Abstract
Biomarkers are characteristics that are measured as indicators of normal biological processes, pathogenic processes, or responses to an exposure or intervention, including therapeutic interventions. Biomarkers may serve a number of important uses, particularly in diagnosis and prognosis of disease, and as surrogates for clinical outcomes of disease (i.e., outcomes that measure how patient survives, functions, or feels). Establishing the validity of a given biomarker for a specific role requires the conduct of carefully designed clinical studies in which the biomarker and the outcome of interest are measured independently. The design and analysis of such studies is discussed. Surrogate outcomes in clinical trials consist of events or biomarkers intended to reflect important clinical outcomes. Surrogate outcomes may offer advantages in providing statistically robust estimates of treatment effects with smaller sample sizes. However, to be useful, surrogate outcomes have to be validated to ensure that the effect of therapy on them truly reflects the effect of therapy on the important clinical outcomes of interest.
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References
FDA-NIH Biomarker Working Group (2016) BEST (Biomarkers, EndpointS, and other Tools) Resource [Internet]. Food and Drug Administration (US), Silver Spring, MD. https://www.ncbi.nlm.nih.gov/books/NBK326791
Wang TJ, Gona P, Larson MG et al (2006) Multiple biomarkers for the prediction of first major cardiovascular events and death. N Engl J Med 355:2631–2639
Ho J, Tangri N, Komenda P, Kaushal A, Sood M, Brar R, Gill K, Walker S (2015) Urinary, plasma, and serum biomarkers’ utility for predicting acute kidney injury associated with cardiac surgery in adults: a meta-analysis. Am J Kidney Dis 66:993–1005
Sackett D, Haynes R, Guyatt G, Tugwell P (1991) The interpretation of diagnostic data. In: Clinical epidemiology: a basic science for clinical medicine, 2nd edn. Little, Brown and Company, Toronto
Kleinbaum DG (1996) Survival analysis: a self-learning text. Springer-Verlag, New York
Cox DR, Oakes D (1984) Analysis of survival data. Chapman and Hall/CRC, Boca Raton, FL
Pencina MJ, D’Agostino RB (2004) Overall C as a measure of discrimination in survival analysis: model specific population value and confidence interval estimation. Stat Med 23:2109–2123
Pencina MJ, D’Agostino RB Sr, D’Agostino RB Jr, Vasan RS (2008) Evaluating the added predictive ability of a new marker: from area under the ROC curve to reclassification and beyond. Stat Med 27:157–172. discussion 207–212
Pencina MJ, D’Agostino RB Sr, Steyerberg EW (2011) Extensions of net reclassification improvement calculations to measure usefulness of new biomarkers. Stat Med 30:11–21
Peduzzi P, Concato J, Kemper E, Holford TR, Feinstein AR (1996) A simulation of the number of events per variable in logistic regression analysis. J Clin Epi 99:1373–1379
Hanley J, McNeil B (1982) The meaning and use of the area under a receiver operating characteristic (ROC) curve. Radiology 143:29–36
Mulherin SA, Miller WC (2002) Spectrum bias or spectrum effect? Subgroup variation in diagnostic test evaluation. Ann Intern Med 137:598–602
Spaulding C, Daemen J, Boersma E, Cutlip DE, Serruys PW (2007) A pooled analysis of data comparing sirolimus-eluting stents with bare-metal stents. N Engl J Med 356:989–997
Chen EY, Joshi SK, Tran A, Prasad V (2019) Estimation of study time reduction using surrogate end points rather than overall survival in oncology clinical trials. J Am Med Assoc Intern Med 179:642–647
Nissen SE, Tardif JC, Nicholls SJ, Revkin JH, Shear CL, Duggan WT, Ruzyllo W, Bachinsky WB, Lasala GP, Tuzcu EM, ILLUSTRATE Investigators (2007) Effect of torcetrapib on the progression of coronary atherosclerosis. N Engl J Med 356:1304–1316
LaRosa JC, Grundy SM, Waters D, Shear C, Barter P, Fruchart JC et al (2005) Intensive lipid lowering with atorvastatin in patients with stable coronary disease. N Engl J Med 352:1425–1435
Baker AG, Kramer BS (2003) A perfect correlate does not a surrogate make. BMC Med Res Methodol 3:16–21
Prentice RL (1989) Surrogate endpoints in clinical trials: definitions and operational criteria. Stat Med 8:431–430
Gail MH, Pfeiffer R, Houwelingen HC, Carroll RJ (2001) On meta-analytic assessment of surrogate outcomes. Biostat 3:231–246
Alonso A, Meyvisch P, Van der Elst W, Molenberghs G, Verbeke G (2019) A reflection on the possibility of finding a good surrogate. J Biopharm Stat 29:468–477
Coresh J, Heerspink HJL, Sang Y, Matsushita K, Arnlov J, Astor BC, Black C et al (2019) Change in albuminuria and subsequent risk of end-stage kidney disease: an individual participant-level consortium meta-analysis of observational studies. Lancet Diabetes Endocrinol 7:115–127
Harrison TG, Tam-Tham H, Hemmelgarn BR, Elliott M, James MT, Ronksley PE, Jun M (2019) Change in proteinuria or albuminuria as a surrogate for cardiovascular and other major clinical outcomes: a systematic review and meta-analysis. Can J Cardiol:77–91
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Rigatto, C., Barrett, B.J. (2021). Randomized Controlled Trials 5: Biomarkers and Surrogates/Outcomes. In: Parfrey, P.S., Barrett, B.J. (eds) Clinical Epidemiology. Methods in Molecular Biology, vol 2249. Humana, New York, NY. https://doi.org/10.1007/978-1-0716-1138-8_15
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DOI: https://doi.org/10.1007/978-1-0716-1138-8_15
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