Analysis of Nested Case-Control Study Designs: Revisiting the Inverse Probability Weighting Method

Communications for Statistical Applications and Methods
Ryung S Kim

Abstract

In nested case-control studies, the most common way to make inference under a proportional hazards model is the conditional logistic approach of Thomas (1977). Inclusion probability methods are more efficient than the conditional logistic approach of Thomas; however, the epidemiology research community has not accepted the methods as a replacement of the Thomas' method. This paper promotes the inverse probability weighting method originally proposed by Samuelsen (1997) in combination with an approximate jackknife standard error that can be easily computed using existing software. Simulation studies demonstrate that this approach yields valid type 1 errors and greater powers than the conditional logistic approach in nested case-control designs across various sample sizes and magnitudes of the hazard ratios. A generalization of the method is also made to incorporate additional matching and the stratified Cox model. The proposed method is illustrated with data from a cohort of children with Wilm's tumor to study the association between histological signatures and relapses.

References

Jan 24, 1998·Journal of Clinical Oncology : Official Journal of the American Society of Clinical Oncology·D M GreenG J D'Angio

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Citations

Feb 9, 2018·Frontiers in Microbiology·Kevin D'hoeJeroen Raes
Nov 2, 2019·PLoS Neglected Tropical Diseases·Pilot DovihIan H Mendenhall
Jul 31, 2019·JMIR Research Protocols·Judith SchlaegerHolli A DeVon
Jul 19, 2018·Journal of Physics. Condensed Matter : an Institute of Physics Journal·Shuvojit PaulAyan Banerjee
Nov 20, 2020·Statistics in Medicine·Paramita Saha-Chaudhuri, Lamin Juwara

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