Reflection on modern methods: Planned missing data designs for epidemiological research

Charlie Rioux, Antoine Lewin, Omolola A. Odejimi, Todd D. Little

Research output: Contribution to journalArticlepeer-review

1 Scopus citations

Abstract

Taking advantage of the ability of modern missing data treatments in epidemiological research (e.g. multiple imputation) to recover power while avoiding bias in the presence of data that is missing completely at random, planned missing data designs allow researchers to deliberately incorporate missing data into a research design. A planned missing data design may be done by randomly assigning participants to have missing items in a questionnaire (multiform design) or missing occasions of measurement in a longitudinal study (wave-missing design), or by administering an expensive gold-standard measure to a random subset of participants while the whole sample is administered a cheaper measure (two-method design). Although not common in epidemiology, these designs have been recommended for decades by methodologists for their benefits - notably that data collection costs are minimized and participant burden is reduced, which can increase validity. This paper describes the multiform, wave-missing and two-method designs, including their benefits, their impact on bias and power, and other factors that must be taken into consideration when implementing them in an epidemiological study design.

Original languageEnglish
Pages (from-to)1702-1711
Number of pages10
JournalInternational Journal of Epidemiology
Volume49
Issue number5
DOIs
StatePublished - Oct 1 2020

Keywords

  • Bias
  • Data quality
  • Measurement
  • Methods
  • Missing data
  • Questionnaire design
  • Research design

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