Strategies for dealing with missing data in clinical trials: from design to analysis.
Randomized clinical trials are the gold standard for evaluating interventions as randomized assignment equalizes known and unknown characteristics between intervention groups. However, when participants miss visits, the ability to conduct an intent-to-treat analysis and draw conclusions about a causal link is compromised. As guidance to those performing clinical trials, this review is a non-technical overview of the consequences of missing data and a prescription for its treatment beyond the typical analytic approaches to the entire research process. Examples of bias from incorrect analysis with missing data and discussion of the advantages/disadvantages of analytic methods are given. As no single analysis is definitive when missing data occurs, strategies for its prevention throughout the course of a trial are presented. We aim to convey an appreciation for how missing data influences results and an understanding of the need for careful consideration of missing data during the design, planning, conduct, and analytic stages.
Department of Emergency Medicine, Yale School of Medicine, New Haven, Connecticut ; Yale Center for Analytical Sciences, New Haven, Connecticut.
James D
JD
Dziura
Lori A
LA
Post
Qing
Q
Zhao
Zhixuan
Z
Fu
Peter
P
Peduzzi
D015982:Bias; D002986:Clinical Trials as Topic; D006801:Humans; D012107:Research Design / Q000592:standards*
MAR; MCAR; MNAR; clinical trial; intent to treat; missing data; study design