The design and conduct of clinical trials to limit missing data

Stat Med. 2012 Dec 10;31(28):3433-43. doi: 10.1002/sim.5519. Epub 2012 Jul 25.


This article summarizes recommendations on the design and conduct of clinical trials of a National Research Council study on missing data in clinical trials. Key findings of the study are that (a) substantial missing data is a serious problem that undermines the scientific credibility of causal conclusions from clinical trials; (b) the assumption that analysis methods can compensate for substantial missing data is not justified; hence (c) clinical trial design, including the choice of key causal estimands, the target population, and the length of the study, should include limiting missing data as one of its goals; (d) missing-data procedures should be discussed explicitly in the clinical trial protocol; (e) clinical trial conduct should take steps to limit the extent of missing data; (f) there is no universal method for handling missing data in the analysis of clinical trials - methods should be justified on the plausibility of the underlying scientific assumptions; and (g) when alternative assumptions are plausible, sensitivity analysis should be conducted to assess robustness of findings to these alternatives. This article focuses on the panel's recommendations on the design and conduct of clinical trials to limit missing data. A companion paper addresses the panel's findings on analysis methods.

MeSH terms

  • Assisted Circulation / instrumentation
  • Assisted Circulation / methods
  • Bias
  • Chronic Pain / therapy
  • Data Collection / methods
  • Data Interpretation, Statistical*
  • Guidelines as Topic
  • HIV Infections / drug therapy
  • HIV Infections / virology
  • Humans
  • Informed Consent / standards
  • Motivation
  • Outcome Assessment, Health Care / methods
  • Outcome Assessment, Health Care / standards*
  • Outcome Assessment, Health Care / statistics & numerical data
  • Patient Dropouts / psychology
  • Patient Dropouts / statistics & numerical data
  • Randomized Controlled Trials as Topic / methods
  • Randomized Controlled Trials as Topic / standards*
  • Randomized Controlled Trials as Topic / statistics & numerical data
  • Research Design*
  • Research Personnel / education
  • Research Personnel / standards
  • Research Subjects