Display options
Cite
Mason AJ, Grieve RD, Richards-Belle A, et al. A framework for extending trial design to facilitate missing data sensitivity analyses. BMC Med Res Methodol. 2020;20(1):66doi: 10.1186/s12874-020-00930-2.
Mason, A. J., Grieve, R. D., Richards-Belle, A., Mouncey, P. R., Harrison, D. A., & Carpenter, J. R. (2020). A framework for extending trial design to facilitate missing data sensitivity analyses. BMC medical research methodology, 20(1), 66. https://doi.org/10.1186/s12874-020-00930-2
Mason, Alexina J, et al. "A framework for extending trial design to facilitate missing data sensitivity analyses." BMC medical research methodology vol. 20,1 (2020): 66. doi: https://doi.org/10.1186/s12874-020-00930-2
Mason AJ, Grieve RD, Richards-Belle A, Mouncey PR, Harrison DA, Carpenter JR. A framework for extending trial design to facilitate missing data sensitivity analyses. BMC Med Res Methodol. 2020 Mar 17;20(1):66. doi: 10.1186/s12874-020-00930-2. PMID: 32183708; PMCID: PMC7076973.
Copy Download .nbib Format: NLM
Share it on

BMC Med Res Methodol. 2020 Mar 17;20(1):66. doi: 10.1186/s12874-020-00930-2.

A framework for extending trial design to facilitate missing data sensitivity analyses.

BMC medical research methodology

Alexina J Mason, Richard D Grieve, Alvin Richards-Belle, Paul R Mouncey, David A Harrison, James R Carpenter

Affiliations

  1. Department of Health Services Research and Policy, London School of Hygiene and Tropical Medicine, 15-17 Tavistock Place, London, WC1H 9SH, UK. [email protected].
  2. Department of Health Services Research and Policy, London School of Hygiene and Tropical Medicine, 15-17 Tavistock Place, London, WC1H 9SH, UK.
  3. Clinical Trials Unit, Intensive Care National Audit & Research Centre (ICNARC), London, UK.
  4. Department of Medical Statistics, London School of Hygiene and Tropical Medicine, Keppel Street, London, WC1E 7HT, UK.
  5. MRC Clinical Trials Unit at UCL, 90 High Holborn, London, WC1V 6LJ, UK.

PMID: 32183708 PMCID: PMC7076973 DOI: 10.1186/s12874-020-00930-2

Abstract

BACKGROUND: Missing data are an inevitable challenge in Randomised Controlled Trials (RCTs), particularly those with Patient Reported Outcome Measures. Methodological guidance suggests that to avoid incorrect conclusions, studies should undertake sensitivity analyses which recognise that data may be 'missing not at random' (MNAR). A recommended approach is to elicit expert opinion about the likely outcome differences for those with missing versus observed data. However, few published trials plan and undertake these elicitation exercises, and so lack the external information required for these sensitivity analyses. The aim of this paper is to provide a framework that anticipates and allows for MNAR data in the design and analysis of clinical trials.

METHODS: We developed a framework for performing and using expert elicitation to frame sensitivity analysis in RCTs with missing outcome data. The framework includes the following steps: first defining the scope of the elicitation exercise, second developing the elicitation tool, third eliciting expert opinion about the missing outcomes, fourth evaluating the elicitation results, and fifth analysing the trial data. We provide guidance on key practical challenges that arise when adopting this approach in trials: the criteria for identifying relevant experts, the outcome scale for presenting data to experts, the appropriate representation of expert opinion, and the evaluation of the elicitation results.The framework was developed within the POPPI trial, which investigated whether a preventive, complex psychological intervention, commenced early in ICU, would reduce the development of patient-reported post-traumatic stress disorder symptom severity, and improve health-related quality of life. We illustrate the key aspects of the proposed framework using the POPPI trial.

RESULTS: For the POPPI trial, 113 experts were identified with potentially suitable knowledge and asked to participate in the elicitation exercise. The 113 experts provided 59 usable elicitation questionnaires. The sensitivity analysis found that the results from the primary analysis were robust to alternative MNAR mechanisms.

CONCLUSIONS: Future studies can adopt this framework to embed expert elicitation within the design of clinical trials. This will provide the information required for MNAR sensitivity analyses that examine the robustness of the trial conclusions to alternative, but realistic assumptions about the missing data.

Keywords: Bayesian analysis; Clinical trials; Expert elicitation; Missing data; Pattern-mixture models; Sensitivity analysis

References

  1. Trials. 2019 Oct 22;20(Suppl 1):579 - PubMed
  2. J Intensive Care Soc. 2018 Nov;19(4):281-286 - PubMed
  3. Stat Med. 2018 Jul 10;37(15):2338-2353 - PubMed
  4. Health Econ. 2018 Nov;27(11):1670-1683 - PubMed
  5. Qual Life Res. 2011 Dec;20(10):1727-36 - PubMed
  6. N Engl J Med. 2012 Oct 4;367(14):1355-60 - PubMed
  7. J R Stat Soc Ser A Stat Soc. 2019 Feb;182(2):623-645 - PubMed
  8. Clin Trials. 2017 Aug;14(4):357-367 - PubMed
  9. BMJ. 2011 Feb 07;342:d40 - PubMed
  10. BMJ. 1999 Sep 11;319(7211):670-4 - PubMed
  11. BMC Med Res Methodol. 2017 Feb 6;17(1):21 - PubMed
  12. Clin Trials. 2017 Aug;14(4):368-369 - PubMed
  13. JAMA. 2019 Feb 19;321(7):665-675 - PubMed
  14. Open Med. 2009 May 12;3(2):e51-3 - PubMed
  15. BMJ Open. 2018 Feb 8;8(2):e020908 - PubMed

MeSH terms

Publication Types

Grant support