Intention-to-Treat Analyses for Randomized Controlled Trials in Hospice/Palliative Care: The Case for Analyses to be of People Exposed to the Intervention

Kochovska, S; Huang, C; Johnson, MJ; Agar, MR; Fallon, MT; Kaasa, S; Hussain, JA; Portenoy, RK; Higginson, IJ; Currow, DC

Intention-to-Treat Analyses for Randomized Controlled Trials in Hospice/Palliative Care: The Case for Analyses to be of People Exposed to the Intervention

Kochovska, S

Huang, C Johnson, MJ Agar, MR

Fallon, MT Kaasa, S Hussain, JA Portenoy, RK Higginson, IJ Currow, DC

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Publication Type:: Journal Article
Citation:: Journal of Pain and Symptom Management, 2020, 59 (3), pp. 637 - 645
Issue Date:: 2020-03-01

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Field	Value	Language
dc.contributor.author	Kochovska, S https://orcid.org/0000-0002-3531-0389	en_US
dc.contributor.author	Huang, C	en_US
dc.contributor.author	Johnson, MJ	en_US
dc.contributor.author	Agar, MR https://orcid.org/0000-0002-6756-6119	en_US
dc.contributor.author	Fallon, MT	en_US
dc.contributor.author	Kaasa, S	en_US
dc.contributor.author	Hussain, JA	en_US
dc.contributor.author	Portenoy, RK	en_US
dc.contributor.author	Higginson, IJ	en_US
dc.contributor.author	Currow, DC https://orcid.org/0000-0003-1988-1250	en_US
dc.date.available	2019-10-25	en_US
dc.date.issued	2020-03-01	en_US
dc.identifier.citation	Journal of Pain and Symptom Management, 2020, 59 (3), pp. 637 - 645	en_US
dc.identifier.issn	0885-3924	en_US
dc.identifier.uri	http://hdl.handle.net/10453/138903
dc.description.abstract	© 2019 American Academy of Hospice and Palliative Medicine Context: Minimizing bias in randomized controlled trials (RCTs) includes intention-to-treat analyses. Hospice/palliative care RCTs are constrained by high attrition unpredictable when consenting, including withdrawals between randomization and first exposure to the intervention. Such withdrawals may systematically bias findings away from the new intervention being evaluated if they are considered nonresponders. Objectives: This study aimed to quantify the impact within intention-to-treat principles. Methods: A theoretical model was developed to assess the impact of withdrawals between randomization and first exposure on study power and effect sizes. Ten reported hospice/palliative care studies had power recalculated accounting for such withdrawal. Results: In the theoretical model, when 5% of withdrawals occurred between randomization and first exposure to the intervention, change in power was demonstrated in binary outcomes (2.0%–2.2%), continuous outcomes (0.8%–2.0%), and time-to-event outcomes (1.6%–2.0%), and odds ratios were changed by 0.06–0.17. Greater power loss was observed with larger effect sizes. Withdrawal rates were 0.9%–10% in the 10 reported RCTs, corresponding to power losses of 0.1%–2.2%. For studies with binary outcomes, withdrawal rates were 0.3%–1.2% changing odds ratios by 0.01–0.22. Conclusion: If blinding is maintained and all interventions are available simultaneously, our model suggests that excluding data from withdrawals between randomization and first exposure to the intervention minimizes one bias. This is the safety population as defined by the International Committee on Harmonization. When planning for future trials, minimizing the time between randomization and first exposure to the intervention will minimize the problem. Power should be calculated on people who receive the intervention.	en_US
dc.relation.ispartof	Journal of Pain and Symptom Management	en_US
dc.relation.isbasedon	10.1016/j.jpainsymman.2019.10.026	en_US
dc.rights	info:eu-repo/semantics/closedAccess
dc.subject.classification	Anesthesiology	en_US
dc.title	Intention-to-Treat Analyses for Randomized Controlled Trials in Hospice/Palliative Care: The Case for Analyses to be of People Exposed to the Intervention	en_US
dc.type	Journal Article
utslib.citation.volume	3	en_US
utslib.citation.volume	59	en_US
utslib.for	11 Medical and Health Sciences	en_US
pubs.embargo.period	Not known	en_US
pubs.organisational-group	/University of Technology Sydney
pubs.organisational-group	/University of Technology Sydney/Faculty of Health
pubs.organisational-group	/University of Technology Sydney/Faculty of Health/IMPACCT
utslib.copyright.status	closed_access	*
pubs.issue	3	en_US
pubs.publication-status	Published	en_US
pubs.volume	59	en_US

Abstract:

© 2019 American Academy of Hospice and Palliative Medicine Context: Minimizing bias in randomized controlled trials (RCTs) includes intention-to-treat analyses. Hospice/palliative care RCTs are constrained by high attrition unpredictable when consenting, including withdrawals between randomization and first exposure to the intervention. Such withdrawals may systematically bias findings away from the new intervention being evaluated if they are considered nonresponders. Objectives: This study aimed to quantify the impact within intention-to-treat principles. Methods: A theoretical model was developed to assess the impact of withdrawals between randomization and first exposure on study power and effect sizes. Ten reported hospice/palliative care studies had power recalculated accounting for such withdrawal. Results: In the theoretical model, when 5% of withdrawals occurred between randomization and first exposure to the intervention, change in power was demonstrated in binary outcomes (2.0%–2.2%), continuous outcomes (0.8%–2.0%), and time-to-event outcomes (1.6%–2.0%), and odds ratios were changed by 0.06–0.17. Greater power loss was observed with larger effect sizes. Withdrawal rates were 0.9%–10% in the 10 reported RCTs, corresponding to power losses of 0.1%–2.2%. For studies with binary outcomes, withdrawal rates were 0.3%–1.2% changing odds ratios by 0.01–0.22. Conclusion: If blinding is maintained and all interventions are available simultaneously, our model suggests that excluding data from withdrawals between randomization and first exposure to the intervention minimizes one bias. This is the safety population as defined by the International Committee on Harmonization. When planning for future trials, minimizing the time between randomization and first exposure to the intervention will minimize the problem. Power should be calculated on people who receive the intervention.

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http://hdl.handle.net/10453/138903