Article Text

Download PDFPDF
Missing data in palliative care research: estimands and estimators
  1. Jessica Roydhouse1,2,
  2. Lysbeth Floden3,
  3. Sabine Braat4,
  4. Anneke Grobler5,
  5. Slavica Kochovska6,
  6. David C Currow6 and
  7. Melanie L Bell7,8
  1. 1University of Tasmania Menzies Institute for Medical Research, Hobart, Tasmania, Australia
  2. 2Department of Health Services, Policy and Practice, Brown University School of Public Health, Providence, Rhode Island, USA
  3. 3Clinical Outcomes Solutions, Chicago, Illinois, USA
  4. 4Centre for Epidemiology and Biostatistics, Melbourne School of Population and Global Health, University of Melbourne, Melbourne, Victoria, Australia
  5. 5Department of Paediatrics, Murdoch Children's Research Institute, Parkville, Victoria, Australia
  6. 6Faculty of Science, Medicine and Health, University of Wollongong, Wollongong, New South Wales, Australia
  7. 7Department of Epidemiology and Biostatistics, University of Arizona, Tucson, Arizona, USA
  8. 8The University of Sydney Psycho-Oncology Co-operative Research Group, Sydney, New South Wales, Australia
  1. Correspondence to Dr Jessica Roydhouse, University of Tasmania Menzies Institute for Medical Research, Hobart, TAS 7000, Australia; jessica.roydhouse{at}utas.edu.au

Abstract

There are several methodological challenges when conducting randomised controlled trials in palliative care. These include worsening function and high mortality, leading to treatment discontinuation, some of which will be unrelated to the intervention being evaluated.

Recently, a new framework for handling postrandomisation events, such as attrition, has been released. This framework aims to align trial objectives, design, conduct and analysis by clarifying what and how to estimate treatment effects in the presence of data affected by postrandomisation events.

The purpose of this paper is to introduce palliative care researchers to this framework and how it can guide trial design, and efficacy and safety analysis in a palliative care context where individual withdrawal from the trial is common.

In this paper, we describe the estimand framework and the background for it. We also consider postrandomisation events that are frequently encountered in palliative care trials and how these might affect objectives of interest. We then construct efficacy and safety estimands for a trial in palliative care. Better trial design and alignment of objectives with analysis can improve our understanding of what treatments do and do not work in palliative care.

Statistics from Altmetric.com

Request Permissions

If you wish to reuse any or all of this article please use the link below which will take you to the Copyright Clearance Center’s RightsLink service. You will be able to get a quick price and instant permission to reuse the content in many different ways.

Footnotes

  • Contributors Conception and design: JR, LF, MLB; interpretation of data: JR, LF, SB, AG, SK, DCC, MLB; drafting the work: JR; revising the work critically for important intellectual content: JR, LF, SB, AG, SK, DCC, MLB; final approval of the version to be published: JR, LF, SB, AG, SK, DCC, MLB.

  • Funding JR is supported by a Select Foundation Fellowship.

  • Competing interests JR: Personal fees from Amgen, outside the submitted work.

  • Provenance and peer review Not commissioned; internally peer reviewed.