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Operations Engineering & Analytics

Optimal data-driven policies for disease screening under noisy biomarker measurement

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Pages 166-180 | Received 30 May 2018, Accepted 02 Jun 2019, Published online: 01 Aug 2019

References

  • Aissi, H., Bazgan, C. and Vanderpooten, D. (2006) Approximating min-max (regret) versions of some polynomial problems, in International Computing and Combinatorics Conference, Springer, Berlin, Heidelberg, pp. 428–438.
  • Atrash, S., Robinson, M.M., Aneralla, A., Brown, T., Friend, R., Sprouse, C., Ndiaye, A., Zhang, Q., Lipford, E.H., Block, J.G. and Smith, E.T. (2017) Validation of dynamic biomarker-based risk progression model for smoldering multiple myeloma. Blood, 130, 1779.
  • Austin, P.C., Tu, J.V., Ho, J.E., Levy, D. and Lee, D.S. (2013) Using methods from the data-mining and machine-learning literature for disease classification and prediction: A case study examining classification of heart failure subtypes. Journal of Clinical Epidemiology, 66(4), 398–407.
  • Averbakh, I. (2004) Minmax regret linear resource allocation problems. Operations Research Letters, 32(2), 174–180.
  • Ayer, T., Alagoz, O. and Stout, N.K. (2012) OR Forum—A POMDP approach to personalize mammography screening decisions. Operations Research, 60(5), 1019–1034.
  • Ayer, T., Alagoz, O., Stout, N.K. and Burnside, E.S. (2015) Heterogeneity in women’s adherence and its role in optimal breast cancer screening policies. Management Science, 62(5), 1339–1362.
  • Ayvaci, M.U., Alagoz, O. and Burnside, E.S. (2012) The effect of budgetary restrictions on breast cancer diagnostic decisions. Manufacturing & Service Operations Management, 14(4), 600–617.
  • Ayvaci, M.U.S., Ahsen, M.E., Raghunathan, S. and Gharibi, Z. (2017) Timing the use of breast cancer risk information in biopsy decision-making. Production and Operations Management, 26(7), 1333–1358.
  • Bacus, S. and Spector, N. (2007) Biomarkers in cancer. US Patent App. 10/568,251.
  • Baker, M.W., Atkins, A.E., Cordovado, S.K., Hendrix, M., Earley, M.C. and Farrell, P.M. (2016) Improving newborn screening for cystic fibrosis using next-generation sequencing technology: A technical feasibility study. Genetics in Medicine, 18(3), 231–238.
  • Barnett, C.L., Tomlins, S.A., Underwood, D.J., Wei, J.T., Morgan, T.M., Montie, J.E. and Denton, B.T. (2017) Two-stage biomarker protocols for improving the precision of early detection of prostate cancer. Medical Decision Making, 37(7), 815–826.
  • Berezin, A.E., Kremzer, A.A., Martovitskaya, Y.V., Berezina, T.A. and Samura, T.A. (2015) The utility of biomarker risk prediction score in patients with chronic heart failure. Clinical Hypertension, 22(1), 3–13.
  • Bertsimas, D., Brown, D.B. and Caramanis, C. (2011) Theory and applications of robust optimization. SIAM Review, 53(3), 464–501.
  • Bertsimas, D., Silberholz, J. and Trikalinos, T. (2018) Optimal healthcare decision making under multiple mathematical models: Application in prostate cancer screening. Healthcare Management Science, 21(1), 105–118.
  • Bonaccorso, G. (2017) Machine Learning Algorithms. Packt Publishing Ltd.
  • Castellani, C., Massie, J., Sontag, M. and Southern, K.W. (2016) Newborn screening for cystic fibrosis. The Lancet Respiratory Medicine, 4(8), 653–661.
  • Comeau, A.M., Accurso, F.J., White, T.B., Campbell, P.W., Hoffman, G., Parad, R.B., Wilfond, B.S., Rosenfeld, M., Sontag, M.K., Massie, J. et al. (2007) Guidelines for implementation of cystic fibrosis newborn screening programs: Cystic Fibrosis Foundation workshop report. Pediatrics, 119(2), e495–e518.
  • Comeau, A.M., Parad, R.B., Dorkin, H.L., Dovey, M., Gerstle, R., Haver, K., Lapey, A., O’Sullivan, B.P., Waltz, D.A., Zwerdling, R.G., and Eaton, R.B. (2004) Population-based newborn screening for genetic disorders when multiple mutation DNA testing is incorporated: A cystic fibrosis newborn screening model demonstrating increased sensitivity but more carrier detections. Pediatrics, 113(6), 1573–1581.
  • Currier, R.J., Sciortino, S., Liu, R., Bishop, T., Koupaei, R.A. and Feuchtbaum, L. (2017) Genomic sequencing in cystic fibrosis newborn screening: What works best, two-tier predefined CFTR mutation panels or second-tier CFTR panel followed by third-tier sequencing? Genetics in Medicine, 19(10), 1159–1163.
  • Cystic Fibrosis Foundation. https://www.cff.org/, accessed April 2018.
  • Deneef, P. and Kent, D.L. (1993) Using treatment-tradeoff preferences to select diagnostic strategies: Linking the ROC curve to threshold analysis. Medical Decision Making, 13(2), 126–132.
  • Dijk, F.N., McKay, K., Barzi, F., Gaskin, K.J. and Fitzgerald, D.A. (2011) Improved survival in cystic fibrosis patients diagnosed by newborn screening compared to a historical cohort from the same centre. Archives of Disease in Childhood, 96(12), 1118–1123.
  • Dodd, R., Notari, E. and Stramer, S. (2002) Current prevalence and incidence of infectious disease markers and estimated window-period risk in the American Red Cross blood donor population. Transfusion, 42(8), 975–979.
  • Doecke, J.D., Laws, S.M., Faux, N.G., Wilson, W., Burnham, S.C., Lam, C.P., Mondal, A., Bedo, J., Bush, A.I., Brown, B. and De Ruyck, K. (2012) Blood-based protein biomarkers for diagnosis of Alzheimer disease. Archives of Neurology, 69(10), 1318–1325.
  • Draper, N.R. and Smith, H. (1998) Selecting the “best” regression equation. Applied Regression Analysis, 327–368.
  • El-Amine, H., Bish, E.K., and Bish, D.R. (2018) Robust postdonation blood screening under prevalence rate uncertainty. Operations Research, 66(1), 1–17.
  • Farrell, P.M., Kosorok, M.R., Rock, M.J., Laxova, A., Zeng, L., Lai, H., Hoffman, G., Laessig, R.H., Splaingard, M.L., Wisconsin Cystic Fibrosis Neonatal Screening Study Group and others (2001) Early diagnosis of cystic fibrosis through neonatal screening prevents severe malnutrition and improves long-term growth. Pediatrics, 107(1), 1–13.
  • Farrell, P.M., Rosenstein, B.J., White, T.B., Accurso, F.J., Castellani, C., Cutting, G.R., Durie, P.R., LeGrys, V.A., Massie, J., Parad, R.B. and Rock, M.J. (2008) Guidelines for diagnosis of cystic fibrosis in newborns through older adults: Cystic Fibrosis Foundation consensus report. The Journal of Pediatrics, 153(2), S4–S14.
  • Felder, S. and Mayrhofer, T. (2014) Risk preferences: Consequences for test and treatment thresholds and optimal cutoffs. Medical Decision Making, 34(1), 33–41.
  • Fluss, R., Faraggi, D. and Reiser, B. (2005) Estimation of the Youden Index and its associated cutoff point. Biometrical Journal, 47(4), 458–472.
  • Greiner, M., Sohr, D. and Göbel, P. (1995) A modified ROC analysis for the selection of cut-off values and the definition of intermediate results of serodiagnostic tests. Journal of Immunological Methods, 185(1), 123–132.
  • Hammond, K.B., Abman, S.H., Sokol, R.J. and Accurso, F.J. (1991) Efficacy of statewide neonatal screening for cystic fibrosis by assay of trypsinogen concentrations. New England Journal of Medicine, 325(11), 769–774.
  • Higgins, T.L., Estafanous, F.G., Loop, F.D., Beck, G.J., Blum, J.M. and Paranandi, L. (1992) Stratification of morbidity and mortality outcome by preoperative risk factors in coronary artery bypass patients: A clinical severity score. JAMA, 267(17), 2344–2348.
  • Jiang, W., Freidlin, B. and Simon, R. (2007) Biomarker-adaptive threshold design: A procedure for evaluating treatment with possible biomarker-defined subset effect. Journal of the National Cancer Institute, 99(13), 1036–1043.
  • Jund, J., Rabilloud, M., Wallon, M. and Ecochard, R. (2005) Methods to estimate the optimal threshold for normally or log-normally distributed biological tests. Medical Decision Making, 25(4), 406–415.
  • Kammesheidt, A., Kharrazi, M., Graham, S., Young, S., Pearl, M., Dunlop, C. and Keiles, S. (2006) Comprehensive genetic analysis of the cystic fibrosis transmembrane conductance regulator from dried blood specimens–implications for newborn screening. Genetics in Medicine, 8(9), 557–562.
  • Kharrazi, M., Yang, J., Bishop, T., Lessing, S., Young, S., Graham, S., Pearl, M., Chow, H., Ho, T., Currier, R. and Gaffney, L. (2015). Newborn screening for cystic fibrosis in California. Pediatrics, 136(6), 1062–1072.
  • Kloosterboer, M., Hoffman, G., Rock, M., Gershan, W., Laxova, A., Li, Z. and Farrell, P.M. (2009) Clarification of laboratory and clinical variables that influence cystic fibrosis newborn screening with initial analysis of immunoreactive trypsinogen. Pediatrics, 123(2), e338–e346.
  • Kucirka, L.M., Sarathy, H., Govindan, P., Wolf, J.H., Ellison, T.A., Hart, L.J., Montgomery, R.A., Ros, R.L. and Segev, D.L. (2011) Risk of window period HIV infection in high infectious risk donors: Systematic review and meta-analysis. American Journal of Transplantation, 11(6), 1176–1187.
  • Kuhn, M. and Johnson, K. (2013) Applied Predictive Modeling, Vol. 26, Springer, New York.
  • Massie, J., Curnow, L., Tzanakos, N., Francis, I. and Robertson, C.F. (2006) Markedly elevated neonatal immunoreactive trypsinogen levels in the absence of cystic fibrosis gene mutations is not an indication for further testing. Archives of Disease in Childhood, 91(3), 222–225.
  • Mastin, A., Jaillet, P. and Chin, S. (2015) Randomized minmax regret for combinatorial optimization under uncertainty, in International Symposium on Algorithms and Computation, Springer, Berlin, Heidelberg, pp. 491–501.
  • Mayeux, R. (2004) Biomarkers: Potential uses and limitations. NeuroRx, 1(2), 182–188.
  • McMahan, C.S., Tebbs, J.M. and Bilder, C.R. (2012). Regression models for group testing data with pool dilution effects. Biostatistics, 14(2), 284–298.
  • Paracchini, V., Seia, M., Raimondi, S., Costantino, L., Capasso, P., Porcaro, L., Colombo, C., Coviello, D.A., Mariani, T., Manzoni, E. and Sangiovanni, M. (2011) Cystic fibrosis newborn screening: Distribution of blood immunoreactive trypsinogen concentrations in hypertrypsinemic neonates, in JIMD Reports-Case and Research Reports, 2012/1, Springer, Berlin, Heidelberg, pp.17–23.
  • Pauker, S.G. and Kassirer, J.P. (1980) The threshold approach to clinical decision making. New England Journal of Medicine, 302(20), 1109–1117.
  • Pepe, M.S. (2003) The Statistical Evaluation of Medical Tests for Classification and Prediction, Medicine.
  • Perakis, G. and Roels, G. (2008) Regret in the newsvendor model with partial information. Operations Research, 56(1), 188–203.
  • Pollitt, R.J. and Matthews, A.J. (2007) Population quantile-quantile plots for monitoring assay performance in newborn screening. Journal of Inherited Metabolic Disease, 30(4), 607–607.
  • Price, S., Golden, B., Wasil, E. and Denton, B.T. (2016) Operations research models and methods in the screening, detection, and treatment of prostate cancer: A categorized, annotated review. Operations Research for Health Care, 8, 9–21.
  • Rapisuwon, S., Vietsch, E.E. and Wellstein, A. (2016) Circulating biomarkers to monitor cancer progression and treatment. Computational and Structural Biotechnology Journal, 14, 211–222.
  • Rohlfs, E.M., Zhou, Z., Heim, R.A., Nagan, N., Rosenblum, L.S., Flynn, K., Scholl, T., Akmaev, V.R., Sirko-Osadsa, D.A., Allitto, B.A. and Sugarman, E.A. (2011) Cystic fibrosis carrier testing in an ethnically diverse US population. Clinical Chemistry, 57, 841–848.
  • Sato, K.K., Hayashi, T., Harita, N., Yoneda, T., Nakamura, Y., Endo, G. and Kambe, H. (2009). Combined measurement of fasting plasma glucose and hba1c is effective for the prediction of type 2 diabetes: The Kansai healthcare study. Diabetes Care. 32, 644–646.
  • Savage, L.J. (1951) The theory of statistical decision. Journal of the American Statistical Association, 46(253), 55–67.
  • Schisterman, E.F., Perkins, N.J., Liu, A. and Bondell, H. (2005) Optimal cut-point and its corresponding Youden Index to discriminate individuals using pooled blood samples. Epidemiology, 16(1), 73–81.
  • Sims, E.J., Clark, A., McCormick, J., Mehta, G., Connett, G. and Mehta, A. (2007). Cystic fibrosis diagnosed after 2 months of age leads to worse outcomes and requires more therapy. Pediatrics, 119(1), 19–28.
  • Solvang, H.K., Frigessi, A., Kaveh, F., Riis, M.L., Lüders, T., Bukholm, I.R., Kristensen, V.N. and Andreassen, B.K. (2016) Gene expression analysis supports tumor threshold over 2.0 cm for T-category breast cancer. EURASIP Journal on Bioinformatics and Systems Biology, 2016(1),6–16.
  • Somoza, E. and Mossman, D. (1992) Comparing and optimizing diagnostic tests: An information-theoretical approach. Medical Decision Making, 12(3), 179–188.
  • Sontag, M.K., Hammond, K.B., Zielenski, J., Wagener, J.S. and Accurso, F.J. (2005) Two-tiered immunoreactive trypsinogen-based newborn screening for cystic fibrosis in Colorado: Screening efficacy and diagnostic outcomes. The Journal of Pediatrics, 147(3), S83–S88.
  • Stephen, J., Murray, G., Cameron, D.A., Thomas, J., Kunkler, I.H., Jack, W., Kerr, G.R., Piper, T., Brookes, C.L., Rea, D. W. and Van De Velde, C.J.H. (2014) Time dependence of biomarkers: Non-proportional effects of immunohistochemical panels predicting relapse risk in early breast cancer. British Journal of Cancer, 111(12), 2242–2247.
  • Subtil, F. and Rabilloud, M. (2010) A Bayesian method to estimate the optimal threshold of a longitudinal biomarker. Biometrical Journal, 52(3), 333–347.
  • Subtil, F. and Rabilloud, M. (2014) Estimating the optimal threshold for a diagnostic biomarker in case of complex biomarker distributions. BMC Medical Informatics and Decision Making, 14(1), 53–63.
  • Szefler, S.J., Wenzel, S., Brown, R., Erzurum, S.C., Fahy, J.V., Hamilton, R.G., Hunt, J.F., Kita, H., Liu, A.H., Panettieri Jr, R.A. and Schleimer, R.P. (2012) Asthma outcomes: Biomarkers. Journal of Allergy and Clinical Immunology, 129(3), S9–S23.
  • Therrell, B.L., Hannon, W.H., Hoffman, G., Ojodu, J. and Farrell, P.M. (2012) Immunoreactive trypsinogen (IRT) as a biomarker for cystic fibrosis: Challenges in newborn dried blood spot screening. Molecular Genetics and Metabolism, 106(1), 1–6.
  • Tluczek, A., Mischler, E.H., Farrell, P.M., Fost, N., Peterson, N.M., Carey, P., Bruns, W.T., and McCarthy, C. (1992) Parents’ knowledge of neonatal screening and response to false-positive cystic fibrosis testing. Journal of Developmental and Behavioral Pediatrics: JDBP, 13(3), 181–186.
  • Underwood, D.J., Zhang, J., Denton, B.T., Shah, N.D. and Inman, B.A. (2012) Simulation optimization of PSA-threshold based prostate cancer screening policies. Healthcare Management Science, 15(4), 293–309.
  • Van Giessen, A., de Wit, G.A., Moons, K.G., Dorresteijn, J.A. and Koffijberg, H. (2017) An alternative approach identified optimal risk thresholds for treatment indication: An illustration in coronary heart disease. Journal of Clinical Epidemiology, 94, 122–131.
  • Vermont, J., Bosson, J., Francois, P., Robert, C., Rueff, A. and Demongeot, J. (1991) Strategies for graphical threshold determination. Computer Methods and Programs in Biomedicine, 35(2), 141–150.
  • Wang, D., McMahan, C.S., Tebbs, J.M. and Bilder, C.R. (2018) Group testing case identification with biomarker information. Computational Statistics & Data Analysis, 122, 156–166.
  • Wein, L.M. and Zenios, S.A. (1996) Pooled testing for HIV screening: Capturing the dilution effect. Operations Research, 44(4), 543–569.
  • Wells, J., Rosenberg, M., Hoffman, G., Anstead, M. and Farrell, P.M. (2012) A decision-tree approach to cost comparison of newborn screening strategies for cystic fibrosis. Pediatrics, 129, 339–347.
  • Wilson Tang, W.H., Francis, G.S., Morrow, D.A., Newby, L.K., Cannon, C.P., Jesse, R.L., Storrow, A.B., Christenson, R.H., COMMITTEE MEMBERS, Christenson, R.H. and Apple, F.S. (2007) National Academy of Clinical Biochemistry Laboratory Medicine practice guidelines: Clinical utilization of cardiac biomarker testing in heart failure. Circulation, 116(5), e99–e109.
  • Yang, Y., Goldhaber-Fiebert, J.D. and Wein, L.M. (2013) Analyzing screening policies for childhood obesity. Management Science, 59(4), 782–795.
  • Ypma, T.J. (1995) Historical development of the Newton–Raphson method. SIAM Review, 37(4), 531–551.
  • Yu, W., Liu, T., Valdez, R., Gwinn, M. and Khoury, M.J. (2010) Application of support vector machine modeling for prediction of common diseases: The case of diabetes and pre-diabetes. BMC Medical Informatics and Decision Making, 10(1), 16.
  • Yue, J., Chen, B. and Wang, M. (2006) Expected value of distribution information for the newsvendor problem. Operations Research, 54(6), 1128–1136.
  • Zenios, S.A. and Wein, L.M. (1998) Pooled testing for HIV prevalence estimation: exploiting the dilution effect. Statistics in Medicine, 17(13), 1447–1467.
  • Zhang, J., Denton, B.T., Balasubramanian, H., Shah, N.D., and Inman, B.A. (2012) Optimization of prostate biopsy referral decisions. Manufacturing & Service Operations Management, 14(4), 529–547.

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