Framework for the treatment and reporting of missing data in observational studies: The tarmos framework

STRATOS initiative

Research output: Contribution to journalArticlepeer-review

Abstract

Missing data are ubiquitous in medical research. Although there is increasing guidance on how to handle missing data, practice is changing slowly and misapprehensions abound, particularly in observational research. We present a practical framework for handling and reporting the analysis of incomplete data in observational studies, which we illustrate using a case study from the Avon Longitudinal Study of Parents and Children. The framework consists of three steps: 1) Develop an analysis plan specifying the analysis model and how missing data are going to be addressed. An important consideration is whether a complete records analysis is likely to be valid, whether multiple imputation or an alternative approach is likely to offer benefits, and whether a sensitivity analysis regarding the missingness mechanism is required. 2) Explore the data, checking the methods outlined in the analysis plan are appropriate, and conduct the pre-planned analysis. 3) Report the results, including a description of the missing data, details on how the missing data were addressed, and the results from all analyses, interpreted in light of the missing data and the clinical relevance. This framework seeks to support researchers in thinking systematically about missing data, and transparently reporting the potential effect on the study results.

MSC Codes 6207

Original languageEnglish (US)
JournalUnknown Journal
StatePublished - Apr 29 2020

Keywords

  • ALSPAC
  • Bias
  • Efficiency
  • Incomplete data
  • Missing data
  • Multiple imputation
  • Observational studies
  • Reporting
  • STRATOS initiative

ASJC Scopus subject areas

  • General

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