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.
- Age Factors
- Bias (Epidemiology)
- Clinical Trials as Topic
- Confounding Factors (Epidemiology)
- Data Interpretation, Statistical
- Epidemiologic Research Design
- Influenza Vaccines
- Patient Selection
- Population Groups