title: Effects of expanding the look-back period to all available data in the assessment of covariates.
abstract: BACKGROUND
  A fixed baseline period has been a common covariate assessment approach in pharmacoepidemiological studies from claims but may lead to high levels of covariate misclassification. Simulation studies have recommended expanding the look-back approach to all available data (AAD) for binary indicators of diagnoses, procedures, and medications, but there have been few real data analyses using this approach.
  
  
  OBJECTIVE
  The objective of the study is to explore the impact on treatment effect estimates and covariate prevalence of expanding the look-back period within five validated studies in the Aetion system, a rapid cycle analytics platform.
  
  
  METHODS
  We reran the five studies and assessed covariates using (i) a fixed window approach (usually 180 days before treatment initiation), (ii) AAD prior to treatment initiation, and (iii) AAD with a categorized by recency approach, where the most recent occurrence of a covariate was labeled as recent (occurring within the fixed window) or past (before the start of the fixed window). For each covariate assessment approach, we adjusted for covariates via propensity score matching.
  
  
  RESULTS
  All studies had at least one covariate that had an increase in prevalence of 15% or higher from the fixed window to the AAD approach. However, there was little change in treatment effect estimates resulting from differing covariate assessment approaches. For example, in a study of acute coronary syndrome in high-intensity versus low-intensity statin users, the estimated hazard ratio from the fixed window approach was 1.11 (95% confidence interval 0.98, 1.25) versus 1.21 (1.07, 1.37) when using AAD and 1.19 (1.05, 1.35) using categorized by recency.
  
  
  CONCLUSIONS
  Expanding the baseline period to AAD improved covariate sensitivity by capturing data that would otherwise be missed yet did not meaningfully change the overall treatment effect estimates compared with the fixed window approach. Copyright © 2017 John Wiley & Sons, Ltd.
authors.affiliation: Division of Pharmacoepidemiology & Pharmacoeconomics, Department of Medicine, Brigham & Women's Hospital and Harvard Medical School, Boston, MA, USA.|Aetion, Inc., New York, NY, USA.|Ludwig-Maximilians University of Munich, Munich, Germany.
authors.forename: Sonja S
authors.identifier: http://orcid.org/0000-0003-4223-246X
authors.initials: SS
authors.lastname: Nakasian
authors.affiliation: Aetion, Inc., New York, NY, USA.
authors.forename: Jeremy A
authors.initials: JA
authors.lastname: Rassen
authors.affiliation: Division of Pharmacoepidemiology & Pharmacoeconomics, Department of Medicine, Brigham & Women's Hospital and Harvard Medical School, Boston, MA, USA.
authors.forename: Jessica M
authors.identifier: http://orcid.org/0000-0002-8941-4116
authors.initials: JM
authors.lastname: Franklin
mesh_terms: D000894:Anti-Inflammatory Agents, Non-Steroidal / Q000009:adverse effects; D003627:Data Interpretation, Statistical; D006471:Gastrointestinal Hemorrhage / Q000139:chemically induced / Q000453:epidemiology; D006801:Humans; D019161:Hydroxymethylglutaryl-CoA Reductase Inhibitors / Q000009:adverse effects; D007345:Insurance Claim Review / Q000706:statistics & numerical data*; D064887:Observational Studies as Topic / Q000379:methods / Q000706:statistics & numerical data; D017063:Outcome Assessment, Health Care / Q000379:methods / Q000706:statistics & numerical data*; D010195:Pancreatitis / Q000139:chemically induced / Q000453:epidemiology; D017891:Pharmacoepidemiology / Q000379:methods / Q000706:statistics & numerical data*; D016896:Treatment Outcome
chemical_list: D000894:Anti-Inflammatory Agents, Non-Steroidal; D019161:Hydroxymethylglutaryl-CoA Reductase Inhibitors
keywords: bias; confounding variables; data analysis; misclassification; pharmacoepidemiology; statistical