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Inverse probability weighting in nested case-control studies with additional matching--a simulation study.

Nathalie C Støer1, Sven Ove Samuelsen

  • 1Department of Mathematics, University of Oslo, PO Box 1053, 0316 Oslo, Norway.

Statistics in Medicine
|October 18, 2013
PubMed
Summary
This summary is machine-generated.

Nested case-control studies can improve efficiency by reusing controls with inverse probability weighting. Proper adjustment for matching variables is crucial when this matching is broken to avoid bias in parameter estimates.

Keywords:
inverse probability weightingmatchingnested case-controlproportional hazardweighted partial likelihood

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Area of Science:

  • Epidemiology
  • Biostatistics

Background:

  • Nested case-control designs offer efficiency but can be improved.
  • Reusing controls via inverse probability weighting is a potential method for efficiency gains, especially with multiple endpoints.
  • Additional matching factors beyond risk status are common in nested case-control studies and require careful handling when reusing controls.

Purpose of the Study:

  • To investigate the impact of additional matching on parameter estimates and weights when controls are reused in nested case-control designs.
  • To provide a thorough discussion and develop guidelines for practitioners on handling additional matching in control reuse.
  • To assess the bias introduced by additional matching and batch effects when matching is broken.

Main Methods:

  • Development and presentation of three types of estimators for inverse sampling probabilities, accounting for additional matching.
  • Demonstration of the importance of adjusting for matching variables in regression analyses when matching is broken.
  • Simulation studies based on a prostate cancer and vitamin D cohort to evaluate bias and effects of matching and batch variables.

Main Results:

  • Adjusting for matching variables in regression analyses is essential when matching is broken.
  • One proposed estimator showed some bias with very close matching.
  • Strong associations between matching variables and exposure/outcome introduced minimal bias when matching variables were properly adjusted for.
  • Significant batch effects were required to introduce substantial bias when matching was broken.

Conclusions:

  • Guidelines for practitioners are proposed for reusing controls in nested case-control studies with additional matching.
  • Proper adjustment for matching variables is key to minimizing bias when matching is broken.
  • The methods discussed are robust to moderate batch effects and associations between matching variables and exposure/outcome.