Quantitative assessment of unobserved confounding is mandatory in nonrandomized intervention studies

• Published in: Journal of clinical epidemiology 2009-01, Vol.62 (1), p.22-28
• Main Authors: Groenwold, R.H.H, Hak, E, Hoes, A.W
• Format: Article
• Language: English
• Subjects: Age Factors; Bias; Biological and medical sciences; Clinical trials; Clinical Trials as Topic; Comorbidity; Confounding factors; Confounding Factors (Epidemiology); Data collection; Data Interpretation, Statistical; Disease; Effectiveness studies; Epidemiologic methods; Epidemiologic Research Design; Epidemiology; General aspects; Human viral diseases; Humans; Infectious diseases; Influenza; Influenza vaccines; Influenza Vaccines - administration & dosage; Internal Medicine; Intervention; Medical sciences; Miscellaneous; Mortality; Multivariate analysis; Patient Selection; Patients; Population; Population Groups; Public health. Hygiene; Public health. Hygiene-occupational medicine; Sensitivity analysis; Vaccines; Viral diseases; Viral diseases of the respiratory system and ent viral diseases
• ISSN: 0895-4356; 1878-5921
• DOI: 10.1016/j.jclinepi.2008.02.011

Abstract Objective In nonrandomized intervention studies unequal distribution of patient characteristics in the groups under study may hinder comparability of prognosis and therefore lead to confounding bias. Our objective was to review methods to control for observed confounding, as well as unobserved confounding Study Design and Setting We reviewed epidemiologic literature on methods to control for observed and unobserved confounding. Results Various methods are available to control for observed (i.e., measured) confounders, either in the design of data collection (i.e., matching, restriction), or in data analysis (i.e., multivariate analysis, propensity score analysis). Methods to quantify unobserved confounding can be categorized in methods with and without prior knowledge of the effect estimate. Without prior knowledge of the effect estimate, unobserved confounding can be quantified using different types of sensitivity analysis. When prior knowledge is available, the size of unobserved confounding can be estimated directly by comparison with prior knowledge. Conclusion Unobserved confounding should be addressed in a quantitative way to value the inferences of nonrandomized intervention studies.