Inverse probability weighting to handle attrition in cohort studies: some guidance and a call for caution

BMC Med Res Methodol. 2022 Feb 16;22(1):45. doi: 10.1186/s12874-022-01533-9.

Abstract

Background: Attrition in cohort studies challenges causal inference. Although inverse probability weighting (IPW) has been proposed to handle attrition in association analyses, its relevance has been little studied in this context. We aimed to investigate its ability to correct for selection bias in exposure-outcome estimation by addressing an important methodological issue: the specification of the response model.

Methods: A simulation study compared the IPW method with complete-case analysis (CCA) for nine response-mechanism scenarios (3 missing at random - MAR and 6 missing not at random - MNAR). Eighteen response models differing by the type of variables included were assessed.

Results: The IPW method was equivalent to CCA in terms of bias and consistently less efficient in all scenarios, regardless of the response model tested. The most effective response model included only the confounding factors of the association model.

Conclusion: Our study questions the ability of the IPW method to correct for selection bias in situations of attrition leading to missing outcomes. If the method is to be used, we encourage including only the confounding variables of the association of interest in the response model.

Keywords: Attrition; Cohort studies; Complete-case analysis; Inverse probability weighting; Missing outcome; Selection bias.

Publication types

  • Research Support, Non-U.S. Gov't

MeSH terms

  • Bias
  • Cohort Studies
  • Confounding Factors, Epidemiologic
  • Humans
  • Probability*
  • Selection Bias