jddt.v9i4-s.3371.

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The efficacy of health technologies, medicines and medical devices should be demonstrated in trails that evaluate final patient-relevant outcomes such as survival or morbidity. We provide a summary of the present use of surrogate end points in health care policy, discussing the case for and against their reviewing and adoption validation methods. Although the use of surrogates can be problematic, they can be validated and selected properly, offers important chances for more efficient clinical trials and faster access to new health technologies that benefit health care systems and patients. In early drug development studies, tumor response is often the true primary endpoint. Usually clinical trials are needed to show that it can be dependent upon to predict, or correlate with, clinical benefit in a context of use. Surrogate endpoints that have undergone this ample testing are called validated surrogate endpoints and these are accepted by the Food and Drug Administration as evidence of benefit. Choosing the right surrogate endpoint and proving that it can predict the intended clinical benefit, however, is not always straightforward. When a disease has been sufficiently studied, surrogate endpoints can measure the underlying cause of a disease (such as low thyroxine levels and hypothyroidism) or an effect that predicts the ultimate outcome (such as measuring diuresis, which is expected to improve symptoms of heart failure).

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16

Dobler, Claudia C., Rebecca L. Morgan, Yngve Falck-Ytter, Victor M. Montori, and M. Hassan Murad. "Assessing the validity of surrogate endpoints in the context of a controversy about the measurement of effectiveness of hepatitis C virus treatment." BMJ Evidence-Based Medicine 23, no. 2 (February 26, 2018): 50–53. http://dx.doi.org/10.1136/bmjebm-2017-110852.

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Surrogate endpoints are often used in clinical trials, as they allow for indirect measures of outcomes (eg, shorter trials with less participants). Improvements in surrogate endpoints (eg, reduction in low density lipoprotein cholesterol, normalisation of glycated haemoglobin) achieved with an intervention are, however, not always associated with improvements in patient-important outcomes. The common tendency in evidence-based medicine is to view results based on surrogate endpoints as less certain than results based on long term, final patient-important outcomes and rate them as ‘lower quality evidence’. However, careful appraisal of the validity of a surrogate endpoint as a measure of the final, patient-important outcome is more useful than an automatic judgement. In this guide, we use a contemporary and currently highly debated example of the surrogate endpoint ‘sustained viral response’ (ie, viral eradication considered to represent successful treatment) in patients treated for chronic hepatitis C virus. We demonstrate how the validity of a surrogate endpoint can be critically appraised to assess the quality of the evidence (ie, the certainty in estimates) and the implications for decision-making.

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17

Royce, Trevor Joseph, Ming-Hui Chen, Jing Wu, Marian Loffredo, Andrew A. Renshaw, Philip W. Kantoff, and Anthony Victor D'Amico. "A comparison of surrogate endpoints for all cause mortality in men with localized unfavorable-risk prostate cancer." Journal of Clinical Oncology 35, no. 6_suppl (February 20, 2017): 21. http://dx.doi.org/10.1200/jco.2017.35.6_suppl.21.

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21 Background: Several surrogates for prostate cancer-specific mortality exist, but whether these are surrogates for all cause mortality (ACM), and how their performance compares, is unknown. We investigated the relative efficacy of 4 candidate surrogates for ACM using the proportion of treatment effect (PTE) metric. Methods: Two-hundred and six men with localized unfavorable-risk prostate cancer were randomized to radiation therapy (RT) or RT and 6 months of androgen-deprivation therapy (ADT) from 1995 to 2001 and followed for a median of 16.62 years. Among the 159 men with no or minimal comorbidity, a significant reduction in the risk of death was observed in those randomized to RT and ADT versus RT alone; these 159 men formed the study cohort. In order to assess whether the candidate surrogated satisfied Prentice’s criteria for surrogacy, Cox regression analyses were performed to assess the risk of death for each of the candidate surrogates and treatment before and after adjusting for prostate-specific antigen (PSA), age at randomization, T category, and Gleason score. Results: PSA nadir > 0.5 ng/mL, PSA doubling time < 9 months and interval to PSA failure < 30 months met Prentice’s criteria for surrogacy (P = 0.01, 0.003, and 0.03 for the surrogate covariate in the multivariable model, respectively) for ACM, while PSA failure did not (P = 0.10). For the three surrogates, the PTE values were 103.86%, 43.09%, and 41.26%, respectively. Conclusions: A PSA nadir value of > 0.5 ng/mL following RT and ADT identified men prior to PSA failure who were at high-risk for death and therefore could be used to select men for entry, at the time of PSA nadir and before PSA failure, onto randomized trials evaluating the impact on survival of salvage ADT with or without agents shown to prolong survival in men with castrate-resistant metastatic prostate cancer. By enriching study cohorts with men who have achieved a surrogate endpoint for ACM, one can enhance the likelihood that the study will be able to observe whether survival is prolonged when novel treatment is added to ADT, as compared to ADT alone, in an abbreviated time period. Clinical Trial Number: NCT00116220

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18

Weintraub, William S., Thomas F. Lüscher, and Stuart Pocock. "The perils of surrogate endpoints." European Heart Journal 36, no. 33 (May 13, 2015): 2212–18. http://dx.doi.org/10.1093/eurheartj/ehv164.

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19

Fleming, Thomas R. "Surrogate Endpoints in Clinical Trials." Drug Information Journal 30, no. 2 (April 1996): 545–51. http://dx.doi.org/10.1177/009286159603000230.

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20

Van Houwelingen, Hans C. "The Evaluation of Surrogate Endpoints." Biometrics 62, no. 3 (September 2006): 948–49. http://dx.doi.org/10.1111/j.1541-0420.2006.00588_12.x.

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21

Gilbert, Peter B., and Michael G. Hudgens. "Evaluating Candidate Principal Surrogate Endpoints." Biometrics 64, no. 4 (March 24, 2008): 1146–54. http://dx.doi.org/10.1111/j.1541-0420.2008.01014.x.

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22

Wortzel, Hal S., Peter M. Gutierrez, Beeta Y. Homaifar, Ryan E. Breshears, and Jeri E. Harwood. "Surrogate Endpoints in Suicide Research." Suicide and Life-Threatening Behavior 40, no. 5 (October 2010): 500–505. http://dx.doi.org/10.1521/suli.2010.40.5.500.

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23

Gelman, Simon. "Right, Wrong, and Surrogate Endpoints." Anesthesiology 82, no. 4 (April 1995): 1084. http://dx.doi.org/10.1097/00000542-199504000-00035.

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24

Khalil, Samia. "Right, Wrong, and Surrogate Endpoints." Anesthesiology 82, no. 4 (April 1995): 1084. http://dx.doi.org/10.1097/00000542-199504000-00036.

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25

Somberg, John. "Surrogate Endpoints and Drug Approval." American Journal of Therapeutics 13, no. 5 (September 2006): 388. http://dx.doi.org/10.1097/01.mjt.0000244278.30783.81.

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26

Koppman, Aaron F. "Surrogate Endpoints and Neuromuscular Recovery." Anesthesiology 87, no. 5 (November 1, 1997): 1029–31. http://dx.doi.org/10.1097/00000542-199711000-00001.

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27

Mamelok, Richard. "How controversial are surrogate endpoints?" Nature Biotechnology 12, no. 2 (February 1994): 134–35. http://dx.doi.org/10.1038/nbt0294-134.

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28

Friedman, Lawrence, and Salim Yusuf. "Surrogate endpoints in clinical trials." Controlled Clinical Trials 6, no. 3 (September 1985): 222. http://dx.doi.org/10.1016/0197-2456(85)90012-1.

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29

Qureshi, Mahin Iqbal, Matthew C. Cheung, Sierra Cheng, Di Maria Jiang, Erica McDonald, Vanessa Sarah Arciero, Doreen Anuli Ezeife, Alexandra Chambers, Kelley-Anne Sabarre, and Kelvin K. Chan. "Are surrogate endpoints unbiased metrics compared to hazard ratio for death? An evaluation of clinical benefit scores (CBS) in the American Society of Clinical Oncology (ASCO) value framework." Journal of Clinical Oncology 35, no. 15_suppl (May 20, 2017): 6600. http://dx.doi.org/10.1200/jco.2017.35.15_suppl.6600.

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6600 Background: Clinical benefit scores (CBS) are a key element of the American Society of Clinical Oncology (ASCO) value framework's Net Health Benefit valuation of cancer therapies. CBS are assigned based on a hierarchy of efficacy endpoints, from hazard ratio for death (HR OS), to median overall survival (mOS), HR for disease progression (HR PFS), median progression-free survival (mPFS), and response rate (RR). When HR OS is unavailable, other endpoints in the hierarchy are used as "surrogates" to calculate CBS via their scaling factors. We aim to examine whether surrogate-derived CBS offer unbiased scoring of clinical benefit compared to HR OS-derived CBS. Methods: CBS for advanced-disease settings were computed for randomized clinical trials (RCTs) of oncology drug approvals by the Food and Drug Administration, European Medicines Agency, and Health Canada, between 2006 and August 2015. Spearman's correlation assessed association between CBS derived from surrogates and HR OS. Mean bias (surrogate-derived CBS minus HR OS-derived CBS) evaluated the tendency for surrogate-derived CBS to over- or under- estimate clinical benefit. Mean absolute error (MAE), a measure of average deviation, assessed precision of surrogate-derived CBS in relation to HR OS-derived CBS. Results: Scored RCTs (n=104) yielded 69, 93, 88, and 89 paired CBS between HR OS and mOS, HR PFS, mPFS, and RR, respectively. See table for results. Restricting to RCTs reporting all endpoints (n=59) and RCTs without OS as primary endpoint (n=68) showed similar results. Conclusions: Findings suggest HR PFS-, mPFS-, and RR-derived CBS are poor "surrogates" as they are imprecise and weakly correlated to HR OS-derived CBS. HR PFS and particularly mPFS exhibit bias to overestimate CBS. [Table: see text]

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30

Feigin, Andrew. "Evidence from biomarkers and surrogate endpoints." NeuroRX 1, no. 3 (July 2004): 323–30. http://dx.doi.org/10.1602/neurorx.1.3.323.

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31

Sikora, Karol. "Surrogate endpoints in cancer drug development." Drug Discovery Today 7, no. 18 (September 2002): 951–56. http://dx.doi.org/10.1016/s1359-6446(02)02434-0.

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32

Xu, Jane, and Scott L. Zeger. "The Evaluation of Multiple Surrogate Endpoints." Biometrics 57, no. 1 (March 2001): 81–87. http://dx.doi.org/10.1111/j.0006-341x.2001.00081.x.

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33

Burzykowski, Tomasz. "Surrogate endpoints: wishful thinking or reality?" Statistical Methods in Medical Research 17, no. 5 (February 19, 2008): 463–66. http://dx.doi.org/10.1177/0962280207081866.

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34

Huang, Ying, and Peter B. Gilbert. "Comparing Biomarkers as Principal Surrogate Endpoints." Biometrics 67, no. 4 (April 22, 2011): 1442–51. http://dx.doi.org/10.1111/j.1541-0420.2011.01603.x.

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35

Daskalakis, C., and E. Shenassa. "Inference about Mediators or Surrogate Endpoints." American Journal of Epidemiology 163, suppl_11 (June 1, 2006): S241. http://dx.doi.org/10.1093/aje/163.suppl_11.s241-b.

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36

Baker, Stuart G. "Surrogate Endpoints: Wishful Thinking or Reality?" JNCI: Journal of the National Cancer Institute 98, no. 8 (April 19, 2006): 502–3. http://dx.doi.org/10.1093/jnci/djj153.

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37

Ellenberg, Susan S., and J. Michael Hamilton. "Surrogate endpoints in clinical trials: Cancer." Statistics in Medicine 8, no. 4 (April 1989): 405–13. http://dx.doi.org/10.1002/sim.4780080404.

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38

Baak, Jan P. A. "Histological surrogate endpoints using quantitative cytometry." Journal of Cellular Biochemistry 53, S17G (1993): 96–97. http://dx.doi.org/10.1002/jcb.240531119.

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39

Hartman, Holly E., and William C. Jackson. "Surrogate Endpoints in Localized Prostate Cancer." Cancer Journal 26, no. 1 (2020): 48–52. http://dx.doi.org/10.1097/ppo.0000000000000422.

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40

Li, Nicole F. "Surrogate Endpoints in Oncology Drug Development." Annals of Oncology 25 (October 2014): v86. http://dx.doi.org/10.1093/annonc/mdu436.53.

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41

Gottlieb, Stephen S. "Surrogate Endpoints: Not the Real Thing." Journal of Cardiac Failure 22, no. 10 (October 2016): 761–62. http://dx.doi.org/10.1016/j.cardfail.2016.07.431.

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42

Berns, B., P. Démolis, and M. E. Scheulen. "How can biomarkers become surrogate endpoints?" European Journal of Cancer Supplements 5, no. 9 (December 2007): 37–40. http://dx.doi.org/10.1016/j.ejcsup.2007.09.003.

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43

Markman, Maurie. "Surrogate Efficacy Endpoints in Oncology Trials." Pharmaceutical Medicine 23, no. 5-6 (October 2009): 283–87. http://dx.doi.org/10.1007/bf03256783.

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44

Feigin, Andrew. "Evidence from biomarkers and surrogate endpoints." Neurotherapeutics 1, no. 3 (July 2004): 323–30. http://dx.doi.org/10.1007/bf03206617.

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45

Biglan, Kevin M., and Robert G. Holloway. "Surrogate endpoints in Parkinson’s disease research." Current Neurology and Neuroscience Reports 3, no. 4 (July 2003): 314–20. http://dx.doi.org/10.1007/s11910-003-0008-y.

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46

Ellenberg, Susan S. "Surrogate endpoints: the debate goes on." Pharmacoepidemiology and Drug Safety 10, no. 6 (October 2001): 493–96. http://dx.doi.org/10.1002/pds.655.

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47

Busch, MP, HA Perkins, P. Holland, and L. Petersen. "The CUE debate (continued): on surrogate tests and surrogate endpoints." Transfusion 31, no. 9 (November 1991): 869–71. http://dx.doi.org/10.1046/j.1537-2995.1991.31992094677.x.

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48

Ying, Jian, Andrew Redd, and Tom Greene. "2091." Journal of Clinical and Translational Science 1, S1 (September 2017): 22–23. http://dx.doi.org/10.1017/cts.2017.92.

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OBJECTIVES/SPECIFIC AIMS: The objective of this research is to determine under what conditions endpoints based on estimated glomerular filtration rate (eGFR) slope or on relatively small declines in eGFR provide valid and useful surrogate endpoints for pivotal clinical trials in chronic kidney disease (CKD) patients. METHODS/STUDY POPULATION: We consider 2 classes of surrogate endpoints. The first class includes endpoints defined by the average rate of change in eGFR during defined portions of the follow-up period of the trial, following initiation of the randomized treatment interventions. The second class includes composite endpoints defined by the time from randomization until the occurrence of a designated decline in eGFR or kidney failure. The true clinical endpoint is considered to be the time from randomization until kidney failure, irrespective of the trajectory in eGFR measurements prior to kidney failure. We apply statistical simulation to determine conditions under which alternative endpoints within the 2 classes are (1) valid surrogate endpoints, in the sense of preserving a low probability of rejecting the null hypothesis of no treatment effect on the surrogate endpoint when there is no treatment effect on the clinical endpoints and are also (2) useful surrogate endpoints, in the sense of providing increased statistical power that allows significant reductions in sample size and/or duration of follow-up. Input parameters for the simulations include (a) characteristics of the joint distribution of the longitudinal eGFR measurements and the time to occurrence of renal failure, (b) characteristics of the short-term and long-term effects of the treatment, and (c) design parameters, including the duration of accrual and follow-up and the spacing of eGFR measurements during the follow-up period. We use joint analyses of 19 treatment comparisons across 13 previous clinical trials of CKD patients to guide the selection of input parameters for the simulations. We apply longitudinal mixed effects models for analysis of endpoints based on eGFR slope, and Cox regression for analyses of the composite time-to-event endpoints. RESULTS/ANTICIPATED RESULTS: We have previously shown that surrogate endpoints defined by eGFR declines of 30% or 40% can provide valid and useful alternative endpoints in CKD clinical trials for interventions that do not produce short-term effects on eGFR which differ from the longer-term effects of the interventions. Other factors influencing the validity and utility of these endpoints include the average baseline eGFR, the mean rate of change in eGFR, and the extent to which the size of the treatment effect depends on the patient’s underling rate of eGFR decline. We will extend these results by presenting preliminary results describing conditions under which outcomes based on eGFR slope provide valid and useful alternatives to the clinical endpoint of time until occurrence of kidney failure. DISCUSSION/SIGNIFICANCE OF IMPACT: The statistical simulation strategy described in this research can be used during the design of clinical trials of chronic kidney disease to assist in the selection of endpoints that maximize savings in sample size and duration of follow-up while retaining a low risk of producing a false positive conclusion in the absence of a true effect of the treatment on the time until kidney failure.

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49

Cheng, Sierra, Matthew C. Cheung, Di Maria Jiang, Erica McDonald, Vanessa S. Arciero, Doreen Anuli Ezeife, Amanda Rahmadian, et al. "Are Surrogate Endpoints Unbiased Metrics in Clinical Benefit Scores of the ASCO Value Framework?" Journal of the National Comprehensive Cancer Network 17, no. 12 (December 2019): 1489–96. http://dx.doi.org/10.6004/jnccn.2019.7333.

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Background: Clinical benefit scores (CBS) are key elements of the ASCO Value Framework (ASCO-VF) and are weighted based on a hierarchy of efficacy endpoints: hazard ratio for death (HR OS), median overall survival (mOS), HR for disease progression (HR PFS), median progression-free survival (mPFS), and response rate (RR). When HR OS is unavailable, the other endpoints serve as “surrogates” to calculate CBS. CBS are computed from PFS or RR in 39.6% of randomized controlled trials. This study examined whether surrogate-derived CBS offer unbiased scoring compared with HR OS–derived CBS. Methods: Using the ASCO-VF, CBS for advanced disease settings were computed for randomized controlled trials of oncology drug approvals by the FDA, European Medicines Agency, and Health Canada in January 2006 through December 2017. Mean differences of surrogate-derived CBS minus HR OS–derived CBS assessed the tendency of surrogate-derived CBS to overestimate or underestimate clinical benefit. Spearman’s correlation evaluated the association between surrogate- and HR OS–derived CBS. Mean absolute error assessed the average difference between surrogate-derived CBS relative to HR OS–derived CBS. Results: CBS derived from mOS, HR PFS, mPFS, and RR overestimated HR OS–derived CBS in 58%, 68%, 77%, and 55% of pairs and overall by an average of 5.62 (n=90), 6.86 (n=110), 29.81 (n=101), and 3.58 (n=108), respectively. Correlation coefficients were 0.80 (95% CI, 0.70–0.86), 0.38 (0.20–0.53), 0.20 (0.00–0.38), and 0.01 (–0.18 to 0.19) for mOS-, HR PFS–, mPFS-, and RR-derived CBS, respectively, and mean absolute errors were 11.32, 12.34, 40.40, and 18.63, respectively. Conclusions: Based on the ASCO-VF algorithm, HR PFS–, mPFS-, and RR-derived CBS are suboptimal surrogates, because they were shown to be biased and poorly correlated to HR OS–derived CBS. Despite lower weighting than OS in the ASCO-VF algorithm, PFS still overestimated CBS. Simple rescaling of surrogate endpoints may not improve their validity within the ASCO-VF given their poor correlations with HR OS–derived CBS.

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Michiels, S., A. Le Maître, M. Buyse, T. Burzykowski, J. Bogaerts, J. B. Vermorken, W. Budach, K. Ang, T. Pajak, and J. P. Pignon. "Surrogate endpoints for overall survival (OS) in head and neck squamous cell carcinoma (HNSCC): Evaluation using individual data of 23,737 patients." Journal of Clinical Oncology 25, no. 18_suppl (June 20, 2007): 6035. http://dx.doi.org/10.1200/jco.2007.25.18_suppl.6035.

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6035 Background: The gold standard endpoint in randomized trials of HNSCC is OS. Our objective was to study if event-free survival (EFS) or loco-regional control (LRC) could be good surrogate endpoints to estimate the effect of radiotherapy (RT) and chemotherapy (CT) on OS. This would permit to decrease the duration and cost of the development of new treatments for HNSCC. Methods: EFS is the time from randomization to first event (loco-regional, distant recurrence or death), LRC the time from randomization to first loco-regional event. Individual patient data from two meta-analyses (MARCH; Bourhis, Lancet 2006, MACH-NC; Bourhis, ASCO 2004) were used. At the individual level, the rank correlation coefficient ρ between the surrogate endpoint (EFS or LRC) and OS was estimated from the bivariate distribution of these endpoints. At the trial level, the correlation coefficient R between treatment effects (estimated by log hazard ratios) on the surrogate endpoint and OS was estimated from a linear regression. EFS and LRC would be acceptable surrogates only if the correlation coefficients ρ and R were close to 1. Results: At the individual level, EFS was more strongly correlated with OS than LRC. For RT, treatment effects on both LRC and EFS were strongly correlated with those on OS. For CT, the correlation coefficients between treatment effects on EFS and OS were larger than those between LRC and OS. Conclusions: The preliminary analysis indicates that EFS can be used as a surrogate for OS to evaluate the treatment effect in randomized trials of patients with HNSCC. LRC is a possible alternative in RT alone trials. Unrestricted grants from ARC, LNCC, PHRC, Sanofi-Aventis. [Table: see text] [Table: see text]

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