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Estimating Additive Interaction Effect in Stratified Two-Phase Case-Control Design.

Ai Ni1, Jaya M Satagopan2

  • 1Division of Biostatistics, The Ohio State University, Columbus, Ohio, USA, ni.304@osu.edu.

Human Heredity
|October 22, 2019
PubMed
Summary
This summary is machine-generated.

Estimating additive interaction effects in stratified case-control studies requires careful method selection. The multiple imputation method is recommended for additive models, while the offset method is best for non-additive models to ensure accurate risk factor analysis.

Keywords:
Additive interactionInverse-probability weightingMultiple imputationOffsetStratified two-phase case-control design

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Area of Science:

  • Epidemiology
  • Biostatistics

Background:

  • Estimating additive interaction effects between risk factors is crucial in epidemiology.
  • Stratified two-phase case-control designs are cost-effective but require accounting for stratification for accurate parameter estimation.
  • Accurate estimation of additive interaction effects is vital for understanding differential risk factor impacts.

Purpose of the Study:

  • To examine the properties of different methods for estimating model parameters and additive interaction effects.
  • To compare stratum-specific offset, inverse-probability weighting, and multiple imputation methods.
  • To provide recommendations for method selection based on analysis model characteristics.

Main Methods:

  • Utilized simulation studies to evaluate three existing methods: stratum-specific offset, inverse-probability weighting, and multiple imputation.
  • Assessed the performance of these methods in estimating model parameters and additive interaction effects.
  • Illustrated method properties using data from two published epidemiology studies.

Main Results:

  • Multiple imputation performed well for additive analysis models but showed no advantage over the offset method for non-additive models.
  • The offset method demonstrated the best overall properties when the analysis model included multiplicative interaction effects.
  • Method performance varied depending on the additivity of the true and analysis models.

Conclusions:

  • Recommend multiple imputation for estimating additive interaction effects when the analysis model is additive.
  • Recommend the offset method for estimating additive interaction effects when the analysis model is non-additive.
  • Emphasize the importance of choosing the appropriate statistical method for accurate analysis of risk factor interactions in stratified case-control studies.