Sensitivity to missing not at random dropout in clinical trials: Use and interpretation of the trimmed means estimator

Audinga-Dea Hazewinkel*, Jack Bowden, Kaitlin H Wade, Tom Palmer, Nicola J Wiles, Kate Tilling

*Corresponding author for this work

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Abstract

Outcome values in randomized controlled trials (RCTs) may be missing not at random (MNAR), if patients with extreme outcome values are more likely to drop out (eg, due to perceived ineffectiveness of treatment, or adverse effects). In such scenarios, estimates from complete case analysis (CCA) and multiple imputation (MI) will be biased. We investigate the use of the trimmed means (TM) estimator for the case of univariable missingness in one continuous outcome. The TM estimator operates by setting missing values to the most extreme value, and then "trimming" away equal fractions of both groups, estimating the treatment effect using the remaining data. The TM estimator relies on two assumptions, which we term the "strong MNAR" and "location shift" assumptions. We derive formulae for the TM estimator bias resulting from the violation of these assumptions for normally distributed outcomes. We propose an adjusted TM estimator, which relaxes the location shift assumption and detail how our bias formulae can be used to establish the direction of bias of CCA and TM estimates, to inform sensitivity analyses. The TM approach is illustrated in a sensitivity analysis of the CoBalT RCT of cognitive behavioral therapy (CBT) in 469 individuals with 46 months follow-up. Results were consistent with a beneficial CBT treatment effect, with MI estimates closer to the null and TM estimates further from the null than the CCA estimate. We propose using the TM estimator as a sensitivity analysis for data where extreme outcome value dropout is plausible.

Original languageEnglish
Pages (from-to)1462-1481
Number of pages20
JournalStatistics in Medicine
Volume41
Issue number8
Early online date31 Jan 2022
DOIs
Publication statusPublished - 15 Apr 2022

Bibliographical note

Funding Information:
The CoBalT trial was supported by the NIHR Biomedical Research Centre at University Hospitals Bristol and Weston NHS Foundation Trust and the University of Bristol. The views expressed are those of the author(s) and not necessarily those of the NIHR or the Department of Health and Social Care. Audinga‐Dea Hazewinkel, Tom Palmer, and Kate Tilling were supported by the Integrative Epidemiology Unit, which receives funding from the UK Medical Research Council and the University of Bristol (MC_UU_00011/3). Jack Bowden's research at the University of Exeter is funded by a UKRI Expanding Excellence in England (E3) award. Kaitlin H. Wade was supported by the Elizabeth Blackwell Institute for Health Research, University of Bristol and the Wellcome Trust Institutional Strategic Support Fund [204813/Z/16/Z] and is now affiliated to the Integrative Cancer Epidemiology Programme (ICEP), and works within the Medical Research Council Integrative Epidemiology Unit. Nicola J. Wiles: The CoBalT trial was funded by the National Institute for Health Research Health Technology Assessment (NIHR HTA) programme (project number 06/404/02).

Funding Information:
information Health Technology Assessment Programme, 06/404/02; Medical Research Council, MC_UU_0011/3; UK Research and Innovation, E3; Wellcome Trust, 204813/Z/16/ZThe CoBalT trial was supported by the NIHR Biomedical Research Centre at University Hospitals Bristol and Weston NHS Foundation Trust and the University of Bristol. The views expressed are those of the author(s) and not necessarily those of the NIHR or the Department of Health and Social Care. Audinga-Dea Hazewinkel, Tom Palmer, and Kate Tilling were supported by the Integrative Epidemiology Unit, which receives funding from the UK Medical Research Council and the University of Bristol (MC_UU_00011/3). Jack Bowden's research at the University of Exeter is funded by a UKRI Expanding Excellence in England (E3) award. Kaitlin H. Wade was supported by the Elizabeth Blackwell Institute for Health Research, University of Bristol and the Wellcome Trust Institutional Strategic Support Fund [204813/Z/16/Z] and is now affiliated to the Integrative Cancer Epidemiology Programme (ICEP), and works within the Medical Research Council Integrative Epidemiology Unit. Nicola J. Wiles: The CoBalT trial was funded by the National Institute for Health Research Health Technology Assessment (NIHR HTA) programme (project number 06/404/02).

Funding Information:
Health Technology Assessment Programme, 06/404/02; Medical Research Council, MC_UU_0011/3; UK Research and Innovation, E3; Wellcome Trust, 204813/Z/16/Z Funding information

Publisher Copyright:
© 2022 The Authors. Statistics in Medicine published by John Wiley & Sons Ltd.

Keywords

  • trimmed means
  • dropout
  • randomized controlled trials
  • missing not at random
  • sensitivity analyses
  • bias quantification

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