Using the landmark method for creating prediction models in large datasets derived from electronic health records

Health Care Management Science

Volumn 18, Issue 1, 2015, Pages 86-92

  Wells, Brian J ^a   Chagin, Kevin M ^a   Li, Liang ^b   Hu, Bo ^a   Yu, Changhong ^a   Kattan, Michael W ^a  

^a LERNER RESEARCH INSTITUTE   (United States)

^b UNIVERSITY OF TEXAS MD ANDERSON CANCER CENTER   (United States)

Author keywords

Clinical research informatics; Electronic health records; Landmark analysis; Risk prediction

Indexed keywords

ADULT; AGE; AGED; ANCESTRY GROUP; BLOOD ANALYSIS; BLOOD EXAMINATION; COHORT ANALYSIS; COMORBIDITY; ELECTRONIC MEDICAL RECORD; FEMALE; FORECASTING; HUMAN; MALE; MIDDLE AGED; PROCEDURES; RISK ASSESSMENT; SEX DIFFERENCE; STATISTICAL MODEL; STATISTICS AND NUMERICAL DATA; TIME; UNITED STATES; VERY ELDERLY;

ADULT; AGE FACTORS; AGED; AGED, 80 AND OVER; BLOOD CHEMICAL ANALYSIS; COHORT STUDIES; COMORBIDITY; CONTINENTAL POPULATION GROUPS; ELECTRONIC HEALTH RECORDS; FEMALE; FORECASTING; HEMATOLOGIC TESTS; HUMANS; MALE; MIDDLE AGED; MODELS, STATISTICAL; RISK ASSESSMENT; SEX FACTORS; TIME FACTORS; UNITED STATES;

EID: 84939886481     PISSN: 13869620     EISSN: 15729389     Source Type: Journal    
DOI: 10.1007/s10729-014-9281-3     Document Type: Article

References (18)

1. Kokkonen EW, Davis SA, Lin HC, Dabade TS, Feldman SR, Fleischer AB Jr (2013) Use of electronic medical records differs by specialty and office settings. J Am Med Inform Assoc 20:e33–8
2. Shapiro S, Rosenberg L (2005) Bias in Case Control Studies. In: Peter Armitage, Theodore Colton (eds) Encyclopedia of Biostatistics. John Wiley & Sons, Ltd
3. US Preventive Services Task Force (1989) Guide to clinical preventive services: report of the US Preventive Services Task Force. DIANE publishing
4. Kalbfleisch J, Prentice R (2002) The Statistical Analysis of Failure Time Data, 2nd edn. Wiley, New York
5. Kanis JA (2002) Diagnosis of osteoporosis and assessment of fracture risk. The Lancet 359:1929–1936
6. Buuren S, Groothuis-Oudshoorn K (2011) MICE: Multivariate imputation by chained equations in R. J stat softw 45
7. Anderson JR, Cain KC, Gelber RD (1983) Analysis of survival by tumor response. J Clin Oncol 1:710–719
8. Dafni U (2011) Landmark analysis at the 25-year landmark point. Circulation: Cardiovasc Qual and Outcomes 4:363–371
9. de Cogain M, Krambeck AE, Rule AD, Li X, Bergstralh EJ, Gettman MT, Lieske JC (2012) Shock wave lithotripsy and diabetes mellitus: a population-based cohort study. Urology 79:298–302
10. Amin AP, Mukhopadhyay E, Nathan S, Napan S, Kelly RF (2009) Association of medical noncompliance and long-term adverse outcomes, after myocardial infarction in a minority and uninsured population. Transl Res 154:78–89
11. Parast L, Cai B, Bedayat A, Kumamaru KK, George E, Dill KE, Rybicki FJ (2012) Statistical methods for predicting mortality in patients diagnosed with acute pulmonary embolism. Acad Radiol 19:1465–1473
12. Therneau TM, Grambsch PM (2000) Modeling survival data: extending the Cox model. Springer, New York
13. Harrell FE Jr, Califf RM, Pryor DB, Lee KL, Rosati RA (1982) Evaluating the yield of medical tests. JAMA 247:2543–2546
14. Harrell FE (2001) Regression modeling strategies: with applications to linear models, logistic regression, and survival analysis. Springer, New York
15. Wells BJ, Nowacki AS, Chagin K, Kattan MW (2013) Strategies for Handling Missing Data in Electronic Health Record Derived Data. eGEMs 1(3):Article 7
16. Wells BJ, Roth R, Nowacki AS, Arrigain S, Yu C, Rosenkrans WA Jr, Kattan MW (2013) Prediction of morbidity and mortality in patients with type 2 diabetes. PeerJ 1:e87
17. Mannino DM, Homa DM, Akinbami LJ, Ford ES, Redd SC (2002) Chronic obstructive pulmonary disease surveillance–United States, 1971–2000. Respir Care 47:1184–1199
18. van Houwelingen JC, Putter H (2012) Dynamic prediction in clinical survival analysis. CRC Press, Boca Raton