Intention-to-Treat Analyses for Randomized Controlled Trials in Hospice/Palliative Care: The Case for Analyses to be of People Exposed to the Intervention
- Publication Type:
- Journal Article
- Journal of Pain and Symptom Management, 2020, 59 (3), pp. 637 - 645
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© 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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