title: Estimation using all available covariate information versus a fixed look-back window for dichotomous covariates.
abstract: OBJECTIVE
  When using claims data, dichotomous covariates (C) are often assumed to be absent unless a claim for the condition is observed. When available historical data differs among subjects, investigators must choose between using all available historical data versus data from a fixed window to assess C. Our purpose was to compare estimation under these two approaches.
  
  
  METHODS
  We simulated cohorts of 20,000 subjects with dichotomous variables representing exposure (E), outcome (D), and a single time-invariant C, as well as varying availability of historical data. C was operationally defined under each paradigm and used to estimate the adjusted risk ratio of E on D via Mantel-Haenszel methods.
  
  
  RESULTS
  In the base case scenario, less bias and lower mean square error were observed using all available information compared with a fixed window; differences were magnified at higher modeled confounder strength. Upon introduction of an unmeasured covariate (F), the all-available approach remained less biased in most circumstances and rendered estimates that better approximated those that were adjusted for the true (modeled) value of C in all instances.
  
  
  CONCLUSIONS
  In most instances considered, operationally defining time-invariant dichotomous C based on all available historical data, rather than on data observed over a commonly shared fixed historical window, results in less biased estimates.
authors.affiliation: Division of Pharmacoepidemiology and Pharmacoeconomics, Department of Medicine, Brigham and Women's Hospital and Harvard Medical School, Boston, MA 02120, USA. sbrunelli@partners.org
authors.forename: Steven M
authors.initials: SM
authors.lastname: Brunelli
authors.forename: Joshua J
authors.initials: JJ
authors.lastname: Gagne
authors.forename: Krista F
authors.initials: KF
authors.lastname: Huybrechts
authors.forename: Shirley V
authors.initials: SV
authors.lastname: Wang
authors.forename: Amanda R
authors.initials: AR
authors.lastname: Patrick
authors.forename: Kenneth J
authors.initials: KJ
authors.lastname: Rothman
authors.forename: John D
authors.initials: JD
authors.lastname: Seeger
mesh_terms: D015982:Bias; D015331:Cohort Studies; D015986:Confounding Factors, Epidemiologic; D003625:Data Collection / Q000379:methods; D003627:Data Interpretation, Statistical; D016208:Databases, Factual / Q000706:statistics & numerical data*; D004812:Epidemiologic Methods; D006801:Humans; D017063:Outcome Assessment, Health Care / Q000379:methods; D017891:Pharmacoepidemiology / Q000379:methods*; D013997:Time Factors