Analysis of Nested Case-Control Study Designs: Revisiting the Inverse Probability Weighting Method Kim, Ryung S.;
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.
Nested Case-Control;Inverse Probability Weighting;Approximate Jackknife Standard Error;
Barlow, W. E. (1994). Robust variance estimation for the case-cohort design, Biometrics, 50, 1064-1072.
Beckwith, J. B. and Palmer, N. F. (1978). Histopathology and prognosis of Wilms tumor, Cancer, 41, 1937-1948.
Binder, D. A. (1992). Fitting Cox's proportional hazards models from survey data, Biometrika, 79, 139-147.
Borgan, O. and Langholz, B. (1993). Nonparametric estimation of relative mortality from nested case-control studies, Biometrics, 49.
Breslow, N. and Chatterjee, N. (1999). Design and analysis of two-phase studies with binary outcome applied to Wilms tumour prognosis, Applied Statistics, 48.
Chen, K. N. (2001). Generalized case-cohort sampling, Journal of the Royal Statistical Society: Series B (Statistical Methodology), 63.
Chen, K. N. (2004). Statistical estimation in the proportional hazards model with risk set sampling, Annals of Statistics, 32, 1513-1532.
Cox, D. R. (1972). Regression models and life tables, Journal of the Royal Statistical Society: Series B (Statistical Methodology), 34, 187-220.
D'angio, G. J., Breslow, N., Beckwith, B., Evans, A., Baum, E., Delorimier, A., Fernbach, D., Hrabovsky, E., Jones, B., Kelalis, P., Othersen, B., Tefft, M. and Thomas, P. R. M. (1989). Treatment of Wilms' tumor, Cancer, 64, 349-360.
Green, D. M., Breslow, N. E., Beckwith, J. B., Finklestein, J. Z., Grundy, P. G., Thomas, P. R. M., Kim, T., Shochat, S., Haase, G. M., Ritchey, M. L., Kelalis, P. P. and D'angio, G. J. (1998). Comparison between single-dose and divided-dose administration of dactinomycin and doxorubicin for patients with Wilms tumor: a report from the National Wilms Tumor Study Group, Journal of Clinical Oncology, 16, 237-245.
Kim, R. S. (2013). Analysis of Secondary Outcomes in Nested Case-Control Study Designs, Technical Reports. Division of Biostatistics, Albert Einstein College of Medicine.
Langholz, B. (2010). Case-control studies = Odds ratios: Blame the retrospective model, Epidemiology, 21, 10-12.
Lin, D. Y. (2000). On fitting Cox's proportional hazards models to survey data, Biometrika, 87, 37-47.
Lin, D. Y. andWei, L. J. (1989). The robust inference for the Cox proportional hazards model, Journal of the American Statistical Association, 84.
Reid, N. and Crepeau, H. (1985). Influence function for proportional hazards regression, Biometrika, 72, 1-9.
Samuelsen, S. (1997). A pseudo-likelihood approach to analysis of nested case-control studies, Biometrika, 84, 379-394.
Thomas, D. (1977). Addendum to 'Methods of cohort analysis: Appraisal by application to asbestos mining' by Liddell FDK, McDonald JC, Thomas DC, Journal of the Royal Statistical Society, A140, 469-491.