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Valid and efficient subgroup analyses using nested case-control data.

Bénédicte Delcoigne1, Nathalie C Støer2, Marie Reilly1

  • 1Department of Medical Epidemiology and Biostatistics, Karolinska Institutet, Stockholm, Sweden.

International Journal of Epidemiology
|February 2, 2018
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Summary
This summary is machine-generated.

Subgroup analyses using nested case-control data provide unbiased exposure effect estimates. Weighted Cox regression is more efficient than conditional logistic regression for these analyses.

Keywords:
Conditional logistic regressionrisk set samplingweighted Cox regressionweighted likelihood

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Area of Science:

  • Epidemiology
  • Biostatistics

Background:

  • Nested case-control designs are common for subgroup analyses.
  • Outcome-related sampling affects data representativeness.
  • Subgroup analysis validity requires careful consideration.

Purpose of the Study:

  • To evaluate the validity of subgroup analyses in nested case-control studies.
  • To compare estimation methods for subgroup analyses.

Main Methods:

  • Simulation studies generated cohorts and nested case-control data.
  • Analyzed binary exposure effects on time-to-event outcomes in subgroups.
  • Compared conditional logistic regression and weighted Cox regression.

Main Results:

  • Subgroup analyses yielded unbiased exposure coefficient estimates across scenarios.
  • Weighted Cox regression demonstrated higher efficiency than conditional logistic regression.

Conclusions:

  • Nested case-control subgroup analysis provides unbiased exposure effect estimates.
  • This holds true regardless of the subgroup variable's role (risk factor, confounder, modifier).
  • Weighted Cox regression offers superior efficiency over conditional logistic regression.