title: Using the landmark method for creating prediction models in large datasets derived from electronic health records.
abstract: With the integration of electronic health records (EHRs), health data has become easily accessible and abounded. The EHR has the potential to provide important healthcare information to researchers by creating study cohorts. However, accessing this information comes with three major issues: 1) Predictor variables often change over time, 2) Patients have various lengths of follow up within the EHR, and 3) the size of the EHR data can be computationally challenging. Landmark analyses provide a perfect complement to EHR data and help to alleviate these three issues. We present two examples that utilize patient birthdays as landmark times for creating dynamic datasets for predicting clinical outcomes. The use of landmark times help to solve these three issues by incorporating information that changes over time, by creating unbiased reference points that are not related to a patient's exposure within the EHR, and reducing the size of a dataset compared to true time-varying analysis. These techniques are shown using two example cohort studies from the Cleveland Clinic that utilized 4.5 million and 17,787 unique patients, respectively.
authors.affiliation: Department of Quantitative Health Sciences, Cleveland Clinic, 9500 Euclid Avenue/JJN3-01, Cleveland, OH, 44195, USA, wellsb@ccf.org.
authors.forename: Brian J
authors.initials: BJ
authors.lastname: Wells
authors.forename: Kevin M
authors.initials: KM
authors.lastname: Chagin
authors.forename: Liang
authors.initials: L
authors.lastname: Li
authors.forename: Bo
authors.initials: B
authors.lastname: Hu
authors.forename: Changhong
authors.initials: C
authors.lastname: Yu
authors.forename: Michael W
authors.initials: MW
authors.lastname: Kattan
mesh_terms: D000328:Adult; D000367:Age Factors; D000368:Aged; D000369:Aged, 80 and over; D001774:Blood Chemical Analysis; D015331:Cohort Studies; D015897:Comorbidity; D057286:Electronic Health Records / Q000706:statistics & numerical data*; D005260:Female; D005544:Forecasting / Q000379:methods*; D006403:Hematologic Tests; D006801:Humans; D008297:Male; D008875:Middle Aged; D015233:Models, Statistical; D044469:Racial Groups; D018570:Risk Assessment / Q000379:methods*; D012737:Sex Factors; D013997:Time Factors; D014481:United States
references.citation: JAMA. 1982 May 14;247(18):2543-6
references.pmid: 7069920
references.citation: Urology. 2012 Feb;79(2):298-302
references.pmid: 22088569
references.citation: J Clin Oncol. 1983 Nov;1(11):710-9
references.pmid: 6668489
references.citation: Acad Radiol. 2012 Dec;19(12):1465-73
references.pmid: 23122566
references.citation: EGEMS (Wash DC). 2013 Dec 17;1(3):1035
references.pmid: 25848578
references.citation: J Am Med Inform Assoc. 2013 Jun;20(e1):e33-8
references.pmid: 23538721
references.citation: PeerJ. 2013 Jun 11;1:e87
references.pmid: 23781409
references.citation: Lancet. 2002 Jun 1;359(9321):1929-36
references.pmid: 12057569
references.citation: Respir Care. 2002 Oct;47(10):1184-99
references.pmid: 12354338
references.citation: Transl Res. 2009 Aug;154(2):78-89
references.pmid: 19595439
references.citation: Circ Cardiovasc Qual Outcomes. 2011 May;4(3):363-71
references.pmid: 21586725