Strategies for dealing with missing data in clinical trials: from design to analysis

James D Dziura, Lori A Post, Qing Zhao, Zhixuan Fu, Peter Peduzzi, James D Dziura, Lori A Post, Qing Zhao, Zhixuan Fu, Peter Peduzzi

Abstract

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.

Keywords: MAR; MCAR; MNAR; clinical trial; intent to treat; missing data; study design.

Figures

Figure 1
Figure 1
A summary of the acceptable and unacceptable analytic methods for types of missing data. Green boxes show methods giving unbiased estimates of treatment effects and correct estimates of standard errors and p-values, yellow boxes show methods giving only unbiased estimates of treatment effects, red boxes show unacceptable methods. *Preferred method as it uses all available data.

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