Comparison of cohort and nested case-control designs for estimating the effect of time-varying drug exposure on the risk of adverse event in the presence of ties.
breast cancer
cohort design
nested case control design
pharmacoepidemiology
simulation study
tied events
time-varying exposure
Journal
Biometrical journal. Biometrische Zeitschrift
ISSN: 1521-4036
Titre abrégé: Biom J
Pays: Germany
ID NLM: 7708048
Informations de publication
Date de publication:
08 2023
08 2023
Historique:
revised:
12
08
2022
received:
30
11
2021
accepted:
20
10
2022
medline:
4
8
2023
pubmed:
28
2
2023
entrez:
27
2
2023
Statut:
ppublish
Résumé
Cohort and nested case-control (NCC) designs are frequently used in pharmacoepidemiology to assess the associations of drug exposure that can vary over time with the risk of an adverse event. Although it is typically expected that estimates from NCC analyses are similar to those from the full cohort analysis, with moderate loss of precision, only few studies have actually compared their respective performance for estimating the effects of time-varying exposures (TVE). We used simulations to compare the properties of the resulting estimators of these designs for both time-invariant exposure and TVE. We varied exposure prevalence, proportion of subjects experiencing the event, hazard ratio, and control-to-case ratio and considered matching on confounders. Using both designs, we also estimated the real-world associations of time-invariant ever use of menopausal hormone therapy (MHT) at baseline and updated, time-varying MHT use with breast cancer incidence. In all simulated scenarios, the cohort-based estimates had small relative bias and greater precision than the NCC design. NCC estimates displayed bias to the null that decreased with a greater number of controls per case. This bias markedly increased with higher proportion of events. Bias was seen with Breslow's and Efron's approximations for handling tied event times but was greatly reduced with the exact method or when NCC analyses were matched on confounders. When analyzing the MHT-breast cancer association, differences between the two designs were consistent with simulated data. Once ties were taken correctly into account, NCC estimates were very similar to those of the full cohort analysis.
Identifiants
pubmed: 36846937
doi: 10.1002/bimj.202100384
doi:
Types de publication
Journal Article
Research Support, Non-U.S. Gov't
Langues
eng
Sous-ensembles de citation
IM
Pagination
e2100384Informations de copyright
© 2023 The Authors. Biometrical Journal published by Wiley-VCH GmbH.
Références
Abrahamowicz, M., Beauchamp, M.-E., & Sylvestre, M.-P. (2012). Comparison of alternative models for linking drug exposure with adverse effects. Statistics in Medicine, 31(11-12), 1014-1030.
Allison, P. D. (2010). Survival analysis using SAS: A practical guide (2nd ed.). SAS Press.
Austin, P. C., Anderson, G. M., Cigsar, C., & Gruneir, A. (2012). Comparing the cohort design and the nested case-control design in the presence of both time-invariant and time-dependent treatment and competing risks: Bias and precision. Pharmacoepidemiology and Drug Safety, 21(7), 714-724. https://doi.org/10.1002/pds.3299
Bertke, S., Hein, M., Schubauer-Berigan, M., & Deddens, J. (2013). A simulation study of relative efficiency and bias in the nested case-control study design. Epidemiologic Methods, 2(1), 85-93.
Borucka, J. (2014). Methods of handling tied events in the Cox proportional hazard model. Studia Oeconomica Posnaniensia, 2(2), 263.
Breslow, N. (1974). Covariance analysis of censored survival data. Biometrics, 30(1), 89-99.
Breslow, N. E., Lubin, J. H., Marek, P., & Langholz, B. (1983). Multiplicative models and cohort analysis. Journal of the American Statistical Association, 78(381), 1-12.
Breslow, N. E., & Day, N. E. (1987). Statistical methods in cancer research. Volume II-The design and analysis of cohort studies. Lyon: International Agency for Research on Cancer (IARC Scientific Publications No. 82).
Brickner, C. P. (2015). Estimating the relationship between a transient effect and the onset of an acute event: a comparison of the case-crossover design and cohort design. Rutgers, The State University of New Jersey.
Cox, D. R. (1972). Regression models and life-tables. Journal of the Royal Statistical Society, Series B, 34(2), 187-202.
Cox, D. R. (1975). Partial likelihood. Biometrika, 62(2), 269-276.
Desai, R. J., Glynn, R. J., Wang, S., & Gagne, J. J. (2016). Performance of disease risk score matching in nested case-control studies: A simulation study. American Journal of Epidemiology, 183(10), 949-957.
Desquilbet, L., & Meyer, L. (2005). Variables dépendantes du temps dans le modèle de Cox Théorie et pratique. Revue d’Épidémiologie et de Santé Publique, 53(1), 51-68.
Efron, B. (1977). The efficiency of Cox's likelihood function for censored data. Journal of the American Statistical Association, 72(359), 557-565.
Essebag, V., Platt, R. W., Abrahamowicz, M., & Pilote, L. (2005). Comparison of nested case-control and survival analysis methodologies for analysis of time-dependent exposure. BMC Medical Research Methodology, 5(1), 5.
Etminan, M. (2004). Pharmacoepidemiology II: The nested case-control Study-A novel approach in pharmacoepidemiologic research. Pharmacotherapy, 24(9), 1105-1109.
Farewell, V. T., & Prentice, R. L. (1980). The approximation of partial likelihood with emphasis on case-control studies. Biometrika, 67(2), 273-278.
Fournier, A., Mesrine, S., Dossus, L., Boutron-Ruault, M.-C., Clavel-Chapelon, F., & Chabbert-Buffet, N. (2014). Risk of breast cancer after stopping menopausal hormone therapy in the E3N cohort. Breast Cancer Research and Treatment, 145(2), 535-543.
Goldstein, L., & Langholz, B. (1992). Asymptotic theory for nested case-control sampling in the Cox regression model. Annals of Statistics, 20(4), 1903-1928.
Hertz-Picciotto, I., & Rockhill, B. (1997). Validity and efficiency of approximation methods for tied survival times in Cox regression. Biometrics, 53(3), 1151-1156.
Klein, J. P., van Houwelingen, H. C., Ibrahim, J. G., & Scheike, T. H., Eds. (2014). Handbook of survival analysis. CRC Press, Taylor & Francis Group.
Langholz, B. (2014). Case-control study, nested. In N. Balakrishnan, T. Colton, B. Everitt, W. Piegorsch, F. Ruggeri, & J. L. Teugels Eds.), Wiley StatsRef: Statistics reference online. John Wiley & Sons. https://doi.org/10.1002/9781118445112.stat05121
Langholz, B., & Richardson, D. B. (2010). Fitting general relative risk models for survival time and matched case-control analysis. American Journal of Epidemiology, 171(3), 377-383.
Liddell, F. D. K., McDonald, J. C., Thomas, D. C., & Cunliffe, S. V. (1977). Methods of cohort analysis: Appraisal by application to asbestos mining. Journal of the Royal Statistical Society. Series A, 140(4), 469.
Lubin, J. H., & Gail, M. H. (1984). Biased selection of controls for case-control analyses of cohort studies. Biometrics, 40(1), 63-75.
Mackenzie, T., & Abrahamowicz, M. (2002). Marginal and hazard ratio specific random data generation: Applications to semi-parametric bootstrapping. Statistics and Computing, 12(3), 245-252.
O'Quigley, J. (2008). Proportional Hazards Regression. Springer.
Pang, D. (1999). A relative power table for nested matched case-control studies. Occupational and Environmental Medicine, 56(1), 67-69.
Pazzagli, L., Linder, M., Zhang, M., Vago, E., Stang, P., Myers, D., Andersen, M., & Bahmanyar, S. (2018). Methods for time-varying exposure related problems in pharmacoepidemiology: An overview. Pharmacoepidemiology and Drug Safety, 27(2), 148-160.
Peto, R. (1972). Discussion of: Regression models and life tables, by D.R. Cox. Journal of the Royal Statistical Society, Series B, 34(2), 205-207.
Prentice, R. L., & Breslow, N. E. (1978). Retrospective studies and failure time models. Biometrika, 65(1), 153-158.
Ryan, T. P., & Woodall, W. H. (2005). The most-cited statistical papers. Journal of Applied Statistics, 32(5), 461-474.
Sylvestre, M.-P., & Abrahamowicz, M. (2008). Comparison of algorithms to generate event times conditional on time-dependent covariates. Statistics in Medicine, 27(14), 2618-2634.
Sylvestre, M.-P., & Abrahamowicz, M. (2009). Flexible modeling of the cumulative effects of time-dependent exposures on the hazard. Statistics in Medicine, 28(27), 3437-3453.
Sylvestre, M.-P., Edens, T., MacKenzie, T., & Abrahamowicz, M. (2015). PermAlgo: Permutational Algorithm to Simulate Survival Data (1.1) [Computer software]. https://CRAN.R-project.org/package=PermAlgo