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Determining a Bayesian predictive power stopping rule for futility in a non-inferiority trial with binary outcomes.

ABSTRACT: Background/Aims:Non-inferiority trials investigate whether a novel intervention, which typically has other benefits (i.e., cheaper or safer), has similar clinical effectiveness to currently available treatments. In situations where interim evidence in a non-inferiority trial suggests that the novel treatment is truly inferior, ethical concerns with continuing randomisation to the "inferior" intervention are raised. Thus, if interim data indicate that concluding non-inferiority at the end of the trial is unlikely, stopping for futility should be considered. To date, limited examples are available to guide the development of stopping rules for non-inferiority trials. Methods:We used a Bayesian predictive power approach to develop a stopping rule for futility for a trial collecting binary outcomes. We evaluated the frequentist operating characteristics of the stopping rule to ensure control of the Type I and Type II error. Our case study is the Intranasal Ketamine for Procedural Sedation trial (INK trial), a non-inferiority trial designed to assess the sedative properties of ketamine administered using two alternative routes. Results:We considered implementing our stopping rule after the INK trial enrols 140 patients out of 560. The trial would be stopped if 12 more patients experience a failure on the novel treatment compared to standard care. This trial has a type I error rate of 2.2% and a power of 80%. Conclusions:Stopping for futility in non-inferiority trials reduces exposure to ineffective treatments and preserves resources for alternative research questions. Futility stopping rules based on Bayesian predictive power are easy to implement and align with trial aims. Trial registration:ClinicalTrials.gov NCT02828566 July 11, 2016.

SUBMITTER: Heath A

PROVIDER: S-EPMC7153169 | biostudies-literature | 2020 Jun

REPOSITORIES: biostudies-literature

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Determining a Bayesian predictive power stopping rule for futility in a non-inferiority trial with binary outcomes.

Heath Anna A Offringa Martin M Pechlivanoglou Petros P Rios Juan David JD Klassen Terry P TP Poonai Naveen N Pullenayegum Eleanor E

Contemporary clinical trials communications 20200408

<h4>Background/aims</h4>Non-inferiority trials investigate whether a novel intervention, which typically has other benefits (i.e., cheaper or safer), has similar clinical effectiveness to currently available treatments. In situations where interim evidence in a non-inferiority trial suggests that the novel treatment is truly inferior, ethical concerns with continuing randomisation to the "inferior" intervention are raised. Thus, if interim data indicate that concluding non-inferiority at the end ...[more]

PMID: 32300671

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Project description:BackgroundIn clinical trials with fixed study designs, statistical inference is only made when the trial is completed. In contrast, group sequential designs allow an early stopping of the trial at interim, either for efficacy when the treatment effect is significant or for futility when the treatment effect seems too small to justify a continuation of the trial. Efficacy stopping boundaries based on alpha spending functions have been widely discussed in the statistical literature, and there is also solid work on the choice of adequate futility stopping boundaries. Still, futility boundaries are often chosen with little or completely without theoretical justification, in particular in investigator initiated trails. Some authors contributed to fill this gap. In here, we rely on an idea of Schüler et al. (2017) who discuss optimality criteria for futility boundaries for the special case of trials with (multiple) time-to-event endpoints. Their concept can be adopted to define "optimal" futility boundaries (with respect to given performance indicators) for continuous endpoints.MethodsWe extend Schülers' definition for "optimal" futility boundaries to the most common study situation of a single continuous primary endpoint compared between two groups. First, we introduce the analytic algorithm to derive these futility boundaries. Second, the new concept is applied to a real clinical trial example. Finally, the performance of a study design with an "optimal" futility boundary is compared to designs with arbitrarily chosen futility boundaries.ResultsThe presented concept of deriving futility boundaries allows to control the probability of wrongly stopping for futility, that means stopping for futility even if the treatment effect is promizing. At the same time, the loss in power is also controlled by this approach. Moreover, "optimal" futility boundaries improve the probability of correctly stopping for futility under the null hypothesis of no difference between two groups.ConclusionsThe choice of futility boundaries should be thoroughly investigated at the planning stage. The sometimes met, arbitrary choice of futility boundaries can lead to a substantial negative impact on performance. Applying futility boundaries with predefined optimization criteria increases efficiency of group sequential designs. Other optimization criteria than proposed in here might be incorporated.

Project description:Rationale: National guidelines have laid out a process to conflict resolution in cases of potentially inappropriate medical interventions. Objectives: To determine the association between information about a process-based approach and lay public perceptions of the appropriateness of withholding medically inappropriate interventions. Methods: Respondents from a nationwide sample completed a survey with two adult intensive care unit-based vignettes: one about advanced cancer where doctors told the family that additional chemotherapy would not be offered, and a second case of multiorgan failure after brain hemorrhage where dialysis would not be offered. Participants were randomly assigned to see or not see information about a detailed process for the determination to withhold (second opinion, ethics consultation, exploring transfer to another institution). The primary outcome was the perceived appropriateness of not providing the treatment (four-point scale, dichotomized for analysis, modified Poisson regression), and the secondary outcome was the negative emotional reaction to the case (positive and negative affect schedule, range 1-5, higher is greater negative emotional response, linear regression). Results: A total of 1,191 respondents were included. Providing detailed process information increased the perceived appropriateness of withholding treatment by approximately 10 percentage points in each vignette: (chemotherapy, 75.7-85.4%; dialysis, 68.0-79.3%). Process information remained associated with perceived appropriateness of withholding treatment after adjustment for order effects and prespecified respondent characteristics (chemotherapy: prevalence ratio, 1.13; 95% confidence interval [CI], 1.07-1.19) (dialysis: prevalence ratio, 1.17; 95% CI, 1.10-1.25). Process information was not associated with emotional response to the cases (chemotherapy: β = -0.04; 95% CI, -0.16 to 0.09) (dialysis: β = -0.02; 95% CI, -0.14 to 0.10; both adjusted for order effects). Conclusions: Providing process-based conflict resolution information increased public acceptance of determinations of medical futility, supporting the practice outlined in national consensus statements.

Dataset Information

Determining a Bayesian predictive power stopping rule for futility in a non-inferiority trial with binary outcomes.

Publications

Determining a Bayesian predictive power stopping rule for futility in a non-inferiority trial with binary outcomes.

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