A method for the early health technology assessment of novel biomarker measurement in primary prevention programs

Statistics in Medicine

Volumn 31, Issue 23, 2012, Pages 2733-2744

  Postmus, Douwe ^a   de Graaf, Gimon ^a   Hillege, Hans L ^a   Steyerberg, Ewout W ^b   Buskens, Erik ^a  

^a UNIVERSITY MEDICAL CENTER GRONINGEN   (Netherlands)

^b ERASMUS MEDICAL CENTER   (Netherlands)

Author keywords

Biomarker; Cost effectiveness analysis; Early health technology assessment; Primary prevention

Indexed keywords

ARTICLE; BIOMEDICAL TECHNOLOGY ASSESSMENT; COST EFFECTIVENESS ANALYSIS; COST UTILITY ANALYSIS; DISEASE MARKER; HEALTH PROGRAM; HEALTH STATUS; HIGH RISK POPULATION; PATIENT SELECTION; PREDICTIVE VALUE; PRIMARY PREVENTION; PROBABILITY; QUALITY ADJUSTED LIFE YEAR; QUANTITATIVE ANALYSIS; SENSITIVITY ANALYSIS;

BIOLOGICAL MARKERS; BIOMEDICAL TECHNOLOGY; COST-BENEFIT ANALYSIS; DIABETES MELLITUS, TYPE 2; HUMANS; MODELS, ECONOMIC; MODELS, STATISTICAL; PRIMARY PREVENTION; TECHNOLOGY ASSESSMENT, BIOMEDICAL;

EID: 84866447372     PISSN: 02776715     EISSN: 10970258     Source Type: Journal    
DOI: 10.1002/sim.5434     Document Type: Article

References (27)

1. Hlatky M, Greenland P, Arnett D, Ballantyne C, Criqui M, Elkind M, Go A, Harrell F Jr, Hong Y, Howard B, Howard V, Hsue P, Kramer C, McConnell J, Normand S, O'Donnell C, Smith S Jr, Wilson P. Criteria for evaluation of novel markers of cardiovascular risk: a scientific statement from the american heart association. Circulation 2009; 119(17):2408-2416. DOI: 10.1161/CIRCULATIONAHA.109.192278.
2. Pietzsch J, Paté-Cornell M. Early technology assessment of new medical devices. International Journal of Technology Assessment in Health Care 2008; 24(1):36-44. DOI: 10.1017/S0266462307080051.
3. Chambless L, Cummiskey C, Cui G. Several methods to assess improvement in risk prediction models: extension to survival analysis. Statistics in Medicine 2011; 30(1):22-38. DOI: 10.1002/sim.4026.
4. Chambless L, Diao G. Estimation of time-dependent area under the ROC curve for long-term risk prediction. Statistics in Medicine 2006; 25(20):3474-3486. DOI: 10.1002/sim.2299.
5. Cook N, Ridker P. Advances in measuring the effect of individual predictors of cardiovascular risk: the role of reclassification measures. Annals of Internal Medicine 2009; 150(11):795-802. DOI: 10.1059/0003-4819-150-11-200906020-00007.
6. Heagerty P, Zheng Y. Survival model predictive accuracy and ROC curves. Biometrics 2005; 61(1):92-105. DOI: 10.1111/j.0006-341X.2005.030814.x.
7. Pencina M, D'Agostino R Sr, D'Agostino R Jr, Vasan RS. Evaluating the added predictive ability of a new marker: from area under the ROC curve to reclassification and beyond. Statistics in Medicine 2008; 27(2):157-172. DOI: 10.1002/sim.2995.
8. Pencina M, D'Agostino R Sr, Steyerberg E. Extensions of net reclassification improvement calculations to measure usefulness of new biomarkers. Statistics in Medicine 2011; 30(1):11-21. DOI: 10.1002/sim.4085.
9. Vickers A, Elkin E. Decision curve analysis: a novel method for evaluating prediction models. Medical Decision Making 2006; 26(6):565-574. DOI: 10.1177/0272989X06295361.
10. Hemingway H, Henriksson M, Chen R, Damant J, Fitzpatrick N, Abrams K, Hingorani A, Janzon M, Shipley M, Feder G, Keogh B, Stenestrand U, McAllister K, Kaski J, Timmis A, Palmer S, Sculpher M. The effectiveness and cost-effectiveness of biomarkers for the prioritisation of patients awaiting coronary revascularisation: a systematic review and decision model. Health Technology Assessment 2010; 14(9):1-178. DOI: 10.3310/hta14090.
11. Henriksson M, Palmer S, Chen R, Damant J, Fitzpatrick N, Abrams K, Hingorani A, Stenestrand U, Janzon M, Feder G, Keogh B, Shipley M, Kaski J, Timmis A, Sculpher M, Hemingway H. Assessing the cost effectiveness of using prognostic biomarkers with decision models: case study in prioritising patients waiting for coronary artery surgery. British Medical Journal 2010; 340:b5606. DOI: 10.1136/bmj.b5606.
12. IJzerman M, Steuten L. Early assessment of medical technologies to inform product development and market access: a review of methods and applications. Applied Health Economics and Health Policy 2011; 9(5):331-347. DOI: 10.2165/11593380-000000000-00000.
13. Cosh E, Girling A, Lilford R, McAteer H, Young T. Investing in new medical technologies: a decision framework. Journal of Commercial Biotechnology 2007; 13(4):263-271. DOI: 10.1057/palgrave.jcb.3050062.
14. Steyerberg E, Vickers A, Cook N, Gerds T, Gonen M, Obuchowski N, Pencina M, Kattan M. Assessing the performance of prediction models: a framework for traditional and novel measures. Epidemiology 2010; 21(1):128-138. DOI: 10.1097/EDE.0b013e3181c30fb2.
15. Drummond M, Sculpher M, Torrance G, O'brien B, Stoddart G. Methods for the Economic Evaluation of Health Care Programmes. Oxford University Press: New York, 2005.
16. Briggs AH, Claxton K, Sculpher M. Decision Modelling for Health Economic Evaluation. Oxford University Press: New York, 2006.
17. GrootKoerkamp B, Stijnen T, Weinstein M, Hunink M. The combined analysis of uncertainty and patient heterogeneity in medical decision models. Medical Decision Making 2011; 31(4):650-661. DOI: 10.1177/0272989X10381282.
18. Garthwaite P, Kadane J, O'Hagan A. Statistical methods for eliciting probability distributions. Journal of the American Statistical Association 2005; 100(470):680-701. DOI: 10.1198/016214505000000105.
19. Vallejo-Torres L, Steuten L, Parkinson B, Girling A, Buxton M. Integrating health economics into the product development cycle: a case study of absorbable pins for treating hallux valgus. Medical Decision Making 2011; 31(4):596-610. DOI: 10.1177/0272989X10388041.
20. Briggs A, Ades A, Price M. Probabilistic sensitivity analysis for decision trees with multiple branches: use of the Dirichlet distribution in a Bayesian framework. Medical Decision Making 2003; 23(4):341-350. DOI: 10.1177/0272989X03255922.
21. Gillies C, Abrams K, Lambert P, Cooper N, Sutton A, Hsu R, Khunti K. Pharmacological and lifestyle interventions to prevent or delay type 2 diabetes in people with impaired glucose tolerance: systematic review and meta-analysis. British Medical Journal 2007; 334(7588):299. DOI: 10.1136/bmj.39063.689375.55.
22. Salomaa V, Havulinna A, Saarela O, Zeller T, Jousilahti P, Jula A, Muenzel T, Aromaa A, Evans A, Kuulasmaa K, Blankenberg S. Thirty-one novel biomarkers as predictors for clinically incident diabetes. PLoS ONE 2010; 5(4):e10100. DOI: 10.1371/journal.pone.0010100.
23. Gillies C, Lambert P, Abrams K, Sutton A, Cooper N, Hsu R, Davies M, Khunti K. Different strategies for screening and prevention of type 2 diabetes in adults: cost effectiveness analysis. British Medical Journal 2008; 336(7654):1180-1185. DOI: 10.1136/bmj.39545.585289.25.
24. Roumen C, Corpeleijn E, Feskens E, Mensink M, Saris W, Blaak E. Impact of 3-year lifestyle intervention on postprandial glucose metabolism: the SLIM study. Diabetic Medicine 2008; 25(5):597-605. DOI: 10.1111/j.1464-5491.2008.02417.x.
25. Sullivan P, Ghushchyan V. Preference-based EQ-5D index scores for chronic conditions in the United States. Medical Decision Making 2006; 26(4):410-420. DOI: 10.1177/0272989X06290495.
26. Claxton K, Briggs A, Buxton M, Culyer A, McCabe C, Walker S, Sculpher M. Value based pricing for NHS drugs: an opportunity not to be missed? British Medical Journal 2008; 336(7638):251-254. DOI: 10.1136/bmj.39434.500185.25.
27. Girling A, Young T, Brown C, Lilford R. Early-stage valuation of medical devices: the role of developmental uncertainty. Value in Health 2010; 13(5):585-591. DOI: 10.1111/j.1524-4733.2010.00726.x.