title: Assessment of Confounders in Comparative Effectiveness Studies From Secondary Databases.
abstract: Secondary clinical databases are an important and growing source of data for comparative effectiveness research (CER) studies. However, measurement of confounders, such as biomarker values or patient-reported health status, in secondary clinical databases may not align with the initiation of a new treatment. In many published CER analyses of registry data, investigators assessed confounders based on the first questionnaire in which the new exposure was recorded. However, it is known that adjustment for confounders measured after the start of exposure can lead to biased treatment effect estimates. In the present study, we conducted simulations to compare assessment strategies for a dynamic clinical confounder in a registry-based comparative effectiveness study of 2 therapies. As expected, we found that adjustment for the confounder value at the time of the first questionnaire after the start of exposure creates a biased estimate the total effect of exposure choice on outcome when the confounder mediates part of the effect. However, adjustment for the prior value can also be badly biased when measured long before exposure initiation. Thus, investigators should carefully consider the timing of confounder measurements relative to exposure initiation and the rate of change in the confounder in order to choose the most relevant measure for each patient.
authors.affiliation: Division of Pharmacoepidemiology, Brigham and Women's Hospital, Boston, MA, USA.
authors.forename: Jessica M
authors.initials: JM
authors.lastname: Franklin
authors.affiliation: Division of Pharmacoepidemiology and Pharmacoeconomics, Department of Medicine, Brigham and Women's Hospital and Harvard Medical School, 1620 Tremont St. Boston, MA,USA.|Department of Epidemiology, Harvard T.H. Chan School of Public Health, Boston, MA, USA.
authors.forename: Sebastian
authors.initials: S
authors.lastname: Schneeweiss
authors.affiliation: Division of Rheumatology, Immunology and Allergy, Brigham and Women's Hospital and Harvard Medical School, Boston, USA.
authors.forename: Daniel H
authors.initials: DH
authors.lastname: Solomon
mesh_terms: D061067:Antibodies, Monoclonal, Humanized / Q000009:adverse effects / Q000627:therapeutic use; D018501:Antirheumatic Agents / Q000009:adverse effects / Q000627:therapeutic use; D001172:Arthritis, Rheumatoid / Q000150:complications / Q000188:drug therapy; D015982:Bias; D015415:Biomarkers / Q000032:analysis; D057186:Comparative Effectiveness Research / Q000379:methods / Q000592:standards* / Q000706:statistics & numerical data; D003198:Computer Simulation; D015986:Confounding Factors, Epidemiologic; D016208:Databases, Factual; D006801:Humans; D007239:Infections / Q000453:epidemiology / Q000209:etiology; D016015:Logistic Models; D017063:Outcome Assessment, Health Care / Q000379:methods* / Q000592:standards / Q000706:statistics & numerical data; D063868:Patient Outcome Assessment; D012042:Registries
chemical_list: D061067:Antibodies, Monoclonal, Humanized; D018501:Antirheumatic Agents; D015415:Biomarkers; C502936:tocilizumab
keywords: bias; biomarkers; confounding; measurement error; patient-reported outcomes; registries