Performance status and trial site-level factors are associated with missing data in palliative care trials: An individual participant-level data analysis of 10 phase 3 trials

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Abstract

Background:

Missing data compromise the internal and external validity of trial findings, however there is limited evidence on how best to reduce missing data in palliative care trials.

Aim:

To assess the association between participant and site level factors and missing data in palliative care trials.

Design and setting:

Individual participant-level data analysis of 10 phase 3 palliative care trials using multi-level cross-classified models.

Results:

Participants with missing data at the previous time-point and poorer performance status were more likely to have missing data for the primary outcome and quality of life outcomes, at the primary follow-up point and end of follow-up. At the end of follow-up, the number of site randomisations and number of study site personnel were significantly associated with missing data. Trial duration and the number of research personnel explained most of the variance at the trial and site-level respectively, except for the primary outcome where the amount of data requested was most important at the trial-level. Variance at the trial level was more substantial than at the site level across models and considerable variance remained unexplained for all models except quality of life at the end of follow-up.

Conclusion:

Participants with a poorer performance status are at higher risk of missing data in palliative care trials and require additional support to provide complete data. Performance status is a potential auxiliary variable for missing data imputation models. Reducing trial variability should be prioritised and further factors need to be identified and explored to explain the residual variance.

Related collections

Most cited references 20

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Systematic reviews and meta-analyses of individual participant data (IPD) aim to collect, check, and reanalyze individual-level data from all studies addressing a particular research question and are therefore considered a gold standard approach to evidence synthesis. They are likely to be used with increasing frequency as current initiatives to share clinical trial data gain momentum and may be particularly important in reviewing controversial therapeutic areas. To develop PRISMA-IPD as a stand-alone extension to the PRISMA (Preferred Reporting Items for Systematic Reviews and Meta-Analyses) Statement, tailored to the specific requirements of reporting systematic reviews and meta-analyses of IPD. Although developed primarily for reviews of randomized trials, many items will apply in other contexts, including reviews of diagnosis and prognosis. Development of PRISMA-IPD followed the EQUATOR Network framework guidance and used the existing standard PRISMA Statement as a starting point to draft additional relevant material. A web-based survey informed discussion at an international workshop that included researchers, clinicians, methodologists experienced in conducting systematic reviews and meta-analyses of IPD, and journal editors. The statement was drafted and iterative refinements were made by the project, advisory, and development groups. The PRISMA-IPD Development Group reached agreement on the PRISMA-IPD checklist and flow diagram by consensus. Compared with standard PRISMA, the PRISMA-IPD checklist includes 3 new items that address (1) methods of checking the integrity of the IPD (such as pattern of randomization, data consistency, baseline imbalance, and missing data), (2) reporting any important issues that emerge, and (3) exploring variation (such as whether certain types of individual benefit more from the intervention than others). A further additional item was created by reorganization of standard PRISMA items relating to interpreting results. Wording was modified in 23 items to reflect the IPD approach. PRISMA-IPD provides guidelines for reporting systematic reviews and meta-analyses of IPD.

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Two classes of modern missing data procedures, maximum likelihood (ML) and multiple imputation (MI), tend to yield similar results when implemented in comparable ways. In either approach, it is possible to include auxiliary variables solely for the purpose of improving the missing data procedure. A simulation was presented to assess the potential costs and benefits of a restrictive strategy, which makes minimal use of auxiliary variables, versus an inclusive strategy, which makes liberal use of such variables. The simulation showed that the inclusive strategy is to be greatly preferred. With an inclusive strategy not only is there a reduced chance of inadvertently omitting an important cause of missingness, there is also the possibility of noticeable gains in terms of increased efficiency and reduced bias, with only minor costs. As implemented in currently available software, the ML approach tends to encourage the use of a restrictive strategy, whereas the MI approach makes it relatively simple to use an inclusive strategy.

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Author and article information

Journal

Journal ID (nlm-ta): Palliat Med

Journal ID (iso-abbrev): Palliat Med

Journal ID (publisher-id): PMJ

Journal ID (hwp): sppmj

Title: Palliative Medicine

Publisher: SAGE Publications (Sage UK: London, England )

ISSN (Print): 0269-2163

ISSN (Electronic): 1477-030X

Publication date (Electronic): 16 September 2021

Publication date (Print): December 2021

Volume: 35

Issue: 10

Pages: 1998-2007

Affiliations

[1 ]Wolfson Palliative Care Research Centre, Hull York Medical School, Hull, UK

[2 ]MRC Clinical Trials Unit at UCL, London, UK

[3 ]Wolfson Palliative Care Research Centre, University of Hull, Hull, UK

[4 ]Health Sciences Department, University of York, York, UK

[5 ]IMPACCT, Faculty of Health, University of Technology Sydney, Ultimo, NSW, Australia

Author notes

[*]Jamilla A Hussain, Wolfson Palliative Care Research Centre, Allam Medical Building, University of Hull, Cottingham Road, Hull HU6 7RX, UK. Email: hyjah@ 123456hyms.ac.uk

Author information

Jamilla A Hussain https://orcid.org/0000-0002-3644-6480

Miriam J Johnson https://orcid.org/0000-0001-6204-9158

David C Currow https://orcid.org/0000-0003-1988-1250

Article

Publisher ID: 10.1177_02692163211040970

DOI: 10.1177/02692163211040970

PMC ID: 8637362

PubMed ID: 34528466

SO-VID: 97923585-37aa-42a6-afdc-a06335aad195

License:

This article is distributed under the terms of the Creative Commons Attribution-NonCommercial 4.0 License ( https://creativecommons.org/licenses/by-nc/4.0/) which permits non-commercial use, reproduction and distribution of the work without further permission provided the original work is attributed as specified on the SAGE and Open Access pages ( https://us.sagepub.com/en-us/nam/open-access-at-sage).