TABLE A-1 Historical Review of the Biomarker–Surrogate Endpoint Literature with Special Reference to the Nomenclature, Initial Reports, Systems of Classification, and Statistical Methods Developed for Their Evaluation
Year |
Author |
Focus |
Field/Summary and Commentary |
1963 |
Mainland |
Nomenclature: Substituted variables |
Statistics and Medicine In his Elementary Medical Statistics, he discusses substituting variables that are easy to observe for ones that are difficult to observe. |
1966 |
Rushing |
Nomenclature: First report of surrogate used in any context |
Psychology, Ethics, Social Science, Law The role of the hospital nurse as a mother surrogate. (Many publications followed in the 1960s and 1970s where surrogate was used in this context of a person’s role in the fields of psychology, ethics, social science, and law.) |
1973 |
Rho et al. |
Nomenclature: First report of biomarker |
Biology A search for porphyrin biomarkers in nonesuch shale and extraterrestrial samples. Biomarker here represents biological marker—origins of biological life. |
1976 |
Schlenger |
Nomenclature: First report of surrogate AND outcome |
Epidemiology Mortality and morbidity rates as surrogates for “health.” |
1977 |
Karpetsky et al. |
Nomenclature: Second report of biomarker |
Oncology Serum RNase level was found to be an indicator of renal function, and was not a biomarker either for the presence or extent of the plasma cell tumor. (Forty of 46 biomarker reports from 1977 to 1985 were in oncology.) |
1978 |
Baker |
Nomenclature: Third report of biomarker |
Oncology Preoperative assessment of the patient with breast cancer. |
1980 |
Regelson |
Nomenclature: First report of biomarker outside cancer in medicine |
General Medicine Biomarkers in aging: A beginning for a therapeutic approach in Transactions of the Association of Life Insurance Medical Directors of America. |
Year |
Author |
Focus |
Field/Summary and Commentary |
|
Webb and Lin |
Nomenclature: First report of biomarker in title of publication |
Oncology Urinary fibronectin: Potential as a biomarker in prostatic cancer. |
1982 |
Waalkes et al. |
Biomarkers for clinical application |
Oncology Feasibility study in the development of 17 biological markers for ovarian cancer. |
1983 |
Wood |
Nomenclature: First report of surrogate AND endpoint, second report of surrogate AND outcome |
Rheumatology Nature of surrogate endpoints. Relationships considered at two levels: (1) ability of the attribute to act as a surrogate in detection of the underlying state (at a particular point in time); (2) potential of the surrogate to reveal changes in the underlying state as its course unfolds. |
1986 |
Bigger |
Second surrogate and endpoint, third surrogate and outcome |
Cardiology Electrophysiological testing to select patients with ventricular arrhythmias for drug trials and to determine anti-arrhythmic drug efficacy. (By the end of the decade, the use of biomarkers as surrogates in cardiology had a number of high-profile failures.) |
|
Buccheri et al. |
First report of biomarker as measure of tumor burden and predict outcome |
Oncology Clinical value of a multiple biomarker assay (CEA, TPA, b-HCG, LDH) in patients with bronchogenic carcinoma. |
1987 |
Kalish et al. |
Third surrogate and endpoint |
Oncology Surrogates as endpoints in bladder cancer trials. Data show that superficial disease endpoints do not predict surrogates for invasive disease endpoints. |
|
Schulof et al. |
Surrogates markers first used as response to therapy |
HIV Phase I/II trial of thymosin fraction 5 and thymosin alpha one in HTLV-III–seropositive subjects. |
Year |
Author |
Focus |
Field/Summary and Commentary |
|
Rosin et al. |
Intermediate endpoints |
Oncology Promise of intermediate endpoints in quantitating the response of precancerous lesions to chemopreventive agents. |
1989 |
The Cardiac Arrhythmia Suppression Trial (CAST) Investigators; Ruskin |
First example of study to test surrogate; treatment of a biomarker successful, but patient outcome worse |
Cardiology CAST showed that successful suppression of the ventricular arrhythmia biomarker with antiarrhythmic therapy was associated with increased rather than decreased patient mortality. |
|
Herson |
First substantive discussion on surrogate endpoints in clinical trials |
Methodology An introduction to four invited papers on surrogate endpoints in clinical trials. These were pivotal papers. Trigger was an FDA criticism of new drug applications in cardiology and oncology because they used surrogate endpoints. |
Ellenberg and Hamilton |
All key issues discussed using examples from oncology |
Methodology Advantages and disadvantages of surrogate endpoints. Key points: Used when endpoints of interest are too difficult and/or expensive to measure; must be sufficiently well correlated with the endpoints of interest to justify substitution; initial choice often based on biologic rationale as primary endpoints are more acceptable in early drug development than later pivotal studies. |
|
|
Wittes et al. |
Many key issues discussed using examples from cardiology |
Methodology Key points: “True” endpoint is one with clinical importance to the patient, such as mortality or a major clinical outcome; surrogate is one biologically closer to the process of disease; surrogate is useful if easily measured and highly correlated with the true endpoint; surrogates can dramatically reduce sample size and trial duration. |
Year |
Author |
Focus |
Field/Summary and Commentary |
|
Prentice |
First report addressing the key statistical barrier to the use of surrogates |
Statistics Prentice defines a surrogate endpoint to be a “response variable for which a test of the null hypothesis of no relationship to the treatment groups under comparison is also a valid test of the corresponding null hypothesis based on the true endpoint.” |
|
Beaudry and Spence |
First report of surrogate outcome |
Cardiology Atherosclerosis severity index based on noninvasive ultrasound assessment to replace angiographic measurement of atherosclerosis (costly and invasive), which in turn replaced clinical endpoints (latter most expensive). (Example of developing a surrogate to replace another surrogate.) |
|
Buchwald et al. |
Empirical surrogate endpoint validation |
Cardiology RCT to demonstrate a reduction in overall mortality by lipid modification and to validate coronary arteriographic change as a surrogate for change in coronary heart disease risk. |
1990 |
Machado et al. |
Testing validity of surrogate therapeutics |
HIV Medicine Pros for surrogate endpoints: Ethical/practical reasons for hastening decision making about the efficacy of new treatments for HIV infection. Cons: Serious overestimates of clinical benefit if treatment had delayed toxicity or only transient beneficial effects; serious underestimates of clinical benefit when the treatment had no effect on the transition from healthy to the marker state. |
Year |
Author |
Focus |
Field/Summary and Commentary |
|
Schatzkin et al. |
Statistical validation strategy |
Oncology The intermediate endpoint is a valid cancer surrogate if the attributable proportion is near 1.0, but not if it is near 0 (usually the attributable proportion is neither 1.0 nor 0); in this case in an established exposure-cancer relationship, the exposure effect would vanish if adjusted for the intermediate endpoint. |
|
Woosley |
Further commentary on CAST results and implications for drug development |
Cardiology High-profile study that illustrated the dangers of surrogate therapeutics. (Further failures followed in other cardiology studies. Within a few years, surrogates rarely used in cardiology and large outcome trials with patient endpoints were the norm. Other fields in medicine did not have resources to conduct large, long studies and continued to argue for the use of surrogates in drug development.) |
Lippman et al. |
Schema |
Oncology Proposed three classes of biomarkers: genomic, proliferation, and differentiation markers. Biomarker validation studies should follow an evolutionary process. This leads to first generation (short-term trials in high-risk patients), second generation (dose and schedule trials), and third generation trials (long-term phase III trials to validate first generation candidate biomarkers). |
|
1992 |
New drug, antibiotic, and biological drug product regulations; accelerated approval—FDA. Final Rulea |
FDA “accelerated approval” regulation |
General Accelerate approval of new drugs and biological products for serious or life-threatening illnesses, with provisions for any necessary continued study of the drugs’ clinical benefits after approval. |
Year |
Author |
Focus |
Field/Summary and Commentary |
|
Freedman et al. |
Statistical validation |
Statistics Statistical validation of intermediate endpoints requires exposure or intervention effect, adjusted for the intermediate endpoint, to be reduced to zero. The estimating statistic—PTE—is explained by the intermediate/surrogate endpoint and its 95% confidence limits are determined. |
|
Boissel et al. |
Schema |
Methodology Three provisos for surrogate outcome evaluation. Proviso 1, the surrogate endpoint, should occur more frequently than corresponding clinical endpoint. Proviso 2, that relationship between the surrogate and clinical endpoint, is well established through relevant epidemiological studies. Proviso 3, that the estimate of the expected clinical benefit should be derivable from the estimate of the reduction on the surrogate endpoint, which can be obtained from randomized clinical trials data. |
|
Freedman et al. |
Schema |
Methodology A new validation criterion based on an analysis of the three-way relationship of exposure (E), marker (M), and disease (D). Provides the level of evidence required for using intermediate markers as endpoints for Phase II and Phase III trials. (These criteria were conceptual and qualitative only.) |
|
Freedman and Schatzkin |
Sample-size issues |
Methodology Different sample-size requirements for questions on surrogate endpoint validity: Does the intervention affect the intermediate endpoint? Is the intermediate endpoint associated with the main outcome? Is the intervention effect on the main outcome mediated by the intermediate endpoint? |
Year |
Author |
Focus |
Field/Summary and Commentary |
1993 |
The Hypertension Optimal Treatment Study (the HOT Study) |
Targeting biomarker |
Cardiology Dose–response relationship between surrogate target and clinical outcome. |
|
Lin et al. |
Application of Prentice |
AIDS CD4-lymphocyte count captures part of the relationship between zidovudine and time to a first critical event, but does not fulfill the Prentice criterion. |
1994 |
Aickin |
Surrogate endpoint biomarker |
Oncology If there is gold in the labeling index hills, are we digging in the right place? (Tool for cancer chemoprevention studies.) |
1995 |
Temple |
Schema |
Methodology “Feels function or survives” definition for surrogate endpoint. |
|
Lee et al. |
Review |
Methodology Surrogate biochemical markers: Precise measurement for strategic drug and biologics development. |
Hughes et al. |
Review |
Statistics/HIV Evaluating surrogate markers. |
|
|
Scientific Advisory Committee on Surrogate Markers of HIV |
Consensus |
HIV Medicine Consensus statement. Scientific advisory committee on surrogate markers of HIV. |
Year |
Author |
Focus |
Field/Summary and Commentary |
1996 |
Fleming and DeMets |
Review |
Methodology Surrogate endpoints in clinical trials. Are we being misled? Argues for use of surrogate endpoints in Phase II, but not Phase III pivotal trials. Failure of surrogate endpoints because: (1) surrogate is not in the causal pathway of the disease process; (2) of several causal pathways of the disease, the intervention affects only the pathway mediated through the surrogate; (3) surrogate is not in the pathway of the intervention’s effect or is insensitive to its effect; and (4) intervention has mechanisms of action independent of disease process. |
|
Schatzkin et al. |
Review |
Methodology Surrogate endpoints in cancer research: a critique. |
1997 |
De Gruttola et al. |
Schema |
Methodology Validating surrogate markers: Are we being naïve? The variety of proposed metrics for evaluating the degree to which this criterion is met are subject to misinterpretation because of the multiplicity of mechanisms by which drugs operate. Without detailed understanding of these mechanisms, metrics of “surrogacy” are not directly interpretable. Even when all of the mechanisms are understood, these metrics are associated with a high degree of uncertainty unless either treatment effects are large in moderate-sized studies or sample sizes are large in studies of moderately effective treatments. |
|
Lin et al. |
Statistics |
Statistics Estimating the proportion of a treatment effect explained by surrogate marker. |
Year |
Author |
Focus |
Field/Summary and Commentary |
|
Mildvan et al. |
Schema |
Methodology An approach to the validation of markers for use in AIDS clinical trials. |
|
Rolan |
Schema |
Methodology The contribution of clinical pharmacology surrogates and models to drug development. Proposes five dimensional properties of surrogates. These are validation (statistical), innovation, proximity to clinical outcome, specificity for an intervention, and practicality. |
|
Topol et al. |
Review |
Methodology Need clinical endpoints to establish safety and efficacy. |
|
Daniels and Hughes |
Schema |
Statistical Method/HIV Meta-analysis for the evaluation of potential surrogate markers. |
|
Boissel et al. |
Schema |
Methodology Clinical evaluation: From intermediate to surrogate criteria (French). |
|
Colburn |
Schema |
Methodology Selecting and validating biologic markers for drug development. |
1998 |
Albert et al. |
Review– consensus |
Methodology/HIV Statistical issues for HIV surrogate endpoints: Point/counterpoint. |
|
Buyse and Molenberghs |
Statistics |
Statistics Introduction of the relative effect (RE) and adjusted association (AA) for single-unit studies. |
|
FDA and NIH |
Review and abstracts |
Methodology Biomarkers and surrogate endpoints: Advancing clinical research and applications. (Abstracts.) |
Year |
Author |
Focus |
Field/Summary and Commentary |
|
Hayes |
Schema |
Methodology Tumor Marker Utility Grading System (TMUGS) proposed to evaluate the clinical utility of tumor markers and to establish an investigational agenda for evaluation of new tumor markers for risk assessment, screening, differential diagnosis, prognosis, monitoring clinical course, and use in clinical trials. Includes a TMUGS Worksheet that clarifies the precise characteristics of the marker in question and evaluates its clinical utility on a six-point scale (ranging from 0 to +++). |
1999 |
Bucher et al. |
Schema |
Methodology How to use and article measuring the effects of an intervention on surrogate endpoints. |
2000 |
Buyse et al. |
Statistics |
Statistics Validation of surrogate endpoints in meta-analysis of randomized experiments. |
|
Buyse et al. |
Statistics |
Statistics Statistical validation of surrogate endpoints. |
|
Colburn |
Schema |
Methodology Optimizing the use of biomarkers, surrogate endpoints, and clinical endpoints for efficient drug development. |
Year |
Author |
Focus |
Field/Summary and Commentary |
|
Gail et al. |
Statistics |
Methodology The strengths and weakness of meta-analytic assessment of surrogate endpoints: (1) which trials? (2) how many trials? (3) difficult to obtain individual-level data to estimate within study variance. (4) between-study variation can yield much less precise estimates of treatment effects on true-endpoint than estimates based on true-endpoint itself. (5) realistic models for distribution complicated. (6) difficulty modeling joint or marginal distributions of true-endpoint and surrogate. (7) which approach frequentist, empirical Bayes, and Bayesian for hierarchical systems. (8) how to use covariates. (9) unanticipated toxicity. Conclusion: Meta-analysis of surrogate endpoints may lead to less precise estimates of treatment effect on clinical endpoint than relying on clinical endpoint itself. |
|
Begg and Leung |
Statistics |
Statistics Provide conceptual alternatives to Prentice criterion for surrogate statistical validation. |
|
Schatzkin |
Review |
Methodology Intermediate markers as surrogate endpoints in cancer research. |
|
Fleming |
Review |
Methodology Brief review of practical and statistical issues. |
2001 |
Li et al. |
Statistics |
Statistics A method to assess the proportion of treatment effect explained by a surrogate endpoint—a general model and graphical setting. |
|
Lesko and Atkinson |
Schemas |
Methodology Biomarkers and surrogate endpoints in drug development and regulatory decision making. |
Year |
Author |
Focus |
Field/Summary and Commentary |
|
Xu and Zeger |
Statistics |
Statistics Evaluation of multiple surrogate endpoints. |
|
Biomarkers Definitions Working Group |
Schema |
Methodology Biomarkers and surrogate endpoints: preferred definitions and conceptual framework. |
|
De Gruttola et al. |
Schema |
Methodology Considerations and recommendations in evaluation of surrogate endpoints in clinical trials: Summary of NIH workshop. |
2002 |
Wang and Taylor |
Statistics |
Statistics A measure of the proportion of treatment effect explained by a surrogate marker. |
|
Lathia |
Review |
Methodology Biomarkers and surrogate endpoints: How and when might they impact drug development? |
|
Molenberghs et al. |
Statistics |
Statistics Statistical challenges in the evaluation of surrogate endpoints in randomized trials. |
|
Wagner |
Review |
Methodology Overview of biomarkers and surrogate endpoints in drug development. |
|
Cowles |
Statistics |
Statistics Bayesian estimation of the PTE captured by a surrogate marker. |
|
Schatzkin and Gail |
Review |
Methodology Promise and peril of surrogate endpoints in cancer research: Review of the logical issues as well as the problem of measurement error. |
|
Frangakis and Rubin |
Statistics |
Statistics Principal stratification in causal inference. |
|
Henderson et al. |
Statistics |
Statistics Longitudinal modeling. |
Year |
Author |
Focus |
Field/Summary and Commentary |
|
Lin et al. |
Statistics |
Statistics Latent class models for joint analysis. |
|
Taylor and Wang |
Statistics |
Statistics Surrogate markers and joint models. |
|
Hughes |
Comment |
Methodology Imprecision in the estimates require modeling. |
2003 |
Rolan et al. |
Review |
Methodology Use of biomarkers from drug discovery through clinical practice. Mechanistic classification into six types of biomarkers. |
|
Baker and Freedman |
Statistics |
Statistics Method for analyzing data from a randomized trial with a missing binary outcome. |
|
Baker and Kramer |
Review |
Methodology A perfect correlate does not a surrogate make. |
2004 |
FDA |
Position paper |
FDA’s Critical Path Document Innovation or Stagnation: Challenge and Opportunity on the Critical Path to New Medical Products. |
|
Berger |
Statistics |
Statistics Does Prentice criterion validate surrogate endpoints? |
|
Molenberghs et al. |
Statistics |
Methodology Perspective of surrogate endpoints in controlled trials. |
|
Alonso et al. |
Statistics |
Methodology Role of statistics in surrogate endpoints. |
|
Rubin |
Statistics |
Methodology Direct versus indirect causal effects. |
Year |
Author |
Focus |
Field/Summary and Commentary |
|
Baker et al. |
Review |
Drug Development A general framework for describing various roles for biomarkers in cancer prevention research (early detection, surrogate endpoint, and cohort identification for primary prevention) and the phases in their evaluation. |
2005 |
Fleming |
Review |
Methodology Surrogate endpoints and FDA’s accelerated approval process. |
|
Sargent et al. |
Statistics |
Oncology Meta-analytic approach for surrogate validation. |
|
Baker, 2006a |
Statistics |
Methodology A simple meta-analytic approach for using a binary endpoint to predict the effect of intervention on true endpoint. |
|
Korn et al. |
Statistics |
Methodology Assessing surrogates as trial endpoints using mixed models. |
2006 |
Weir and Walley |
Statistics |
Review Statistical evaluation of biomarkers as surrogate endpoints: A literature review. |
|
Baker, 2006b |
Statistics |
Review Title: Surrogate endpoints: Wishful thinking or reality? |
|
Finley Austin and Babiss |
Review |
Methodology Where and how could biomarkers be used in 2016? |
|
Qu and Case |
Statistics |
Statistics Quantifying the indirect treatment effect via surrogate markers. |
|
Desai et al. |
Review |
Cardiology Blood pressure as an example of a biomarker that functions as a surrogate. |
|
Hughes |
Review |
HIV Medicine Initial treatment of HIV Infection: Randomized trials with clinical endpoints are still needed. |
TABLE A-2 Continuation of Table A-1 for 2007-2009
Year |
Author |
Focus |
Field/Summary and Commentary |
2007 |
Alonso and Molenberghs |
Statistics |
Methodology An information-based validation method for surrogate endpoints. |
|
Pryseley et al. |
Statistics |
Methodology The authors test and review a meta-analytic approach to biomarker qualification and support use of a recently proposed, more computationally efficient process in some circumstances. |
|
Rasnake et al. |
Regulatory |
Nutrition Discussion of emerging surrogate endpoints and the use of surrogate endpoints in the review of health claims at the FDA. |
2008 |
Alonso and Molenberghs |
Statistics |
Methodology/Oncology Evaluation of time to cancer recurrence as a surrogate endpoint for survival, as evaluated using a meta-analytic framework. |
|
Altar et al. |
Schema |
Methodology Provides an “evidence map” for grading available evidence and a process for biomarker qualification. |
|
Burzykowski |
Comment |
Methodology A concise summary of the topic of surrogate endpoint qualification. |
|
Chakravarty and Sridhara |
Regulatory |
Oncology/Regulatory Issues Discussion of use of progression-free survival as a trial endpoint. |
|
Green et al. |
Statistics |
Methodology Use of multiple methods, both statistical and clinically relevant qualitative methods, is proposed. |
|
Joy and Hegele |
Comment |
Methodology Discussion of the failure of the torcetrapib trials and the implications for CETP inhibition as a treatment target. |
|
Krumholz and Lee |
Comment |
Methodology Recent failures of surrogate endpoints in cardiology and endocrinology. |
Year |
Author |
Focus |
Field/Summary and Commentary |
|
Lassere |
Review, schema |
Methodology Systematic review of biomarker and surrogate endpoint validation criteria from 1950 to 2007; also provides criteria for ranking surrogate validity. |
|
Osborne |
Comment |
Alzheimer’s/Regulatory Comment on shifts in use of surrogate endpoints for Alzheimer’s disease drug development. |
|
Psaty and Lumley |
Comment |
Cardiology Further discussion of recent surrogate endpoint failures in lipid-altering drug clinical trials. |
|
Wagner |
Schema |
Methodology Comprehensive discussion of fit-for-purpose biomarker qualification for all stages of drug development. |
2009 |
Hlatky et al. |
Schema |
Methodology/Cardiology Title: Criteria for evaluation of novel markers of cardiovascular risk: A scientific statement from the American Heart Association. |
|
Lathia et al. |
Schema |
Methodology Successes and failures in use of surrogate endpoints for drug development; discussion of necessary criteria for surrogate endpoint qualification and use. |
|
Prentice |
Statistics |
Methodology Title: Surrogate and mediating endpoints: Current status and future directions. |
|
Rigatto and Barrett |
Review |
Methodology Statement of definitions, advantages, and disadvantages to biomarker and surrogate endpoint use for clinical trials. |
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