![]() We focus on missing outcome data here, though analysis methods have also been developed to handle missing covariates and auxiliary data. For example, measures of quality of life are usually not meaningful for patients who have died and hence would not be considered as missing data under this definition. Missing data are defined as values that are not available and that would be meaningful for analysis if they were observed. However, many of the recommendations are applicable to early-phase randomized trials and epidemiologic studies in general. The use of randomized study-group assignments predominates in such studies, since this design feature ensures comparability of study groups and allows assessment of causation. ![]() ![]() The report focused primarily on phase 3 confirmatory clinical trials for assessing the safety and efficacy of drugs, biologic products, and some medical devices, for which the bar of scientific rigor is set high. 8– 13 Since existing regulatory guidances 2– 4 lack specificity, in 2008 the Food and Drug Administration (FDA) requested that the NRC convene an expert panel to prepare “a report with recommendations that would be useful for FDA’s development of guidance for clinical trials on appropriate study designs and follow-up methods to reduce missing data and on appropriate statistical methods to address missing data for analysis of results.” This article summarizes some of the main findings and recommendations of the report 5 of that panel. 7 High rates of missing data that can affect conclusions occur in trials of treatments for many diseases. 1 For example, editorials in the Journal have noted how missing data have limited the ability to draw definitive conclusions from weight-loss trials 6 or could lead to incorrect inferences about drug safety. Missing data have seriously compromised inferences from clinical trials. The authors of this article served on the panel that prepared the report. A recent National Research Council (NRC) report 5 on the topic seeks to address this gap, and this article summarizes some of the main findings and recommendations of that report. 1 Existing regulatory guidances 2– 4 on the design, conduct, and analysis of clinical trials have little specific advice on how to address the problem of missing data. Missing data have seriously compromised inferences from clinical trials, yet the topic has received little attention in the clinical-trial community. ![]() Second and subsequent authors are listed alphabetically Departments of Biostatistics (R.J.L.) and Statistics (S.A.M.), University of Michigan, Ann Arbor the Department of Mathematics and Statistics, Boston University, Boston (R.D.) National Research Council of the National Academies, Washington, DC (M.L.C.) the Departments of Epidemiology (K.D.) and Biostatistics (C.F., D.S.), Johns Hopkins University, Baltimore the Department of Biostatistics, University of Washington, Seattle (S.S.E.) the Department of Biostatistics and Epidemiology, University of Pennsylvania School of Medicine, Philadelphia (J.T.F.) the Center for Statistical Sciences, Program in Public Health, Brown University, Providence, RI (J.W.H.) the International Institute for Biostatistics and Statistical Bioinformatics, Universiteit Hasselt and Katholieke Universiteit, Leuven, Belgium (G.M.) the School of Public Health, University of Minnesota, Minneapolis (J.D.N.) Departmento de Economia, Universidad Torcuato Di Tella, Buenos Aires (A.R.) the Department of Biostatistics, University of Medicine and Dentistry of New Jersey School of Public Health, New Brunswick (W.J.S.) Johnson & Johnson, Radnor, PA (J.P.S.) and the Department of Statistics, University of California, Irvine (H.S.).
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