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Statistical methods for building better biomarkers of chronic kidney disease

dc.contributor.authorPencina, Michael J.
dc.contributor.authorParikh, Chirag R.
dc.contributor.authorKimmel, Paul L.
dc.contributor.authorCook, Nancy R.
dc.contributor.authorCoresh, Josef
dc.contributor.authorFeldman, Harold I.
dc.contributor.authorFoulkes, Andrea
dc.contributor.authorGimotty, Phyllis A.
dc.contributor.authorHsu, Chi‐yuan
dc.contributor.authorLemley, Kevin
dc.contributor.authorSong, Peter
dc.contributor.authorWilkins, Kenneth
dc.contributor.authorGossett, Daniel R.
dc.contributor.authorXie, Yining
dc.contributor.authorStar, Robert A.
dc.date.accessioned2019-05-31T18:26:24Z
dc.date.available2020-07-01T17:47:46Zen
dc.date.issued2019-05-20
dc.identifier.citationPencina, Michael J.; Parikh, Chirag R.; Kimmel, Paul L.; Cook, Nancy R.; Coresh, Josef; Feldman, Harold I.; Foulkes, Andrea; Gimotty, Phyllis A.; Hsu, Chi‐yuan ; Lemley, Kevin; Song, Peter; Wilkins, Kenneth; Gossett, Daniel R.; Xie, Yining; Star, Robert A. (2019). "Statistical methods for building better biomarkers of chronic kidney disease." Statistics in Medicine 38(11): 1903-1917.
dc.identifier.issn0277-6715
dc.identifier.issn1097-0258
dc.identifier.urihttps://hdl.handle.net/2027.42/149268
dc.publisherDivision of Drug Information, Office of Communications, Center for Drug Evaluation and Research (CDER)
dc.publisherWiley Periodicals, Inc.
dc.subject.othercalibration
dc.subject.othercostâ benefit
dc.subject.otherdiscrimination
dc.subject.otherrisk communication
dc.subject.otherrisk model
dc.subject.othervalidation
dc.titleStatistical methods for building better biomarkers of chronic kidney disease
dc.typeArticle
dc.rights.robotsIndexNoFollow
dc.subject.hlbsecondlevelStatistics and Numeric Data
dc.subject.hlbsecondlevelPublic Health
dc.subject.hlbsecondlevelMedicine (General)
dc.subject.hlbtoplevelHealth Sciences
dc.subject.hlbtoplevelScience
dc.subject.hlbtoplevelSocial Sciences
dc.description.peerreviewedPeer Reviewed
dc.description.bitstreamurlhttps://deepblue.lib.umich.edu/bitstream/2027.42/149268/1/sim8091.pdf
dc.description.bitstreamurlhttps://deepblue.lib.umich.edu/bitstream/2027.42/149268/2/sim8091_am.pdf
dc.identifier.doi10.1002/sim.8091
dc.identifier.sourceStatistics in Medicine
dc.identifier.citedreferenceVickers AJ, Pencina MJ. Prostateâ specific antigen velocity: new methods, same results, still no evidence of clinical utility. Eur Urol. 2013; 64: 394 â 396.
dc.identifier.citedreferenceSteyerberg EW, Harrell FE, Borsboom GJJM, Eijkemans MJC, Vergouwe Y, Habbema JDF. Internal validation of predictive models: efficiency of some procedures for logistic regression analysis. J Clin Epidemiol. 2001; 54: 774 â 781.
dc.identifier.citedreferencePencina MJ, D’Agostino Sr RB. Thoroughly modern risk prediction? Sci Transl Med. 2012; 4:131fs110.
dc.identifier.citedreferenceParikh CR, Thiessenâ Philbrook H. Key concepts and limitations of statistical methods for evaluating biomarkers of kidney disease. J Am Soc Nephrol. 2014; 25: 1621 â 1629.
dc.identifier.citedreferenceCollins GS, Reitsma JB, Altman DG, Moons KG. T ransparent r eporting of a multivariable prediction model for i ndividual p rognosis or d iagnosis (TRIPOD): the TRIPOD statement. Ann Intern Med. 2015; 162: 55 â 63.
dc.identifier.citedreferenceVan Calster B, Vickers AJ. Calibration of risk prediction models: impact on decisionâ analytic performance. Med Dec Making. 2015; 35: 162 â 169.
dc.identifier.citedreferenceSniderman AD, Pencina M, Thanassoulis G. Limitations in the conventional assessment of the incremental value of predictors of cardiovascular risk. Curr Opin Lipidol. 2015; 26: 210 â 214.
dc.identifier.citedreferenceHolland PW. Causal inference, path analysis, and recursive structural equation models (with discussion). Sociol Methodol. 1988; 18: 449 â 484.
dc.identifier.citedreferenceImai K, Tingley D, Yamamoto T. Experimental designs for identifying causal mechanisms. J R Stat Soc A Stat Soc. 2013; 176: 5 â 51.
dc.identifier.citedreferenceSteyerberg EW, Vedder MM, Leening MJ, et al. Graphical assessment of incremental value of novel markers in prediction models: from statistical to decision analytical perspectives. Biom J Biom Z. 2015; 57: 556 â 570.
dc.identifier.citedreferenceHedeker DR, Gibbons RD. Longitudinal Data Analysis. Hoboken, NJ: Wileyâ Interscience; 2006.
dc.identifier.citedreferenceFine JP, Gray RJ. A proportional hazards model for the subdistribution of a competing risk. J Am Statist Assoc. 1999; 94: 496 â 509.
dc.identifier.citedreferenceChapfuwa P, Tao C, Li C, et al. Adversarial timeâ toâ event modelling. Paper presented at: 35th International Conference on Machine Learning; 2018; Stockholm, Sweden.
dc.identifier.citedreferenceAndersen PK. Statistical Models Based on Counting Processes. New York, NY: Springerâ Verlag; 1993.
dc.identifier.citedreferenceTsiatis A. A nonidentifiability aspect of the problem of competing risks. Proc Natl Acad Sci USA. 1975; 72: 20 â 22.
dc.identifier.citedreferenceVan Calster B, Van Belle V, Vergouwe Y, Timmerman D, Van Huffel S, Steyerberg EW. Extending the c â statistic to nominal polytomous outcomes: the Polytomous Discrimination Index. Statist Med. 2012; 31: 2610 â 2626.
dc.identifier.citedreferenceLi JL, Jiang BY, Fine JP. Multicategory reclassification statistics for assessing improvements in diagnostic accuracy. Biostatistics. 2013; 14: 382 â 394.
dc.identifier.citedreferenceWolbers M, Koller MT, Witteman JC, Steyerberg EW. Prognostic models with competing risks: methods and application to coronary risk prediction. Epidemiology. 2009; 20: 555 â 561.
dc.identifier.citedreferenceHallan SI, Matsushita K, Sang Y, et al. Age and association of kidney measures with mortality and endâ stage renal disease. J Am Med Assoc. 2012; 308: 2349 â 2360.
dc.identifier.citedreferenceVedder MM, de Bekkerâ Grob EW, Lilja HG, et al. The added value of percentage of free to total prostateâ specific antigen, PCA3, and a kallikrein panel to the ERSPC risk calculator for prostate cancer in prescreened men. Eur Urol. 2014; 66: 1109 â 1115.
dc.identifier.citedreferenceRizopoulos D. JM: an R package for the joint modelling of longitudinal and timeâ toâ event data. J Stat Softw. 2010; 35: 1 â 33.
dc.identifier.citedreferenceHatfield LA, Carlin BP. Clinically relevant graphical predictions from Bayesian joint longitudinalâ survival models. Health Serv Outcomes Res Meth. 2012; 12: 169 â 181.
dc.identifier.citedreferenceVickers AJ, Till C, Tangen CM, Lilja H, Thompson IM. An empirical evaluation of guidelines on prostateâ specific antigen velocity in prostate cancer detection. J Natl Cancer Inst. 2011; 103: 462 â 469.
dc.identifier.citedreferenceIbrahim JG, Chu H, Chen LM. Basic concepts and methods for joint models of longitudinal and survival data. J Clin Oncology. 2010; 28: 2796 â 2801.
dc.identifier.citedreferenceKomarek A, Komarkova L. Clustering for multivariate continuous and discrete longitudinal data. Ann Appl Stat. 2013; 7: 177 â 200.
dc.identifier.citedreferenceArnlov J, Pencina MJ, Amin S, et al. Endogenous sex hormones and cardiovascular disease incidence in men. Ann Intern Med. 2006; 145: 176 â 184.
dc.identifier.citedreferenceNavarâ Boggan AM, Peterson ED, D’Agostino RB, Neely B, Sniderman AD, Pencina MJ. Hyperlipidemia in early adulthood increases longâ term risk of coronary heart disease. Circulation. 2015; 131: 451 â 458.
dc.identifier.citedreferenceDaniel RM, Cousens SN, De Stavola BL, Kenward MG, Sterne JAC. Methods for dealing with timeâ dependent confounding. Statist Med. 2013; 32: 1584 â 1618.
dc.identifier.citedreferenceYang W, Israni RK, Brunelli SM, Joffe MM, Fishbane S, Feldman HI. Hemoglobin variability and mortality in ESRD. J Am Soc Nephrol. 2007; 18: 3164 â 3170.
dc.identifier.citedreferenceWang F, Wang L, Song PXK. Quadratic inference function approach to merging longitudinal studies: validation and joint estimation. Biometrika. 2012; 99: 755 â 762.
dc.identifier.citedreferenceWang F, Song PX, Wang L. Merging multiple longitudinal studies with studyâ specific missing covariates: a joint estimating function approach. Biometrics. 2015; 71 ( 4 ): 929 â 940.
dc.identifier.citedreferenceKrumholz HM, Ross JS. A model for dissemination and independent analysis of industry data. J Am Med Assoc. 2011; 306: 1593 â 1594.
dc.identifier.citedreferenceInstitute of Medicine of the National Academies. Sharing Clinical Trial Data: Maximizing Benefits, Minimizing Risk. Washington, DC: National Academies Press; 2015.
dc.identifier.citedreferencePencina MJ, Louzao DM, McCour BJ, et al. Supporting open access to clinical trial data for researchers: the Duke Clinical Research Instituteâ Bristolâ Myers Squibb supporting open access to researchers initiative. Am Heart J. 2016; 172: 64 â 69.
dc.identifier.citedreferenceUS National Institutes of Health. HHS and NIH take steps to enhance transparency of clinical trial results. 2014.
dc.identifier.citedreferenceLo B. Sharing clinical trial data maximizing benefits, minimizing risk. J Am Med Assoc. 2015; 313: 793 â 794.
dc.identifier.citedreferenceDetilleux J, Reginster JY, Chines A, Bruyere O. A Bayesian path analysis to estimate causal effects of bazedoxifene acetate on incidence of vertebral fractures, either directly or through nonâ linear changes in bone mass density. Stat Meth Med Res. 2012; 25 ( 1 ): 400 â 412.
dc.identifier.citedreferenceRoysland K, Gran JM, Ledergerber B, von Wyl V, Young J, Aalen OO. Analyzing direct and indirect effects of treatment using dynamic path analysis applied to data from the Swiss HIV cohort study. Statist Med. 2011; 30: 2947 â 2958.
dc.identifier.citedreferenceWinship C, Mare RD. Structural equations and path analysis for discrete data. Am J Sociol. 1983; 89: 54 â 110.
dc.identifier.citedreferenceHlatky MA, Greenland P, Arnett DK, et al. Criteria for evaluation of novel markers of cardiovascular risk: a scientific statement from the American Heart Association. Circulation. 2009; 119: 2408 â 2416.
dc.identifier.citedreferenceUS Food and Drug Administration. Guidance for Industry and FDA Staff: Qualification Process for Drug Development Tools. Silver Spring, MD: Division of Drug Information, Office of Communications, Center for Drug Evaluation and Research (CDER); 2014.
dc.identifier.citedreferenceSniderman AD, D’Agostino RB, Pencina MJ. The role of physicians in the era of predictive analytics. J Am Med Assoc. 2015; 314: 25 â 26.
dc.identifier.citedreferenceDemler OV, Pencina MJ, D’Agostino RB. Misuse of DeLong test to compare AUCs for nested models. Statist Med. 2012; 31: 2577 â 2587.
dc.identifier.citedreferencePepe MS, Kerr KF, Longton G, Wang Z. Testing for improvement in prediction model performance. Statist Med. 2013; 32: 1467 â 1482.
dc.identifier.citedreferenceVickers AJ, Pepe M. Does the net reclassification improvement help us evaluate models and markers? Ann Intern Med. 2014; 160: 136â 137.
dc.identifier.citedreferenceHarrell FE. Regression Modeling Strategies. New York, NY: Springer; 2015.
dc.identifier.citedreferenceSteyerberg EW, Vickers AJ, Cook NR, et al. Assessing the performance of prediction models: a framework for traditional and novel measures. Epidemiology. 2010; 21: 128 â 138.
dc.identifier.citedreferenceBaker SG, Cook NR, Vickers A, Kramer BS. Using relative utility curves to evaluate risk prediction. J R Stat Soc A Stat Soc. 2009; 172: 729 â 748.
dc.identifier.citedreferenceVan Calster B, Vickers AJ, Pencina MJ, Bake SG, Timmerman D, Steyerberg EW. Evaluation of markers and risk prediction models: overview of relationships between NRI and decisionâ analytic measures. Med Decis Making. 2013; 33: 490 â 501.
dc.identifier.citedreferenceChambless LE, Diao G. Estimation of timeâ dependent area under the ROC curve for longâ term risk prediction. Statist Med. 2006; 25: 3474 â 3486.
dc.identifier.citedreferencePencina MJ, D’Agostino RB. Overall C as a measure of discrimination in survival analysis: model specific population value and confidence interval estimation. Statist Med. 2004; 23: 2109 â 2123.
dc.identifier.citedreferenceKerr KF, McClelland RL, Brown ER, Lumley T. Evaluating the incremental value of new biomarkers with integrated discrimination improvement. Am J Epidemiol. 2011; 174: 364 â 374.
dc.identifier.citedreferenceTjur T. Coefficients of determination in logistic regression modelsâ A new proposal: the coefficient of discrimination. Am Stat. 2009; 63: 366 â 372.
dc.identifier.citedreferenceKorn EL, Simon R. Measures of explained variation for survival data. Statist Med. 1990; 9: 487 â 503.
dc.identifier.citedreferenceWang TJ, Gona P, Larson MG, et al. Multiple biomarkers for the prediction of first major cardiovascular events and death. N Engl J Med. 2006; 355: 2631 â 2639.
dc.identifier.citedreferenceZethelius B, Berglund L, Sundstrom J, et al. Use of multiple biomarkers to improve the prediction of death from cardiovascular causes. N Engl J Med. 2008; 358: 2107 â 2116.
dc.identifier.citedreferenceVergouwe Y, Moons KG, Steyerberg EW. External validity of risk models: use of benchmark values to disentangle a caseâ mix effect from incorrect coefficients. Am J Epidemiol. 2010; 172: 971 â 980.
dc.identifier.citedreferenceWhite IR, Rapsomaniki E. Covariateâ adjusted measures of discrimination for survival data. Biom J. 2015; 57: 592 â 613.
dc.identifier.citedreferencePepe MS, Fan J, Seymour CW, Li C, Huang Y, Feng Z. Biases introduced by choosing controls to match risk factors of cases in biomarker research. Clin Chem. 2012; 58: 1242 â 1251.
dc.identifier.citedreferenceSiontis GC, Tzoulaki I, Castaldi PJ, Ioannidis JP. External validation of new risk prediction models is infrequent and reveals worse prognostic discrimination. J Clin Epidemiol. 2015; 68: 25 â 34.
dc.identifier.citedreferenceCook NR. Use and misuse of the receiver operating characteristic curve in risk prediction. Circulation. 2007; 115: 928 â 935.
dc.identifier.citedreferenceHand DJ. Measuring classifier performance: a coherent alternative to the area under the ROC curve. Mach Learn. 2009; 77: 103 â 123.
dc.identifier.citedreferenceHilden J. The area under the ROC curve and its competitors. Med Decis Making. 1991; 11: 95 â 101.
dc.identifier.citedreferenceYates JF. External correspondence: decompositions of the mean probability score. Organ Behav Hum Perf. 1982; 30: 132 â 156.
dc.identifier.citedreferencePencina MJ, D’Agostino RB, Pencina KM, Janssens AC, Greenland P. Interpreting incremental value of markers added to risk prediction models. Am J Epidemiol. 2012; 176: 473 â 481.
dc.identifier.citedreferencePencina MJ, D’Agostino RB, Vasan RS. Evaluating the added predictive ability of a new marker: from area under the ROC curve to reclassification and beyond. Statist Med. 2008; 27: 157 â 172.
dc.identifier.citedreferenceSteyerberg EW, Pencina MJ, Lingsma HF, Kattan MW, Vickers AJ, Van Calster B. Assessing the incremental value of diagnostic and prognostic markers: a review and illustration. Eur J Clin Invest. 2012; 42: 216 â 228.
dc.identifier.citedreferenceBansal A, Pepe MS. When does combining markers improve classification performance and what are implications for practice? Statist Med. 2013; 32: 1877 â 1892.
dc.identifier.citedreferenceDemler OV, Pencina MJ, D’Agostino RB. Impact of correlation on predictive ability of biomarkers. Statist Med. 2013; 32: 4196 â 4210.
dc.identifier.citedreferenceDemler OV, Paynter NP, Cook NR. Tests of calibration and goodnessâ ofâ fit in the survival setting. Statist Med. 2015; 34: 1659 â 1680.
dc.identifier.citedreferencePepe MS. Problems with risk reclassification methods for evaluating prediction models. Am J Epidemiol. 2011; 173: 1327 â 1335.
dc.identifier.citedreferenceMoons KG, Altman DG, Reitsma JB, et al. T ransparent r eporting of a multivariable prediction model for i ndividual p rognosis or d iagnosis (TRIPOD): explanation and elaboration. Ann Intern Med. 2015; 162: W1 â W73.
dc.identifier.citedreferenceHarrell Jr FE, Lee KL, Mark DB. Multivariable prognostic models: issues in developing models, evaluating assumptions and adequacy, and measuring and reducing errors. Statist Med. 1996; 15: 361 â 387.
dc.identifier.citedreferenceKerr KF, Meisner A, Thiessenâ Philbrook H, Coca SG, Parikh CR. Developing risk prediction models for kidney injury and assessing incremental value for novel biomarkers. Clin J Am Soc Nephrol. 2014; 9: 1488 â 1496.
dc.owningcollnameInterdisciplinary and Peer-Reviewed


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