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Published in: Journal of General Internal Medicine 1/2012

Open Access 01-06-2012 | Reviews

Chapter 12: Systematic Review of Prognostic Tests

Authors: Thomas S. Rector, PhD, Brent C. Taylor, PhD, Timothy J. Wilt, MD, MPH

Published in: Journal of General Internal Medicine | Special Issue 1/2012

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Abstract

A number of new biological markers are being studied as predictors of disease or adverse medical events among those who already have a disease. Systematic reviews of this growing literature can help determine whether the available evidence supports use of a new biomarker as a prognostic test that can more accurately place patients into different prognostic groups to improve treatment decisions and the accuracy of outcome predictions. Exemplary reviews of prognostic tests are not widely available, and the methods used to review diagnostic tests do not necessarily address the most important questions about prognostic tests that are used to predict the time-dependent likelihood of future patient outcomes. We provide suggestions for those interested in conducting systematic reviews of a prognostic test. The proposed use of the prognostic test should serve as the framework for a systematic review and to help define the key questions. The outcome probabilities or level of risk and other characteristics of prognostic groups are the most salient statistics for review and perhaps meta-analysis. Reclassification tables can help determine how a prognostic test affects the classification of patients into different prognostic groups, hence their treatment. Review of studies of the association between a potential prognostic test and patient outcomes would have little impact other than to determine whether further development as a prognostic test might be warranted.
Literature
1.
go back to reference Janes H, Pepe MS, Bossuyt PM, Barlow WE. Measuring the performance of markers for guiding treatment decisions. Ann Intern Med. 2011;154:253–9.PubMed Janes H, Pepe MS, Bossuyt PM, Barlow WE. Measuring the performance of markers for guiding treatment decisions. Ann Intern Med. 2011;154:253–9.PubMed
2.
go back to reference Hlatky 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(17):2408–16.PubMedCrossRef Hlatky 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(17):2408–16.PubMedCrossRef
3.
go back to reference Wang TJ. Assessing the role of circulating, genetic and imaging biomarkers in cardiovascular risk prediction. Circulation. 2011;123:551–65.PubMedCrossRef Wang TJ. Assessing the role of circulating, genetic and imaging biomarkers in cardiovascular risk prediction. Circulation. 2011;123:551–65.PubMedCrossRef
4.
go back to reference Ingui BJ, Rogers MA. Searching for clinical prediction rules in MEDLINE. J Am Med Inform Assoc. 2001;8(4):391–7.PubMedCrossRef Ingui BJ, Rogers MA. Searching for clinical prediction rules in MEDLINE. J Am Med Inform Assoc. 2001;8(4):391–7.PubMedCrossRef
5.
go back to reference Hayden JA, Cote P, Bombardier C. Evaluation of the quality of prognosis studies in systematicreviews. Ann Intern Med. 2006;144(6):427–37.PubMed Hayden JA, Cote P, Bombardier C. Evaluation of the quality of prognosis studies in systematicreviews. Ann Intern Med. 2006;144(6):427–37.PubMed
6.
go back to reference McShane LM, Altman DG, Sauerbrei W, et al. Reporting recommendations for tumor markerprognostic studies (REMARK). J Natl Cancer Inst. 2005;97(16):1180–4.PubMedCrossRef McShane LM, Altman DG, Sauerbrei W, et al. Reporting recommendations for tumor markerprognostic studies (REMARK). J Natl Cancer Inst. 2005;97(16):1180–4.PubMedCrossRef
7.
go back to reference Riley RD, Abrams KR, Sutton AJ, et al. Reporting of prognostic markers: current problems and development of guidelines for evidence-based practice in the future. Br J Cancer. 2003;88(8):1191–8.PubMedCrossRef Riley RD, Abrams KR, Sutton AJ, et al. Reporting of prognostic markers: current problems and development of guidelines for evidence-based practice in the future. Br J Cancer. 2003;88(8):1191–8.PubMedCrossRef
8.
go back to reference Kyzas PA, Denaxa-Kyza D, Ioannidis JP. Almost all articles on cancer prognostic markers report statistically significant results. Eur J Cancer. 2007;43(17):2559–79.PubMedCrossRef Kyzas PA, Denaxa-Kyza D, Ioannidis JP. Almost all articles on cancer prognostic markers report statistically significant results. Eur J Cancer. 2007;43(17):2559–79.PubMedCrossRef
9.
go back to reference Kyzas PA, Loizou KT, Ioannidis JP. Selective reporting biases in cancer prognostic factor studies. J Natl Cancer Inst. 2005;97(14):1043–55.PubMedCrossRef Kyzas PA, Loizou KT, Ioannidis JP. Selective reporting biases in cancer prognostic factor studies. J Natl Cancer Inst. 2005;97(14):1043–55.PubMedCrossRef
10.
11.
go back to reference Hall PA, Going JJ. Predicting the future: a critical appraisal of cancer prognosis studies. Histopathology. 1999;35(6):489–94.PubMedCrossRef Hall PA, Going JJ. Predicting the future: a critical appraisal of cancer prognosis studies. Histopathology. 1999;35(6):489–94.PubMedCrossRef
12.
go back to reference Altman DG, Riley RD. Primer: an evidence-based approach to prognostic markers. Nat Clin Pract Oncol. 2005;2(9):466–72.PubMedCrossRef Altman DG, Riley RD. Primer: an evidence-based approach to prognostic markers. Nat Clin Pract Oncol. 2005;2(9):466–72.PubMedCrossRef
13.
go back to reference Speight PM. Assessing the methodological quality of prognostic studies. Chapter 3 (p. 7–13) In: Speight, Palmer, Moles, et al. The cost-effectiveness of screening for oral cancer in primary care. Health Technol Assess 2006;10(14):1–144, iii–iv. Speight PM. Assessing the methodological quality of prognostic studies. Chapter 3 (p. 7–13) In: Speight, Palmer, Moles, et al. The cost-effectiveness of screening for oral cancer in primary care. Health Technol Assess 2006;10(14):1–144, iii–iv.
14.
go back to reference Pepe MS, Feng Z, Janes H, et al. Pivotal evaluation of the accuracy of a biomarker used for classification or prediction: standards for study design. J Natl Cancer Inst. 2008;100(20):1432–8.PubMedCrossRef Pepe MS, Feng Z, Janes H, et al. Pivotal evaluation of the accuracy of a biomarker used for classification or prediction: standards for study design. J Natl Cancer Inst. 2008;100(20):1432–8.PubMedCrossRef
15.
go back to reference Janes H, Pepe MS. Adjusting for covariates in studies of diagnostic, screening, or prognostic markers: an old concept in a new setting. Am J Epidemiol. 2008;168(1):89–97.PubMedCrossRef Janes H, Pepe MS. Adjusting for covariates in studies of diagnostic, screening, or prognostic markers: an old concept in a new setting. Am J Epidemiol. 2008;168(1):89–97.PubMedCrossRef
16.
17.
go back to reference Pepe MS, Janes H, Longton G, et al. Limitations of the odds ratio in gauging the performance of a diagnostic, prognostic, or screening marker. Am J Epidemiol. 2004;159(9):882–90.PubMedCrossRef Pepe MS, Janes H, Longton G, et al. Limitations of the odds ratio in gauging the performance of a diagnostic, prognostic, or screening marker. Am J Epidemiol. 2004;159(9):882–90.PubMedCrossRef
18.
go back to reference Feng Z, Prentice R, Srivastava S. Research issues and strategies for genomic and proteomic biomarker discovery and validation: a statistical perspective. Pharmacogenomics. 2004;5(6):709–19.PubMedCrossRef Feng Z, Prentice R, Srivastava S. Research issues and strategies for genomic and proteomic biomarker discovery and validation: a statistical perspective. Pharmacogenomics. 2004;5(6):709–19.PubMedCrossRef
19.
go back to reference Riesterer O, Milas L, Ang KK. Use of molecular biomarkers for predicting the response to radiotherapy with or without chemotherapy. J Clin Oncol. 2007;25(26):4075–83.PubMedCrossRef Riesterer O, Milas L, Ang KK. Use of molecular biomarkers for predicting the response to radiotherapy with or without chemotherapy. J Clin Oncol. 2007;25(26):4075–83.PubMedCrossRef
20.
go back to reference Parmar MK, Torri V, Stewart L. Extracting summary statistics to perform meta-analyses of the published literature for survival endpoints. Stat Med. 1998;17(24):2815–34.PubMedCrossRef Parmar MK, Torri V, Stewart L. Extracting summary statistics to perform meta-analyses of the published literature for survival endpoints. Stat Med. 1998;17(24):2815–34.PubMedCrossRef
21.
go back to reference The Fibrinogen Studies Collaboration. Measures to assess the prognostic ability of the stratified Cox proportional hazards model. Stat Med. 2009;28(3):389–411.CrossRef The Fibrinogen Studies Collaboration. Measures to assess the prognostic ability of the stratified Cox proportional hazards model. Stat Med. 2009;28(3):389–411.CrossRef
22.
go back to reference Earle CC, Pham B, Wells GA. An assessment of methods to combine published survival curves. Med Decis Making. 2000;20(1):104–11.PubMedCrossRef Earle CC, Pham B, Wells GA. An assessment of methods to combine published survival curves. Med Decis Making. 2000;20(1):104–11.PubMedCrossRef
23.
go back to reference Coplen SE, Antman EM, Berlin JA, et al. Efficacy and safety of quinidine therapy for maintenance of sinus rhythm after cardioversion. A meta-analysis of randomized control trials. Circulation. 1990;82(4):1106–16.PubMedCrossRef Coplen SE, Antman EM, Berlin JA, et al. Efficacy and safety of quinidine therapy for maintenance of sinus rhythm after cardioversion. A meta-analysis of randomized control trials. Circulation. 1990;82(4):1106–16.PubMedCrossRef
24.
go back to reference Sinuff T, Adhikari NK, Cook DJ, et al. Mortality predictions in the intensive care unit: comparing physicians with scoring systems. Crit Care Med. 2006;34(3):878–85.PubMedCrossRef Sinuff T, Adhikari NK, Cook DJ, et al. Mortality predictions in the intensive care unit: comparing physicians with scoring systems. Crit Care Med. 2006;34(3):878–85.PubMedCrossRef
25.
go back to reference Groenveld HF, Januzzi JL, Damman K, et al. Anemia and mortality in heart failure patients: a systematic review and meta-analysis. J Am Coll Cardiol. 2008;52(10):818–27.PubMedCrossRef Groenveld HF, Januzzi JL, Damman K, et al. Anemia and mortality in heart failure patients: a systematic review and meta-analysis. J Am Coll Cardiol. 2008;52(10):818–27.PubMedCrossRef
26.
go back to reference Mackillop WJ, Quirt CF. Measuring the accuracy of prognostic judgments in oncology. J Clin Epidemiol. 1997;50(1):21–9.PubMedCrossRef Mackillop WJ, Quirt CF. Measuring the accuracy of prognostic judgments in oncology. J Clin Epidemiol. 1997;50(1):21–9.PubMedCrossRef
27.
go back to reference Ingelsson E, Schaefer EJ, Contois JH, et al. Clinical utility of different lipid measures for prediction of coronary heart disease in men and women. JAMA. 2007;298(7):776–85.PubMedCrossRef Ingelsson E, Schaefer EJ, Contois JH, et al. Clinical utility of different lipid measures for prediction of coronary heart disease in men and women. JAMA. 2007;298(7):776–85.PubMedCrossRef
28.
go back to reference Poses RM, Cebul RD, Collins M, et al. The importance of disease prevalence in transporting clinical prediction rules. The case of streptococcal pharyngitis. Ann Intern Med. 1986;105(4):586–91.PubMed Poses RM, Cebul RD, Collins M, et al. The importance of disease prevalence in transporting clinical prediction rules. The case of streptococcal pharyngitis. Ann Intern Med. 1986;105(4):586–91.PubMed
29.
go back to reference Cook NR. Use and misuse of the receiver operating characteristic curve in risk prediction. Circulation. 2007;115(7):928–35.PubMedCrossRef Cook NR. Use and misuse of the receiver operating characteristic curve in risk prediction. Circulation. 2007;115(7):928–35.PubMedCrossRef
30.
go back to reference Pepe MS, Janes HE. Gauging the performance of SNPs, biomarkers, and clinical factors for predicting risk of breast cancer. J Natl Cancer Inst. 2008;100(14):978–9.PubMedCrossRef Pepe MS, Janes HE. Gauging the performance of SNPs, biomarkers, and clinical factors for predicting risk of breast cancer. J Natl Cancer Inst. 2008;100(14):978–9.PubMedCrossRef
31.
go back to reference Pepe MS. The statistical evaluation of medical tests for classification and prediction. Oxford, UK: Oxford University Press; 2003. Section 9.2, Incorporating the time dimension; p. 259–67. Pepe MS. The statistical evaluation of medical tests for classification and prediction. Oxford, UK: Oxford University Press; 2003. Section 9.2, Incorporating the time dimension; p. 259–67.
32.
go back to reference Pencina MJ, D'Agostino RB. Overall C as a measure of discrimination in survival analysis: model specific population value and confidence interval estimation. Stat Med. 2004;23(13):2109–23.PubMedCrossRef Pencina MJ, D'Agostino RB. Overall C as a measure of discrimination in survival analysis: model specific population value and confidence interval estimation. Stat Med. 2004;23(13):2109–23.PubMedCrossRef
33.
go back to reference Pepe MS, Zheng Y, Jin Y, et al. Evaluating the ROC performance of markers for future events. Lifetime Data Anal. 2008;14(1):86–113.PubMedCrossRef Pepe MS, Zheng Y, Jin Y, et al. Evaluating the ROC performance of markers for future events. Lifetime Data Anal. 2008;14(1):86–113.PubMedCrossRef
34.
go back to reference Grundy SM, Cleeman JI, Merz CN, et al. Implications of recent clinical trials for the National Cholesterol Education Program Adult Treatment Panel III guidelines. Circulation. 2004;110(2):227–39.PubMedCrossRef Grundy SM, Cleeman JI, Merz CN, et al. Implications of recent clinical trials for the National Cholesterol Education Program Adult Treatment Panel III guidelines. Circulation. 2004;110(2):227–39.PubMedCrossRef
35.
go back to reference Cook NR, Ridker PM. Advances in measuring the effect of individual predictors of cardiovascular risk: the role of reclassification measures. Ann Intern Med. 2009;150(11):795–802.PubMed Cook NR, Ridker PM. Advances in measuring the effect of individual predictors of cardiovascular risk: the role of reclassification measures. Ann Intern Med. 2009;150(11):795–802.PubMed
36.
go back to reference Janes H, Pepe MS, Gu W. Assessing the value of risk predictions by using risk stratification tables. Ann Intern Med. 2008;149(10):751–60.PubMed Janes H, Pepe MS, Gu W. Assessing the value of risk predictions by using risk stratification tables. Ann Intern Med. 2008;149(10):751–60.PubMed
37.
go back to reference Ankle Brachial Index Collaboration, Fowkes FG, Murray GD, et al. Ankle brachial index combined with Framingham Risk Score to predict cardiovascular events and mortality: a meta-analysis. JAMA. 2008;300(2):197–208.PubMedCrossRef Ankle Brachial Index Collaboration, Fowkes FG, Murray GD, et al. Ankle brachial index combined with Framingham Risk Score to predict cardiovascular events and mortality: a meta-analysis. JAMA. 2008;300(2):197–208.PubMedCrossRef
38.
go back to reference Meigs JB, Shrader P, Sullivan LM, et al. Genotype score in addition to common risk factors for prediction of type 2 diabetes. N Engl J Med. 2008;359(21):2208–19.PubMedCrossRef Meigs JB, Shrader P, Sullivan LM, et al. Genotype score in addition to common risk factors for prediction of type 2 diabetes. N Engl J Med. 2008;359(21):2208–19.PubMedCrossRef
39.
go back to reference Pigeon JG, Heyse JF. An improved goodness of fit statistic for probability prediction models. Biom J. 1999;41(1):71–82.CrossRef Pigeon JG, Heyse JF. An improved goodness of fit statistic for probability prediction models. Biom J. 1999;41(1):71–82.CrossRef
40.
go back to reference Pencina MJ, D'Agostino RB Sr, D'Agostino RB Jr, et al. Evaluating the added predictive ability of a new marker: from area under the ROC curve to reclassification and beyond. Stat Med. 2008;27(2):157–72. discussion 207–12.PubMedCrossRef Pencina MJ, D'Agostino RB Sr, D'Agostino RB Jr, et al. Evaluating the added predictive ability of a new marker: from area under the ROC curve to reclassification and beyond. Stat Med. 2008;27(2):157–72. discussion 207–12.PubMedCrossRef
41.
go back to reference Pencina MJ, D'Agostino RB Sr, Steyerberg EW. Extensions of net reclassifcation improvement calculations to measure usefulness of new biomarkers. Stat Med. 2011;30:11–21.PubMedCrossRef Pencina MJ, D'Agostino RB Sr, Steyerberg EW. Extensions of net reclassifcation improvement calculations to measure usefulness of new biomarkers. Stat Med. 2011;30:11–21.PubMedCrossRef
42.
go back to reference Moskowitz CS, Pepe MS. Comparing the predictive values of diagnostic tests: sample size and analysis for paired study designs. Clin Trials. 2006;3(3):272–9.PubMedCrossRef Moskowitz CS, Pepe MS. Comparing the predictive values of diagnostic tests: sample size and analysis for paired study designs. Clin Trials. 2006;3(3):272–9.PubMedCrossRef
43.
go back to reference Leisenring W, Alonzo T, Pepe MS. Comparisons of predictive values of binary medical diagnostic tests for paired designs. Biometrics. 2000;56(2):345–51.PubMedCrossRef Leisenring W, Alonzo T, Pepe MS. Comparisons of predictive values of binary medical diagnostic tests for paired designs. Biometrics. 2000;56(2):345–51.PubMedCrossRef
44.
go back to reference Graf E, Schmoor C, Sauerbrei W, et al. Assessment and comparison of prognostic classification schemes for survival data. Stat Med. 1999;18(17–18):2529–45.PubMedCrossRef Graf E, Schmoor C, Sauerbrei W, et al. Assessment and comparison of prognostic classification schemes for survival data. Stat Med. 1999;18(17–18):2529–45.PubMedCrossRef
45.
go back to reference Royston P, Sauerbrei W. A new measure of prognostic separation in survival data. Stat Med. 2004;23(5):723–48.PubMedCrossRef Royston P, Sauerbrei W. A new measure of prognostic separation in survival data. Stat Med. 2004;23(5):723–48.PubMedCrossRef
46.
go back to reference Huang Y, Sullivan Pepe M, Feng Z. Evaluating the predictiveness of a continuous marker. Biometrics. 2007;63(4):1181–8.PubMedCrossRef Huang Y, Sullivan Pepe M, Feng Z. Evaluating the predictiveness of a continuous marker. Biometrics. 2007;63(4):1181–8.PubMedCrossRef
47.
go back to reference Pepe MS, Feng Z, Huang Y, et al. Integrating the predictiveness of a marker with its performance as a classifier. Am J Epidemiol. 2008;167(3):362–8.PubMedCrossRef Pepe MS, Feng Z, Huang Y, et al. Integrating the predictiveness of a marker with its performance as a classifier. Am J Epidemiol. 2008;167(3):362–8.PubMedCrossRef
48.
go back to reference Vickers AJ, Elkin EB. Decision curve analysis: a novel method for evaluating prediction models. Med Decis Making. 2006;26(6):565–74.PubMedCrossRef Vickers AJ, Elkin EB. Decision curve analysis: a novel method for evaluating prediction models. Med Decis Making. 2006;26(6):565–74.PubMedCrossRef
49.
go back to reference Dear KB. Iterative generalized least squares for meta-analysis of survival data at multiple times. Biometrics. 1994;50(4):989–1002.PubMedCrossRef Dear KB. Iterative generalized least squares for meta-analysis of survival data at multiple times. Biometrics. 1994;50(4):989–1002.PubMedCrossRef
50.
go back to reference Arends LR, Hunink MG, Stijnen T. Meta-analysis of summary survival curve data. Stat Med. 2008;27(22):4381–96.PubMedCrossRef Arends LR, Hunink MG, Stijnen T. Meta-analysis of summary survival curve data. Stat Med. 2008;27(22):4381–96.PubMedCrossRef
Metadata
Title
Chapter 12: Systematic Review of Prognostic Tests
Authors
Thomas S. Rector, PhD
Brent C. Taylor, PhD
Timothy J. Wilt, MD, MPH
Publication date
01-06-2012
Publisher
Springer-Verlag
Published in
Journal of General Internal Medicine / Issue Special Issue 1/2012
Print ISSN: 0884-8734
Electronic ISSN: 1525-1497
DOI
https://doi.org/10.1007/s11606-011-1899-y

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