Framework for the treatment and reporting of missing data in observational studies: The Treatment And Reporting of Missing data in Observational Studies framework

TLDR

Missing data are pervasive in medical research, and despite growing guidance, slow adoption and unclear methodological choices threaten the validity and reproducibility of observational studies. The authors propose a practical framework for handling and reporting incomplete data in observational studies, demonstrated with a case study from the Avon Longitudinal Study of Parents and Children. The framework comprises three steps: (1) develop an analysis plan detailing the model and missing‑data strategy; (2) examine the data and perform the preplanned analysis; and (3) report results, including a description of missing data, the methods used, and interpretation. By encouraging systematic consideration and transparent reporting of missing data, the framework aims to enhance confidence in and reproducibility of research findings.

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. Importantly, the lack of transparency around methodological decisions is threatening the validity and reproducibility of modern 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) Examine the data, checking the methods outlined in the analysis plan are appropriate, and conduct the preplanned analysis; and 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, therefore increasing the confidence in and reproducibility of research findings.