Abstract
Adverse events (AEs) data compose the main body of safety data in clinical trials. Medically important imbalances of AEs in large double-blind randomized controlled trials (RCTs) are signals of potential adverse drug reactions. They will be further evaluated for causality and shape the initial label that gives users necessary information on the safe use of the drug. However, causality assessment in premarketing RCTs can be challenging. This article highlights key aspects that need attention and statistical analysis approaches that could be helpful for screening and evaluation of signals generated from imbalances of AEs in moderate or large RCTs.
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References
Council for International Organizations of Medical Sciences. Management of Safety Information from Clinical Trials. Geneva, Switzerland: Council for International Organizations of Medical Sciences; 2005.
Crowe BJ, Xia HA, Berlin JA, et al. Recommendations for safety planning, data collection, evaluation and reporting during drug, biologic and vaccine development: a report of the safety planning, evaluation, and reporting team. Clinical Trials. 2009;6(5):430–440.
Talbot J, Aronson JK. Stephens’ Detection and Evaluation of Adverse Drug Reactions Principles and Practices. 6th ed. New York, NY: John Wiley & Sons Ltd; 2012.
Jiang Q, Xia HA. Quantitative Evaluation of Safety in Drug Development: Design, Analysis and Reporting. New York, NY: Taylor & Francis; 2015.
Food and Drug Administration. Guidance for Industry and Investigators, Safety Reporting Requirements for INDs and BA/BE Studies. Silver Spring, MD: Food and Drug Administration; 2012.
Kilburn SA, Featherstone P, Higgins B, Brindle R. Interventions for cellulitis and erysipelas. Cochrane Database Syst Rev. 2010;6:CD004299.
Food and Drug Administration. Attachment B: Clinical Safety Review of an NDA or BLA of the Good Review Practice. Clinical Review Template (MAPP 6010.3 Rev. 1), Dec 15, 2010. Silver Spring, MD: Food and Drug Administration; 2010.
European Medicines Agency. A Guideline on Summary of Product Characteristics (SmPC), Revision 2, Sept. 2009. London, England: European Medicines Agency; 2009.
Zhou Y, Ke C, Jiang Q, Shahin S, Snapinn S. Choosing appropriate metrics to evaluate adverse events in safety evaluation. Therapeutic Innovation & Regulatory Science. In press.
Crowe BJ, Brueckner A, Beasley C, Kulkarni P. Current practices, challenges, and statistical issues with product safety labeling. Statistics in Biopharmaceutical Research. 2013;5(3):445–454.
O’Connell M, Knudsen S. Statistical graphics and reporting in drug development. Paper presented at: PhUSE 2006; October 9–11, 2006; Dublin, Ireland. Paper TS04.
Amit O, Heiberger RM, Lane PW. Graphical approaches to the analysis of safety data from clinical trials. Pharmaceutical Statistics. 2008;7(1):20–35.
Berry SM, Berry DA. Accounting for multiplicities in assessing drug safety: a three-level hierarchical mixture model. Biometrics. 2004;60:418–426.
Xia HA, Ma H, Carlin BP. Bayesian hierarchical modeling for detecting safety signals in clinical trials. J Biopharm Stat. 2011;21(5):1006–1029.
Mehrotra DV, Adewale AJ. Flagging clinical adverse experiences: reducing false discoveries without materially compromising power for detecting true signals. Stat Med. 2012;31:1918–1930.
Chang MN, Guess HA, Heyse JF. Reduction in burden of illness: a new measure in prevention trials. Stat Med. 1994;13:1807–1814.
Su L, Tucker R, Frey SE, et al. Measuring injection-site pain associated with vaccine administration in adults: a randomized, double-blind, placebo-controlled clinical trial. J Epidemiol Biostat. 2000;5:359–366.
Food and Drug Administration. Transcript for the Meeting of the Endocrinologic and Metabolic Drugs Advisory Committee for Dapagliflozin NDA, December 12, 2013. Silver Spring, MD: Food and Drug Administration; 2013.
Siddiqui O. Statistical methods to analyze adverse events data of randomized clinical trials. J Biopharm Stat. 2009;19(5):889–899.
Prentice RL, Williams BJ, Peterson AV. On the regression analysis of multivariate failure time data. Biometrika. 1981;68(2):373–379.
Rothman KJ. A potential bias in safety evaluation during open-label extensions of randomized clinical trials. Pharmacoepidemiol Drug Saf. 2004;13(5):295–298.
Rosenblum M, Jewell NP, van der Laan M, Shiboski S, van der Straten A, Padian N. Analysing direct effects in randomized trials with secondary interventions: an application to human immunodeficiency virus prevention trials. Journal of the Royal Statistical Series A. 2009;172(2):443–465.
Dai JY, Gilbert PB, Mâsse BR. Partially hidden Markov model for time-varying principal stratification in HIV prevention trials. J Am Stat Assoc., 2012;107(497):52–65.
Friedman LM, Furberg CD, DeMets DL. Fundamentals of Clinical Trials. 4th ed. New York, NY: Springer; 2010.
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Ma, H., Ke, C., Jiang, Q. et al. Statistical Considerations on the Evaluation of Imbalances of Adverse Events in Randomized Clinical Trials. Ther Innov Regul Sci 49, 957–965 (2015). https://doi.org/10.1177/2168479015587363
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DOI: https://doi.org/10.1177/2168479015587363