A new comparison of nested case–control and case–cohort designs and methods

European Journal of Epidemiology

Volumn 30, Issue 3, 2014, Pages 197-207

  Kim, Ryung S ^a  

^a ALBERT EINSTEIN COLLEGE OF MEDICINE   (United States)

Author keywords

Case cohort; Inverse probability weighting; Nested case control; Simulation study

Indexed keywords

CASE CONTROL STUDY; COHORT ANALYSIS; COMPARATIVE STUDY; COMPUTER SIMULATION; HUMAN; METHODOLOGY; PROBABILITY; PROPORTIONAL HAZARDS MODEL; STATISTICAL MODEL;

CASE-CONTROL STUDIES; COHORT STUDIES; COMPUTER SIMULATION; HUMANS; MODELS, STATISTICAL; PROBABILITY; PROPORTIONAL HAZARDS MODELS; RESEARCH DESIGN;

EID: 84939988355     PISSN: 03932990     EISSN: 15737284     Source Type: Journal    
DOI: 10.1007/s10654-014-9974-4     Document Type: Article

References (36)

1. Thomas D. Addendum to ‘Methods of cohort analysis: appraisal by application to asbestos mining’ by Liddell FDK, McDonald JC, Thomas DC. J R Stat Soc. 1977;A140:469–91.
2. Prentice RL. A case–cohort design for epidemiologic cohort studies and disease prevention trials. Biometrika. 1986;73:1–11.
3. Self SG, Prentice RL. Asymptotic distribution theory and efficiency results for case–cohort studies. Ann Stat. 1988;16:64–81.
4. Langholz B, Thomas D. Nested case–control and case–cohort methods of sampling from a cohort: a critical comparison. Am J Epidemiol. 1990;131:169–76.
5. Barlow WE, Ichikawa L, Rosner D, Izumi S. Analysis of case–cohort designs. J Clin Epidemiol. 1999;52:1165–72.
6. Barlow WE. Robust variance estimation for the case–cohort design. Biometrics. 1994;50:1064–72.
7. Samuelsen S. A pseudo-likelihood approach to analysis of nested case–control studies. Biometrika. 1997;84:379–94.
8. Kim RS, Kaplan R. Analysis of secondary outcomes in nested case–control study designs. Stat Med. 2014;33:4215–26.
9. Kim RS. Analysis of nested case–control study designs: revisiting the inverse probability weighting method. Commun Stat Appl Methods. 2013;20:455–66.
10. Lin DY, Ying Z. Cox regression with incomplete covariate measurements. J Am Stat Assoc. 1993;88:1341–9.
11. Binder DA. Fitting Cox’s proportional hazards models from survey data. Biometrika. 1992;79:139–47.
12. Lin DY. On fitting Cox’s proportional hazards models to survey data. Biometrika. 2000;87:37–47.
13. Anderson PK, Gill RD. Cox’s regression model for counting processes: a large sample study. Ann Stat. 1982;10:1100–20.
14. Borgan O, Goldstein L, Langholz B. Methods for the analysis of sampled cohort data in the Cox proportional hazards model. Ann Stat. 1995;23:1749–78.
15. Therneau TM, Li H. Computing the Cox model for case cohort designs. Lifetime Data Anal. 1999;5:99–112.
16. Lin DY, Wei LJ. The robust inference for the Cox proportional hazards model. J Am Stat Assoc. 1989;84:1074–8.
17. R Development Core Team. R: a language and environment for statistical computing. Vienna: R Development Core Team; 2010.
18. R code: six case-cohort and two nested case-control methods. http://missionalconsulting.com/methods/rcode-cch-ncc
19. Zhang H, Goldstein L. Information and asymptotic efficiency of the case–cohort sampling design in Cox’s regression model. J Multivar Anal. 2003;85:292–317.
20. Goldstein L, Zhang H. Efficiency of the maximum partial likelihood estimator for nested case control sampling. Bernoulli. 2009;15:569–97.
21. Wacholder J. Practical considerations in choosing between the case–cohort and NCC designs. Epidemiology. 1991;2:155–8.
22. Chen KN. Generalized case–cohort sampling. J R Stat Soc Ser B (Stat Methodol). 2001;63:791–809.
23. Chen KN. Statistical estimation in the proportional hazards model with risk set sampling. Ann Stat. 2004;32:1513–32.
24. Chen HY. Double-semiparametric method for missing covariates in Cox regression models. J Am Stat Assoc. 2002;97:565–76.
25. Scheike TH, Juul A. Maximum likelihood estimation for Cox’s regression model under nested case–control sampling. Biostatistics. 2004;5:193–206.
26. Prentice RL, Williams BJ, Peterson AV. On the regression analysis of multivariate failure time data. Biometrika. 1981;68:373–9.
27. Lubin JH. Case–control methods in the presence of multiple failure times and competing risks. Biometrics. 1985;41:49–54.
28. Zhang H, Schaubel DE, Kalbfleisch JD. Proportional hazards regression for the analysis of clustered survival data from case–cohort studies. Biometrics. 2011;67:18–28.
29. Chen F, Chen K. Case–cohort analysis of clusters of recurrent events. Lifetime Data Anal. 2014;20:1–15.
30. Xue X, Xie X, Gunter M, Rohan TE, Wassertheil-Smoller S, Ho GY, et al. Testing the proportional hazards assumption in case–cohort analysis. BMC Med Res Methodol. 2013;13:1–10.
31. Bellera C, MacGrogan G, Debled M, de Lara C, Brouste V, Mathoulin-Pelissier S. Variables with time-varying effects and the cox model: some statistical concepts illustrated with a prognostic factor study in breast cancer. BMC Med Res Methodol. 2010;10:1–12.
32. Lu W, Liu M, Chen Y-H. Testing goodness-of-fit for the proportional hazards model based on nested case–control data. Biometrics. 2014;. doi:10.1111/biom.12239.
33. Ranganathan P, Pramesh CS. Censoring in survival analysis: potential for bias. Perspect Clin Res. 2012;3:40.
34. Meier EN. A sensitivity analysis for clinical trials with informatively censored survival endpoints. Master’s thesis, University of Washington; 2012.
35. Braekers R, Veraverbeke N. Cox’s regression model under partially informative censoring. Commun Stat Theory Methods. 2005;34:1793–811.
36. Lin DY, Robins JM, Wei LJ. Comparing two failure time distributions on the presence of dependent censoring. Biometrika. 1996;83:381–93.