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Continuous outcome estimation in N‐of‐1 trials for accelerated decision‐making.

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Title: Continuous outcome estimation in N‐of‐1 trials for accelerated decision‐making.
Authors: Defelippe, Victoria (AUTHOR), Bartoš, František (AUTHOR), Wagenmakers, Eric‐Jan (AUTHOR), Braun, Kees P. J. (AUTHOR), Jansen, Floor E. (AUTHOR), Otte, Willem M. (AUTHOR)
Source: Epilepsia (Series 4). May2026, Vol. 67 Issue 5, p2254-2269. 16p.
Subjects: Clinical decision making, Treatment effectiveness, Epilepsy, Crossover trials, Statistical hypothesis testing, Neurological disorders
Abstract: Objective: N‐of‐1 trials aim to determine the therapeutic effect for a single individual. This individualized approach necessitates collecting multiple data points over time through repeated alternating periods of active treatment and a comparator or control condition. The extended duration of the treatment periods may increase patient burden, prolong placebo exposure, and increase the likelihood of study discontinuation. In theory, treatment responders (or non‐responders) can be identified early during the trial if the therapeutic effect is strong (or completely lacking). There are no theoretical constraints to evaluate treatment efficacy more regularly—instead of only after a predetermined number of treatment periods. Regularly updating estimates on treatment effects allows clinicians to accelerate clinical decision‐making regarding N‐of‐1 study termination. This study examined the value of continuous treatment effect estimation using Bayesian hypothesis testing in N‐of‐1 trials to accelerate and nuance clinical decision‐making. Methods: An N‐of‐1 trial with severe epilepsy was simulated and three N‐of‐1 trials in neurological conditions were (re‐)analyzed continuously with consecutive data points using Bayesian hypothesis testing and/or a minimally clinically important threshold (30% seizure frequency reduction). Trial duration based on Bayesian testing with strong evidence for treatment effects was compared to original trial duration. Results: Original trial duration could be reduced between 9.5% and 35% of the trial length by using continuous outcome estimation in two of the analyzed trial examples. The moment that strong evidence supporting beneficial treatment effects using Bayesian hypothesis testing and a significant probability of minimally clinically important differences are achieved during the trial may differ. Obtaining additional data points and alternating interventions over time improve certainty of the estimates of treatment effects. Significance: Treatment efficacy decisions can be expedited when outcome estimation is performed continuously rather than delayed until the end of the trial. Clinical significance of N‐of‐1 trial outcome can be improved combining both Bayesian hypothesis testing and a minimally clinically important threshold. [ABSTRACT FROM AUTHOR]
Copyright of Epilepsia (Series 4) is the property of Wiley-Blackwell and its content may not be copied or emailed to multiple sites without the copyright holder's express written permission. Additionally, content may not be used with any artificial intelligence tools or machine learning technologies. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)
Database: Psychology and Behavioral Sciences Collection
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  Data: Continuous outcome estimation in N‐of‐1 trials for accelerated decision‐making.
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  Data: <searchLink fieldCode="AR" term="%22Defelippe%2C+Victoria%22">Defelippe, Victoria</searchLink> (AUTHOR)<br /><searchLink fieldCode="AR" term="%22Bartoš%2C+František%22">Bartoš, František</searchLink> (AUTHOR)<br /><searchLink fieldCode="AR" term="%22Wagenmakers%2C+Eric‐Jan%22">Wagenmakers, Eric‐Jan</searchLink> (AUTHOR)<br /><searchLink fieldCode="AR" term="%22Braun%2C+Kees+P%2E+J%2E%22">Braun, Kees P. J.</searchLink> (AUTHOR)<br /><searchLink fieldCode="AR" term="%22Jansen%2C+Floor+E%2E%22">Jansen, Floor E.</searchLink> (AUTHOR)<br /><searchLink fieldCode="AR" term="%22Otte%2C+Willem+M%2E%22">Otte, Willem M.</searchLink> (AUTHOR)
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  Data: <searchLink fieldCode="JN" term="%22Epilepsia+%28Series+4%29%22">Epilepsia (Series 4)</searchLink>. May2026, Vol. 67 Issue 5, p2254-2269. 16p.
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  Data: <searchLink fieldCode="DE" term="%22Clinical+decision+making%22">Clinical decision making</searchLink><br /><searchLink fieldCode="DE" term="%22Treatment+effectiveness%22">Treatment effectiveness</searchLink><br /><searchLink fieldCode="DE" term="%22Epilepsy%22">Epilepsy</searchLink><br /><searchLink fieldCode="DE" term="%22Crossover+trials%22">Crossover trials</searchLink><br /><searchLink fieldCode="DE" term="%22Statistical+hypothesis+testing%22">Statistical hypothesis testing</searchLink><br /><searchLink fieldCode="DE" term="%22Neurological+disorders%22">Neurological disorders</searchLink>
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  Label: Abstract
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  Data: Objective: N‐of‐1 trials aim to determine the therapeutic effect for a single individual. This individualized approach necessitates collecting multiple data points over time through repeated alternating periods of active treatment and a comparator or control condition. The extended duration of the treatment periods may increase patient burden, prolong placebo exposure, and increase the likelihood of study discontinuation. In theory, treatment responders (or non‐responders) can be identified early during the trial if the therapeutic effect is strong (or completely lacking). There are no theoretical constraints to evaluate treatment efficacy more regularly—instead of only after a predetermined number of treatment periods. Regularly updating estimates on treatment effects allows clinicians to accelerate clinical decision‐making regarding N‐of‐1 study termination. This study examined the value of continuous treatment effect estimation using Bayesian hypothesis testing in N‐of‐1 trials to accelerate and nuance clinical decision‐making. Methods: An N‐of‐1 trial with severe epilepsy was simulated and three N‐of‐1 trials in neurological conditions were (re‐)analyzed continuously with consecutive data points using Bayesian hypothesis testing and/or a minimally clinically important threshold (30% seizure frequency reduction). Trial duration based on Bayesian testing with strong evidence for treatment effects was compared to original trial duration. Results: Original trial duration could be reduced between 9.5% and 35% of the trial length by using continuous outcome estimation in two of the analyzed trial examples. The moment that strong evidence supporting beneficial treatment effects using Bayesian hypothesis testing and a significant probability of minimally clinically important differences are achieved during the trial may differ. Obtaining additional data points and alternating interventions over time improve certainty of the estimates of treatment effects. Significance: Treatment efficacy decisions can be expedited when outcome estimation is performed continuously rather than delayed until the end of the trial. Clinical significance of N‐of‐1 trial outcome can be improved combining both Bayesian hypothesis testing and a minimally clinically important threshold. [ABSTRACT FROM AUTHOR]
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  Data: <i>Copyright of Epilepsia (Series 4) is the property of Wiley-Blackwell and its content may not be copied or emailed to multiple sites without the copyright holder's express written permission. Additionally, content may not be used with any artificial intelligence tools or machine learning technologies. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract.</i> (Copyright applies to all Abstracts.)
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        Value: 10.1002/epi.70119
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        Text: English
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      – SubjectFull: Statistical hypothesis testing
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      – SubjectFull: Neurological disorders
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              M: 05
              Text: May2026
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              Y: 2026
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