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Chapter 7 - Multiple Tests and Multivariable Risk Models

Published online by Cambridge University Press:  02 May 2020

Thomas B. Newman  and
Michael A. Kohn
Thomas B. Newman
Affiliation:
University of California, San Francisco
Michael A. Kohn
Affiliation:
University of California, San Francisco
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Summary

At this point, we know how to use the result of a single test to update the probability of disease but not how to combine the results from multiple tests, and we can evaluate risk prediction models but not create them. In making a clinical treatment decision (or any other decision), we usually consider multiple variables. This chapter is about combining the results of multiple tests with other information to estimate the probability of a disease or the risk of an outcome. We begin by reviewing the concept of test independence and then discuss how to deal with departures from independence, which are probably the rule rather than the exception. Next, we cover two common methods of combining variables to predict a binary condition or outcome: classification trees and logistic regression. Finally, we discuss the process and pitfalls of variable selection and the importance of model validation.

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Evidence-Based Diagnosis
An Introduction to Clinical Epidemiology
 , pp. 175 - 204
Publisher: Cambridge University Press
Print publication year: 2020

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References

Lachs, MS, Nachamkin, I, Edelstein, PH, et al. Spectrum bias in the evaluation of diagnostic tests: lessons from the rapid dipstick test for urinary tract infection. Ann Intern Med. 1992;117(2):135–40.Google Scholar
Cicero, S, Rembouskos, G, Vandecruys, H, Hogg, M, Nicolaides, KH. Likelihood ratio for trisomy 21 in fetuses with absent nasal bone at the 11-14-week scan. Ultrasound Obstet Gynecol. 2004;23(3):218–23.CrossRefGoogle ScholarPubMed
Stiell, IG, McKnight, RD, Greenberg, GH, et al. Implementation of the Ottawa ankle rules. JAMA. 1994;271(11):827–32.Google Scholar
Hoffman, JR, Mower, WR, Wolfson, AB, Todd, KH, Zucker, MI. Validity of a set of clinical criteria to rule out injury to the cervical spine in patients with blunt trauma. National Emergency X-Radiography Utilization Study Group. N Engl J Med. 2000;343(2):94–9.Google Scholar
Hoffman, JR, Wolfson, AB, Todd, K, Mower, WR. Selective cervical spine radiography in blunt trauma: methodology of the National Emergency X-Radiography Utilization Study (NEXUS). Ann Emerg Med. 1998;32(4):461–9.CrossRefGoogle ScholarPubMed
Therneau, T, Atkinson, E. An introduction to recursive partitioning using the RPART routines [web page/pdf]. cran.r-project.org; 2018. Available from: https://cran.r-project.org/web/packages/rpart/vignettes/longintro.pdf.Google Scholar
Goldman, L, Cook, EF, Brand, DA, et al. A computer protocol to predict myocardial infarction in emergency department patients with chest pain. N Engl J Med. 1988;318(13):797–803.CrossRefGoogle ScholarPubMed
Pantell, RH, Newman, TB, Bernzweig, J, et al. Management and outcomes of care of fever in early infancy. JAMA. 2004;291(10):1203–12.CrossRefGoogle ScholarPubMed
Lee, TH, Juarez, G, Cook, EF, et al. Ruling out acute myocardial infarction. A prospective multicenter validation of a 12-hour strategy for patients at low risk. N Engl J Med. 1991;324(18):1239–46.Google Scholar
Stoltzfus, JC. Logistic regression: a brief primer. Acad Emerg Med. 2011;18(10):1099–104.CrossRefGoogle ScholarPubMed
Laupacis, A, Sekar, N, Stiell, IG. Clinical prediction rules. A review and suggested modifications of methodological standards. JAMA. 1997;277(6):488–94.Google Scholar
Newman, TB, Bernzweig, JA, Takayama, JI, et al. Urine testing and urinary tract infections in febrile infants seen in office settings: the Pediatric Research in Office Settings’ Febrile Infant Study. Arch Pediatr Adolesc Med. 2002;156(1):44–54.Google Scholar
Shaikh, N, Morone, NE, Lopez, J, et al. Does this child have a urinary tract infection? JAMA. 2007;298(24):2895–904.CrossRefGoogle ScholarPubMed
Snijders, RJ, Sundberg, K, Holzgreve, W, Henry, G, Nicolaides, KH. Maternal age- and gestation-specific risk for trisomy 21. Ultrasound Obstet Gynecol. 1999;13(3):167–70.CrossRefGoogle ScholarPubMed
Nicolaides, KH. The 11-13+6 weeks scan. London: Fetal Medicine Foundation; 2004. 112p.Google Scholar
Selker, HP, Beshansky, JR, Griffith, JL, et al. Use of the acute cardiac ischemia time-insensitive predictive instrument (ACI-TIPI) to assist with triage of patients with chest pain or other symptoms suggestive of acute cardiac ischemia. A multicenter, controlled clinical trial. Ann Intern Med. 1998;129(11):845–55.Google Scholar
Selker, HP, Griffith, JL, D’Agostino, RB. A tool for judging coronary care unit admission appropriateness, valid for both real-time and retrospective use. A time-insensitive predictive instrument (TIPI) for acute cardiac ischemia: a multicenter study. Med Care. 1991;29(7):610–27.CrossRefGoogle ScholarPubMed
Fine, MJ, Auble, TE, Yealy, DM, et al. A prediction rule to identify low-risk patients with community-acquired pneumonia. N Engl J Med. 1997;336(4):243–50.Google Scholar
James, G, Witten, D, Hastie, T, Tibshirani, R. Chapter 6 linear model selection and regularization. An introduction to statistical learning: with applications in R. 103. New York: Springer; 2013.Google Scholar
Oostenbrink, R, van der Heijden, AJ, Moons, KG, Moll, HA. Prediction of vesico-ureteric reflux in childhood urinary tract infection: a multivariate approach. Acta Paediatr. 2000;89(7):806–10.Google Scholar
Leroy, S, Marc, E, Adamsbaum, C, et al. Prediction of vesicoureteral reflux after a first febrile urinary tract infection in children: validation of a clinical decision rule. Arch Dis Child. 2006;91(3):241–4.CrossRefGoogle ScholarPubMed
James, G, Witten, D, Hastie, T, Tibshirani, R. Chapter 5 Resampling methods. An introduction to statistical learning: with applications in R. 103. New York: Springer; 2013.CrossRefGoogle Scholar
Obermeyer, Z, Emanuel, EJ. Predicting the future – big data, machine learning, and clinical medicine. N Engl J Med. 2016;375(13):1216–9.CrossRefGoogle ScholarPubMed
Jordan, MI, Mitchell, TM. Machine learning: trends, perspectives, and prospects. Science. 2015;349(6245):255–60.Google Scholar
Efron, B, Hastie, T. Chapter 17 Random forests and boosting. Computer age statistical inference: algorithms, evidence, and data science. Institute of Mathematical Statistics monographs; 2016. pp. 324–50.Google Scholar
Gunčar, G, Kukar, M, Notar, M, et al. An application of machine learning to haematological diagnosis. Sci Rep. 2018;8(1):411.CrossRefGoogle ScholarPubMed
Caruana, R, Niculescu-Mizil, A. An empirical comparison of supervised learning algorithms. Proceedings of the 23rd international conference on machine learning. 2006.CrossRefGoogle Scholar
Ozcift, A. Random forests ensemble classifier trained with data resampling strategy to improve cardiac arrhythmia diagnosis. Comput Biol Med. 2011;41(5):265–71.Google Scholar
Yang, F, Wang, HZ, Mi, H, Lin, CD, Cai, WW. Using random forest for reliable classification and cost-sensitive learning for medical diagnosis. BMC Bioinformatics. 2009;10(Suppl 1):S22.CrossRefGoogle ScholarPubMed
O’Donnell, MJ, Fang, J, D’Uva, C, et al. The PLAN score: a bedside prediction rule for death and severe disability following acute ischemic stroke. Arch Intern Med. 2012;172(20):1548–56.CrossRefGoogle Scholar
Reid, JM, Dai, D, Delmonte, S, et al. Simple prediction scores predict good and devastating outcomes after stroke more accurately than physicians. Age Ageing. 2017;46(3):421–6.Google Scholar

References

Beiser, AS, Takahashi, M, Baker, AL, Sundel, RP, Newburger, JW. A predictive instrument for coronary artery aneurysms in Kawasaki disease. US Multicenter Kawasaki Disease Study Group. Am J Cardiol. 1998;81(9):1116–20.Google Scholar
Tanz, RR, Gerber, MA, Kabat, W, et al. Performance of a rapid antigen-detection test and throat culture in community pediatric offices: implications for management of pharyngitis. Pediatrics. 2009;123(2):437–44.CrossRefGoogle ScholarPubMed
Lessler, AL, Isserman, JA, Agarwal, R, Palevsky, HI, Pines, JM. Testing low-risk patients for suspected pulmonary embolism: a decision analysis. Ann Emerg Med. 2010;55(4):316–26 e1.Google Scholar
Kohn, MA, Klok, FA, van Es, N. D-dimer interval likelihood ratios for pulmonary embolism. Acad Emerg Med. 2017;24(7):832–7.CrossRefGoogle ScholarPubMed
Bui, Q, Miller, CC. The age that women have babies: how a gap divides America. New York Times. August 4, 2018.Google Scholar

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