Quantitative assessment of unobserved confounding is mandatory in nonrandomized intervention studies

R H H Groenwold*, E Hak, A W Hoes

*Corresponding author for this work

Research output: Contribution to journalArticleAcademicpeer-review

46 Citations (Scopus)

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.

Original languageEnglish
Pages (from-to)22-28
Number of pages7
JournalJournal of Clinical Epidemiology
Volume62
Issue number1
DOIs
Publication statusPublished - Jan-2009
Externally publishedYes

Keywords

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

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