1. Missing not at random in end of life care studies
- Author
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Carreras G., Miccinesi G., Wilcock A., Preston N., Nieboer D., Deliens L., Groenvold M., Lunder U., van der Heide A., Baccini M., Korfage I. J., Rietjens J. A. C., Jabbarian L. J., Polinder S., van Delden H., Kars M., Zwakman M., Verkissen M. N., Eecloo K., Faes K., Pollock K., Seymour J., Caswell G., Bramley L., Payne S., Dunleavy L., Sowerby E., Bulli F., Ingravallo F., Toccafondi A., Gorini G., Cerv B., Simonic A., Mimic A., Kodba-Ceh H., Ozbic P., Arnfeldt C., Thit Johnsen A., Family Medicine and Chronic Care, End-of-life Care Research Group, Carreras G., Miccinesi G., Wilcock A., Preston N., Nieboer D., Deliens L., Groenvold M., Lunder U., van der Heide A., Baccini M., Korfage I.J., Rietjens J.A.C., Jabbarian L.J., Polinder S., van Delden H., Kars M., Zwakman M., Verkissen M.N., Eecloo K., Faes K., Pollock K., Seymour J., Caswell G., Bramley L., Payne S., Dunleavy L., Sowerby E., Bulli F., Ingravallo F., Toccafondi A., Gorini G., Cerv B., Simonic A., Mimic A., Kodba-Ceh H., Ozbic P., Arnfeldt C., Thit Johnsen A., and Public Health
- Subjects
Advance care planning ,Quality of life ,Epidemiology ,Missing data ,MODELS ,POWER ,Health Informatics ,Disease cluster ,01 natural sciences ,law.invention ,010104 statistics & probability ,03 medical and health sciences ,missing data ,0302 clinical medicine ,Quality of life (healthcare) ,LUNG-CANCER ,Randomized controlled trial ,SDG 3 - Good Health and Well-being ,law ,QUALITY-OF-LIFE ,Statistics ,Medicine and Health Sciences ,Humans ,030212 general & internal medicine ,Imputation (statistics) ,0101 mathematics ,advance care planning ,Quality Of Life ,Terminal Care ,lcsh:R5-920 ,Models, Statistical ,RANDOM FOREST ,MNAR ,3. Good health ,Random forest ,MICE ,MAR ,Action study ,Oncology ,Research Design ,oncology ,Psychology ,lcsh:Medicine (General) ,Research Article - Abstract
Background Missing data are common in end-of-life care studies, but there is still relatively little exploration of which is the best method to deal with them, and, in particular, if the missing at random (MAR) assumption is valid or missing not at random (MNAR) mechanisms should be assumed. In this paper we investigated this issue through a sensitivity analysis within the ACTION study, a multicenter cluster randomized controlled trial testing advance care planning in patients with advanced lung or colorectal cancer. Methods Multiple imputation procedures under MAR and MNAR assumptions were implemented. Possible violation of the MAR assumption was addressed with reference to variables measuring quality of life and symptoms. The MNAR model assumed that patients with worse health were more likely to have missing questionnaires, making a distinction between single missing items, which were assumed to satisfy the MAR assumption, and missing values due to completely missing questionnaire for which a MNAR mechanism was hypothesized. We explored the sensitivity to possible departures from MAR on gender differences between key indicators and on simple correlations. Results Up to 39% of follow-up data were missing. Results under MAR reflected that missingness was related to poorer health status. Correlations between variables, although very small, changed according to the imputation method, as well as the differences in scores by gender, indicating a certain sensitivity of the results to the violation of the MAR assumption. Conclusions The findings confirmed the importance of undertaking this kind of analysis in end-of-life care studies.
- Published
- 2021