Unexpected predictor-outcome associations in clinical prediction research: Causes and solutions

CMAJ. Canadian Medical Association Journal

Volumn 185, Issue 10, 2013, Pages

  Schuit, Ewoud ^a   Groenwold, Rolf H H ^a   Harrell, Frank E ^b   De Kort, Wim L A M ^d   Kwee, Anneke ^a   Mol, BenWillem J ^e   Riley, Richard D ^c   Moons, Karel G M ^a  

^a UNIVERSITY MEDICAL CENTER UTRECHT   (Netherlands)

^b VANDERBILT UNIVERSITY SCHOOL OF MEDICINE   (United States)

^c UNIVERSITY OF BIRMINGHAM   (United Kingdom)

^d Sanquin Bloedbank Regio Zuidoost Nijmegen   (Netherlands)

^e ACADEMIC MEDICAL CENTER   (Netherlands)

Author keywords

[No Author keywords available]

Indexed keywords

HEMOGLOBIN; PROTEIN P53;

ANEMIA; ARTICLE; BLOOD DONOR; BLOOD PRESSURE MEASUREMENT; CANCER PROGNOSIS; CANCER SURVIVAL; CLINICAL PRACTICE; CLINICAL RESEARCH; DEEP VEIN THROMBOSIS; DISEASE FREE SURVIVAL; ECHOGRAPHY; FEVER; FRAMINGHAM RISK SCORE; HUMAN; IRON DEFICIENCY; MEDICAL ERROR; METABOLIC ACIDOSIS; NEWBORN HYPOXIA; OROPHARYNX CARCINOMA; OUTCOME ASSESSMENT; PATIENT SELECTION; PRACTICE GUIDELINE; PREDICTION; RISK ASSESSMENT; RISK FACTOR; SEX DIFFERENCE; SQUAMOUS CELL CARCINOMA; SYSTEMATIC ERROR; TUMOR SUPPRESSOR GENE;

EID: 84880009059     PISSN: 08203946     EISSN: 14882329     Source Type: Journal    
DOI: 10.1503/cmaj.120812     Document Type: Article

References (22)

1. 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;119:2408-16.
2. Reilly BM, Evans AT. Translating clinical research into clinical practice: impact of using prediction rules to make decisions. Ann Intern Med 2006;144:201-9. (Pubitemid 46780691)
3. Steyerberg EW. Clinical prediction models; a practical approach to development, validation, and updating. New York (NY): Springer; 2009. p. 1-7.
4. D'Agostino RB Sr, Vasan RS, Pencina MJ, et al. General cardiovascular risk profile for use in primary care: the Framingham Heart Study. Circulation 2008;117:743-53. (Pubitemid 351640675)
5. Moons KG, Altman DG, Vergouwe Y, et al. Prognosis and prognostic research: application and impact of prognostic models in clinical practice. BMJ 2009;338:b606.
6. Westerhuis ME, Schuit E, Kwee A, et al. Prediction of neonatal metabolic acidosis in women with a singleton term pregnancy in cephalic presentation. Am J Perinatol 2012;29:167-74.
7. Oudega R, Moons KG, Hoes AW. Ruling out deep venous thrombosis in primary care. A simple diagnostic algorithm including D-dimer testing. Thromb Haemost 2005;94:200-5. (Pubitemid 40974725)
8. Naess IA, Christiansen SC, Romundstad P, et al. Incidence and mortality of venous thrombosis: a population-based study. J Thromb Haemost 2007;5:692-9. (Pubitemid 46563566)
9. Oger E. Incidence of venous thromboembolism: a community-based study in Western France. EPI-GETBP Study Group. Groupe d'Etude de la Thrombose de Bretagne Occidentale. Thromb Haemost 2000;83:657-60.
10. Baart AM, de Kort WL, Atsma F, et al. Development and validation of a prediction model for low hemoglobin deferral in a large cohort of whole blood donors. Transfusion 2012;52:2559-69.
11. Janssen KJ, Donders AR, Harrell FE Jr, et al. Missing covariate data in medical research: To impute is better than to ignore. J Clin Epidemiol 2010;63:721-7.
12. Royston P, Altman DG, Sauerbrei W. Dichotomizing continuous predictors in multiple regression: a bad idea. Stat Med 2006;25:127-41. (Pubitemid 43136953)
13. Sauerbrei W, Royston P. Building multivariable prognostic and diagnostic models: transformation of the predictors by using fractional polynomials. J R Stat Soc Ser A Stat Soc 1999;162:71-94.
14. Cole SR, Chu H, Greenland S. Multiple-imputation for measurement-error correction. Int J Epidemiol 2006;35:1074-81.
15. de Groot JA, Dendukuri N, Janssen KJ, et al. Adjusting for differential-verification bias in diagnostic-accuracy studies: a Bayesian approach. Epidemiology 2011;22:234-41.
16. Hernán MA, Hernandez-Diaz S, Robins JM. A structural approach to selection bias. Epidemiology 2004;15:615-25. (Pubitemid 40416916)
17. Tandon S, Tudur-Smith C, Riley RD, et al. A systematic review of p53 as a prognostic factor of survival in squamous cell carcinoma of the four main anatomical subsites of the head and neck. Cancer Epidemiol Biomarkers Prev 2010;19:574-87.
18. Jackson D, Riley R, White IR. Multivariate meta-analysis: potential and promise. Stat Med 2011 Jan. 26 [Epub ahead of print].
19. Riley R. Multivariate meta-analysis: the effect of ignoring within-study correlation. JRSS Series A 2009;172:789-811.
20. Brotman DJ, Walker E, Lauer MS, et al. In search of fewer independent risk factors. Arch Intern Med 2005;165:138-45.
21. Van den Bosch JE, Moons KG, Bonsel GJ, et al. Does measurement of preoperative anxiety have added value for predicting postoperative nausea and vomiting? Anesth Analg 2005;100:1525-32. (Pubitemid 40577419)
22. Janssen KJ, Vergouwe Y, Kalkman CJ, et al. A simple method to adjust clinical prediction models to local circumstances. Can J Anaesth 2009;56:194-201.