Skip to main content
Key Specialties
Cardiology Diabetology Endocrinology Gastroenterology Geriatrics and Gerontology Gynecology and Obstetrics Hematology Infectious Disease Internal Medicine Nephrology Neurology Oncology Pediatrics Respiratory Medicine Rheumatology Surgery
Congress Coverage
2024 ACC Congress 2023 ESMO Congress 2023 EASD Congress 2023 ERS Congress 2023 ESC Congress
Clinical Collections
Advances in Alzheimer’s

Keynote webinar | Spotlight on medication adherence

LIVE: Thursday 27th June 2024, 18:00-19:30 (CEST)
Join our expert panel to discover why you need to understand the drivers of non-adherence in your patients, and how you can optimize medication adherence in your clinics to drastically improve patient outcomes.
ADVANCED SEARCH
Log in
Top
BMC Medical Research Methodology
Published in: BMC Medical Research Methodology 1/2021

Open Access 01-12-2021 | Care | Research article

Missing not at random in end of life care studies: multiple imputation and sensitivity analysis on data from the ACTION study

Authors: Giulia Carreras, Guido Miccinesi, Andrew Wilcock, Nancy Preston, Daan Nieboer, Luc Deliens, Mogensm Groenvold, Urska Lunder, Agnes van der Heide, Michela Baccini, ACTION consortium

Published in: BMC Medical Research Methodology | Issue 1/2021

Login to get access

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.
Appendix
Available only for authorised users
Literature
1.
Palmer JL. Analysis of missing data in palliative care studies. J Pain Symptom Manag. 2004;28:612–8.CrossRef
2.
Hussain JA, White IR, Langan D, Johnson MJ, Currow DC, Torgerson DJ, et al. Missing data in randomized controlled trials testing palliative interventions pose a significant risk of bias and loss of power: a systematic review and meta-analyses. J Clin Epidemiol. 2015;74:57–65.PubMedCrossRef
3.
Hussain JA, Bland M, Langan D, Johnson MJ, Currow DC, White IR. Quality of missing data reporting and handling in palliative care trials demonstrates that further development of the CONSORT missing data reporting guidance is required: a systematic review. J Clin Epidemiol. 2017;88:81–91.PubMedPubMedCentralCrossRef
4.
Rubin DB. Multiple imputation for nonresponse in surveys. New York: Wiley; 1987.CrossRef
5.
6.
7.
8.
Little RJA, Rubin DB. Statistical analysis with missing data. New Jersey: Wiley; 2002.CrossRef
9.
Raghunathan TE, Lepkowski JM, van Hoewyk J, Solenberger P. A multivariate technique for multiply imputing missing values using a sequence of regression models. Surv Methodol. 2001;27:85–95.
10.
Van Buuren S. Flexible imputation of missing data. Boca Raton: Chapman & Hall; 2018.CrossRef
11.
Jones M, Mishra GD, Dobson A. Analytical results in longitudinal studies depended on target of inference and assumed mechanism of attrition. J Clin Epidemiol. 2015;68:1165–75.PubMedCrossRef
12.
Hu B, Li L, Greene T. Joint multiple imputation for longitudinal outcomes and clinical events that truncate longitudinal follow-up. Stat Med. 2016;35:2991–3006.PubMedCrossRef
13.
Moreno-Betancur M, Chavance M. Sensitivity analysis of incomplete longitudinal data departing from the missing at random assumption: methodology and application in a clinical trial with drop-outs. Stat Methods Med Res. 2016;25:1471–89.PubMedCrossRef
14.
Preston NJ, Fayers P, Walters SJ, Pilling M, Grande GE, Short V, et al. 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. Palliat Med. 2013;27:899e907.CrossRef
15.
Post WJ, Buijs C, Stolk RP, de Vries EG, le Cessie S. The analysis of longitudinal quality of life measures with informative drop-out: a pattern mixture approach. Qual Life Res. 2010;19:137–48.PubMedCrossRef
16.
17.
Pauler DK, McCoy S, Moinpour C. Pattern mixture models for longitudinal quality of life studies in advanced stage disease. Stat Med. 2003;22:795–809.PubMedCrossRef
18.
Diehr P, Johnson LL. Accounting for missing data in end-of-life research. J Palliat Med. 2005;8(Suppl 1):S50–7.PubMedCrossRef
19.
Rietjens JA, Korfage IJ, Dunleavy L, Preston NJ, Jabbarian LJ, Christensen CA, et al. Advance care planning- a multi-centre cluster randomised clinical trial: the research protocol of the ACTION study. BMC Cancer. 2016;8(16):264.CrossRef
20.
Ratitch B, O'Kelly M, Tosiello R. Missing data in clinical trials: from clinical assumptions to statistical analysis using pattern mixture models. Pharm Stat. 2013;12:337–47.PubMedCrossRef
21.
Groenvold M, Petersen MA, Aaronson NK, Arraras JI, Blazeby JM, Bottomley A, Fayers PM, de Graeff A, Hammerlid E, Kaasa S, Sprangers MA, Bjorner JB, EORTC Quality of Life Group. The development of the EORTC QLQ-C15-PAL: a shortened questionnaire for cancer patients in palliative care. Eur J Cancer. 2006;42:55–64.PubMedCrossRef
22.
Fayers PM, Aaronson NK, Bjordal K, Groenvold M, Curran D, Bottomley A, on behalf of the EORTC Quality of Life Group. EORTC QLQ-C30 scoring manual. 3rd ed. Brussels: EORTC; 2001.
23.
24.
Brédart A, Bottomley A, Blazeby JM, Conroy T, Coens C, D'Haese S, et al. An international prospective study of the EORTC cancer in-patient satisfaction with care measure (EORTC IN-PATSAT32). Eur J Cancer. 2005;41:2120–31.PubMedCrossRef
25.
Carver CS, Scheier MF, Weintraub JK. Assessing coping strategies: a theoretically based approach. J Pers Soc Psychol. 1989;56:267–83.PubMedCrossRef
26.
Stanton AL, Kirk SB, Cameron CL, Danoff-Burg S. Coping through emotional approach: scale construction and validation. J Pers Soc Psychol. 2000;78:1150–69.PubMedCrossRef
27.
Greenland S, Senn SJ, Rothman KJ, Carlin JB, Poole C, Goodman SN, Altman DG. Statistical tests, P values, confidence intervals, and power: a guide to misinterpretations. Eur J Epidemiol. 2016;31:337–50.PubMedPubMedCentralCrossRef
28.
Li F, Baccini M, Mealli F, Zell ER, Frangakis CE, Rubin DB. Multiple imputation by ordered monotone blocks with application to the anthrax vaccine research program. J Comput Graphical Stat. 2014;23:877–92.CrossRef
29.
Gelman A, Rubin DB. Inference from iterative simulation using multiple sequences. Stat Sci. 1992;7:457–511.CrossRef
30.
White IR, Royston P, Wood AM. Multiple imputation using chained equations: issues and guidance for practice. Stat Med. 2011;30:377–99.PubMedCrossRef
31.
Van Buuren S, Groothuis-Oudshoorn K. MICE: Multivariate imputation by chained equations. R J Stat Softw. 2011;45:1–67.
32.
R Core Team. R: A language and environment for statistical computing. Vienna: R Foundation for Statistical Computing; 2013.
33.
Tompsett DM, Leacy F, Moreno-Betancur M, Heron J, White IR. On the use of the not-at-random fully conditional specification (NARFCS) procedure in practice. Stat Med. 2018;37:2338–53.PubMedPubMedCentralCrossRef
34.
Resseguier N, Giorgi R, Paoletti X. Sensitivity analysis when data are missing not-at-random. Epidemiology. 2011;22:282.PubMedCrossRef
35.
Leacy FP, Floyd S, Yates TA, White IR. Analyses of sensitivity to the missing-at-random assumption using multiple imputation with delta adjustment: application to a tuberculosis/HIV prevalence survey with incomplete HIV-status data. Am J Epidemiol. 2017;185:304–15.PubMedPubMedCentral
36.
37.
Little RJA. Selection and pattern-mixture models. In: Fitzmaurice G, Davidian M, Verbeke G, Molenberghs G, editors. Longitudinal data analysis, chapter 18. Boca Raton: CRC Press; 2009. p. 409–31.
38.
Van Buuren S, Boshuizen HC, Knook DL. Multiple imputation of missing blood pressure covariates in survival analysis. Stat Med. 1999;18:681–94.PubMedCrossRef
39.
Carpenter JR, Kenward MG. Multiple imputation and its application. 1st ed. Chichester: Wiley; 2013.CrossRef
40.
Burgette LF, Reiter JP. Multiple imputation for missing data via sequential regression trees. Am J Epidemiol. 2010;172:1070–6.PubMedCrossRef
41.
42.
Doove LL, Van Buuren S, Dusseldorp E. Recursive partitioning for missing data imputation in the presence of interaction effects. Comput Stat Data Anal. 2014;72:92–104.CrossRef
43.
Hong S, Lynn HS. Accuracy of random-forest-based imputation of missing data in the presence of non-normality, non-linearity, and interaction. BMC Med Res Methodol. 2020;20:199.PubMedPubMedCentralCrossRef
44.
Shah AD, Bartlett JW, Carpenter J, Nicholas O, Hemingway H. Comparison of random forest and parametric imputation models for imputing missing data using MICE: a CALIBER study. Am J Epidemiol. 2014;179:764–74.PubMedPubMedCentralCrossRef
45.
Lu K. Number of imputations needed to stabilize estimated treatment difference in longitudinal data analysis. Stat Methods Med Res. 2017;26:674–90.PubMedCrossRef
46.
Sullivan TR, White IR, Salter AB, Ryan P, Lee KJ. Should multiple imputation be the method of choice for handling missing data in randomized trials? Stat Methods Med Res. 2018:962280216683570.
47.
Speidel M, Drechsler J, Jolani S. R package hmi: a convenient tool for hierarchical multiple imputation and beyond. IAB-discussion paper. 2018. 16/2018.
48.
49.
50.
51.
Yang F, Small DS. Using post-outcome measurement information in censoring‐by‐death problems. J Royal Stat Soc Series B. 2016;78:299–318.CrossRef
52.
Tchetgen Tchetgen EJ. Identification and estimation of survivor average causal effects. Stat Med. 2014;33:3601–780.PubMedCrossRef
53.
Wen L, Terrera GM, Seaman SR. Methods for handling longitudinal outcome processes truncated by dropout and death. Biostatistics. 2018;19:407–25.PubMedCrossRef
54.
Metadata
Title
Missing not at random in end of life care studies: multiple imputation and sensitivity analysis on data from the ACTION study
Authors
Giulia Carreras
Guido Miccinesi
Andrew Wilcock
Nancy Preston
Daan Nieboer
Luc Deliens
Mogensm Groenvold
Urska Lunder
Agnes van der Heide
Michela Baccini
ACTION consortium
Publication date
01-12-2021
Publisher
BioMed Central
Keyword
Care
Published in
BMC Medical Research Methodology / Issue 1/2021
Electronic ISSN: 1471-2288
DOI
https://doi.org/10.1186/s12874-020-01180-y

Other articles of this Issue 1/2021

BMC Medical Research Methodology 1/2021 Go to the issue

Research

Are morbidity and mortality estimates from randomized controlled trials externally valid? A comparison of outcomes among infants enrolled into an RCT or a cohort study in Botswana

Technical advance

High performance implementation of the hierarchical likelihood for generalized linear mixed models: an application to estimate the potassium reference range in massive electronic health records datasets

Research

GoodReports: developing a website to help health researchers find and use reporting guidelines

Research article

The STS case study: an analysis method for longitudinal qualitative research for implementation science

Research

Random survival forests for dynamic predictions of a time-to-event outcome using a longitudinal biomarker

Research article

Inferencing superspreading potential using zero-truncated negative binomial model: exemplification with COVID-19