Recommendations for managing missing data, attrition and response shift in palliative and end-of-life care research: Part of the MORECare research method guidance on statistical issues. (December 2013) - Preston, Nancy J; Fayers, Peter; Walters, Stephen J; Pilling, Mark; Grande, Gunn E; Short, Vicky; Owen-Jones, Eleanor; Evans, Catherine J; Benalia, Hamid; Higginson, Irene J; Todd, Chris J; ,
Background: Statistical analysis in palliative and end-of-life care research can be problematic due to high levels of missing data, attrition and response shift as disease progresses. Aim: To develop recommendations about managing missing data, attrition and response shift in palliative and end-of-life care research data. Design: We used the MORECare Transparent Expert Consultation approach to conduct a consultation workshop with experts in statistical methods in palliative and end-of-life care research. Following presentations and discussion, nominal group techniques were used to produce recommendations about attrition, missing data and response shift. These were rated online by experts and analysed using descriptive statistics for consensus and importance. Results: In total, 20 participants attended the workshop and 19 recommendations were subsequently ranked. There was broad agreement across recommendations. The top five recommendations were as follows: A taxonomy should be devised to define types of attrition. Types and amount of missing data should be reported with details of imputation methods. The pattern of missing data should be investigated to inform the imputation approach. A statistical analysis plan should be pre-specified in the protocol. High rates of attrition should be assumed when planning studies and specifying analyses. The leading recommendation for response shift was for more research. Conclusions: When designing studies in palliative and end-of-life care, it is recommended that high rates of attrition should not be seen as indicative of poor design and that a clear statistical analysis plan is in place to account for missing data and attrition.