Quantitative assessment of adverse events in clinical trials: Comparison of methods at an interim and the final analysis.
adverse events
clinical study
competing event
interim analysis
simulations
Journal
Biometrical journal. Biometrische Zeitschrift
ISSN: 1521-4036
Titre abrégé: Biom J
Pays: Germany
ID NLM: 7708048
Informations de publication
Date de publication:
05 2020
05 2020
Historique:
received:
09
08
2018
revised:
06
11
2019
accepted:
11
11
2019
pubmed:
23
11
2019
medline:
2
6
2021
entrez:
23
11
2019
Statut:
ppublish
Résumé
In clinical study reports (CSRs), adverse events (AEs) are commonly summarized using the incidence proportion (IP). IPs can be calculated for all types of AEs and are often interpreted as the probability that a treated patient experiences specific AEs. Exposure time can be taken into account with time-to-event methods. Using one minus Kaplan-Meier (1-KM) is known to overestimate the AE probability in the presence of competing events (CEs). The use of a nonparametric estimator of the cumulative incidence function (CIF) has therefore been advocated as more appropriate. In this paper, we compare different methods to estimate the probability of one selected AE. In particular, we investigate whether the proposed methods provide a reasonable estimate of the AE probability at an interim analysis (IA). The characteristics of the methods in the presence of a CE are illustrated using data from a breast cancer study and we quantify the potential bias in a simulation study. At the final analysis performed for the CSR, 1-KM systematically overestimates and in most cases IP slightly underestimates the given AE probability. CIF has the lowest bias in most simulation scenarios. All methods might lead to biased estimates at the IA except for AEs with early onset. The magnitude of the bias varies with the time-to-AE and/or CE occurrence, the selection of event-specific hazards and the amount of censoring. In general, reporting AE probabilities for prespecified fixed time points is recommended.
Identifiants
pubmed: 31756032
doi: 10.1002/bimj.201800234
doi:
Substances chimiques
Antineoplastic Agents
0
Types de publication
Comparative Study
Journal Article
Langues
eng
Sous-ensembles de citation
IM
Pagination
658-669Informations de copyright
© 2019 WILEY-VCH Verlag GmbH & Co. KGaA, Weinheim.
Références
Allignol, A., Beyersmann, J., & Schmoor, C. (2016). Statistical issues in the analysis of adverse events in time-to-event data. Pharmaceutical Statistics, 15, 297-305.
Baselga, J., Campone, M., Piccart, M., Burris III, H. A., Rugo, H. S., Sahmoud, T. … Hortobagyi, G. N. (2012). Everolimus in postmenopausal hormone-receptor-positive advanced breast cancer. New England Journal of Medicine, 366, 520-529.
Bender, R., Beckmann, L., & Lange, S. (2016). Biometrical issues in the analysis of adverse events within the benefit assessment of drugs. Pharmaceutical Statistics, 15, 292-296.
Beyersmann, J., Allignol, A., & Schumacher, M. (2012). Competing risks and multistate models with R. New York: Springer.
Beyersmann, J., Latouche, A., Buchholz, A., & Schumacher, M. (2009). Simulating competing risks data in survival analysis. Statistics in Medicine, 28, 956-971.
Burton, A., Altman, D., Royston, P., & Holder, R. (2006). The design of simulation studies in medical statistics. Statistics in Medicine, 25, 4279-4292.
Committee for Medicinal Products for Human Use (CHMP) (2005). Guideline on data monitoring committees. Retrieved from https://www.ema.europa.eu/en/data-monitoring-committees
Ellenberg, S. S., Fleming, T. R., & DeMets, D. L. (2003). Data Monitoring Committees in clinical trials. Chichester: Wiley.
Gooley, T. A., Leisenring, W., Crowley, J., & Stoerer, B. E. (1999). Estimation of failures probabilities in the presence of competing risk: New representation of old estimators. Statistics in Medicine, 18, 695-706.
Gray, B. (2014). cmprsk: Subdistribution Analysis of Competing Risks. R Package Version 2.2-7.Vienna, Austria: R Foundation for Statistical Computing.
Gray, R. J. (1988). A class of K-sample tests for comparing the cumulative incidence of a competing risk, Annals of Statistics, 16, 1141-1154.
ICH Expert Working Group (1994). ICH harmonised tripartite guideline: Clinical safety data management: Definitions and standards for expedited reporting E2A . Retrieved from https://www.ich.org/fileadmin/Public_Web_Site/ICH_Products/Guidelines/Efficacy/E2A/Step4/E2A_Guideline.pdf
ICH Expert Working Group (1995). ICH harmonised tripartite guideline: Structure and content of clinical study reports E3. Retrieved from http://www.ich.org/fileadmin/Public_Web_Site/ICH_Products/Guidelines/Efficacy/E3/E3_Guideline.pdf
Lavielle, M. (2018). Simulation of time-to-event data. In Simulx User Guide - Version 3.3. Retrieved from http://simulx.webpopix.org/userguide/
Moriña, D., & Navarro, A. (2017). Competing risk simulation with the survsim R package. Communications in Statistics - Simulation and Computation, 46, 5712-5722.
Proctor, T., & Schumacher, M. (2016). Modeling time-dependency in analysis of adverse events by the example of the CLEOPATRA study. Pharmaceutical Statistics, 15, 306-314.
Putter, H., Fiocco, M., & Geskus, R. B. (2007). Tutorial in biostatistics: Competing risks and multi-state models. Statistics in Medicine, 26, 2389-2430.
Rugo, H. S., Pritchard, K. I., Gnant, M., Noguchi, S., Piccart, M., Hortobagyi, G., … Burris III, H. A. (2014). Incidence and time course of everolimus-related adverse events in postmenopausal women with hormone receptor-positive advanced breast cancer: Insights from BOLERO-2. Annals of Oncology, 25, 808-815.
Russell, J. R., & Colevas, D. (2013). Adverse event monitoring in oncology clinical trials. Clinical Investigation, 3(12), 1157-1165.
Sartor, O. (2018). Adverse event reporting in clinical trials: Time to include duration as well as severity. Oncologist, 23, 1. https://doi.org/10.1634/theoncologist.2017-0437
Unkel, S., Amiri, M., Benda, N., Beyersmann, J., Knoerzer, D., Kupas, K., … Friede, T. (2018). On estimands and the analysis of adverse events in the presence of varying follow-up times within the benefit assessment of therapies. Pharmaceutical Statistics, 18, 166-183. https://doi.org/10.1002/pst.1915
U.S. Food and Drug Administration (FDA) (2006). Establishment and operation of clinical trial data monitoring committees. Retrieved from https://www.fda.gov/media/75398/download