Upstrapping to determine futility: predicting future outcomes nonparametrically from past data.

BACKGROUND: Clinical trials often involve some form of interim monitoring to determine futility before planned trial completion. While many options for interim monitoring exist (e.g., alpha-spending, conditional power), nonparametric based interim monitoring methods are also needed to account for more complex trial designs and analyses. The upstrap is one recently proposed nonparametric method that may be applied for interim monitoring. METHODS: Upstrapping is motivated by the case resampling bootstrap and involves repeatedly sampling with replacement from the interim data to simulate thousands of fully enrolled trials. The p-value is calculated for each upstrapped trial and the proportion of upstrapped trials for which the p-value criteria are met is compared with a pre-specified decision threshold. To evaluate the potential utility for upstrapping as a form of interim futility monitoring, we conducted a simulation study considering different sample sizes with several different proposed calibration strategies for the upstrap. We first compared trial rejection rates across a selection of threshold combinations to validate the upstrapping method. Then, we applied upstrapping methods to simulated clinical trial data, directly comparing their performance with more traditional alpha-spending and conditional power interim monitoring methods for futility. RESULTS: The method validation demonstrated that upstrapping is much more likely to find evidence of futility in the null scenario than the alternative across a variety of simulations settings. Our three proposed approaches for calibration of the upstrap had different strengths depending on the stopping rules used. Compared to O'Brien-Fleming group sequential methods, upstrapped approaches had type I error rates that differed by at most 1.7% and expected sample size was 2-22% lower in the null scenario, while in the alternative scenario power fluctuated between 15.7% lower and 0.2% higher and expected sample size was 0-15% lower. CONCLUSIONS: In this proof-of-concept simulation study, we evaluated the potential for upstrapping as a resampling-based method for futility monitoring in clinical trials. The trade-offs in expected sample size, power, and type I error rate control indicate that the upstrap can be calibrated to implement futility monitoring with varying degrees of aggressiveness and that performance similarities can be identified relative to considered alpha-spending and conditional power futility monitoring methods.

Beta spending function based on conditional power in group sequential design.

Conditional power (CP) serves as a widely utilized approach for futility monitoring in group sequential designs. However, adopting the CP methods may lead to inadequate control of the type II error rate at the desired level. In this study, we introduce a flexible beta spending function tailored to regulate the type II error rate while employing CP based on a predetermined standardized effect size for futility monitoring (a so-called CP-beta spending function). This function delineates the expenditure of type II error rate across the entirety of the trial. Unlike other existing beta spending functions, the CP-beta spending function seamlessly incorporates beta spending concept into the CP framework, facilitating precise stagewise control of the type II error rate during futility monitoring. In addition, the stopping boundaries derived from the CP-beta spending function can be calculated via integration akin to other traditional beta spending function methods. Furthermore, the proposed CP-beta spending function accommodates various thresholds on the CP-scale at different stages of the trial, ensuring its adaptability across different information time scenarios. These attributes render the CP-beta spending function competitive among other forms of beta spending functions, making it applicable to any trials in group sequential designs with straightforward implementation. Both simulation study and example from an acute ischemic stroke trial demonstrate that the proposed method accurately captures expected power, even when the initially determined sample size does not consider futility stopping, and exhibits a good performance in maintaining overall type I error rates for evident futility.

Portuguese Advance Directives-a twist against futility? A cross sectional study.

BACKGROUND: Advance Directive documents allow citizens to choose the treatments they want for end-of-life care without considering therapeutic futility. OBJECTIVES: To analyze patients' and caregivers' answers to Advance Directives and understand their expectations regarding their decisions. DESIGN AND SETTING: This study analyzed participants' answers to a previously published trial, conceived to test the document's efficacy as a communication tool. METHODS: Sixty palliative patients and 60 caregivers (n = 120) registered their preferences in the Advance Directive document and expressed their expectations regarding whether to receive the chosen treatments. RESULTS: In the patient and caregiver groups, 30% and 23.3% wanted to receive cardiorespiratory resuscitation; 23.3% and 25% wanted to receive artificial organ support; and 40% and 35% chose to receive artificial feeding and hydration, respectively. The participants ignored the concept of therapeutic futility and expected to receive invasive treatments. The concept of therapeutic futility should be addressed and discussed with both the patients and caregivers. Legal Advanced Directive documents should be made clear to reduce misinterpretations and potential legal conflicts. CONCLUSION: The authors suggest that all citizens should be clarified regarding the futility concept before filling out the Advance Directives and propose a grammatical change in the document, replacing the phrase "Health Care to Receive / Not to Receive" with the sentence "Health Care to Accept / Refuse" so that patients cannot demand treatments, but instead accept or refuse the proposed therapeutic plans. TRIAL REGISTRATION: ClinicalTrials.gov ID NCT05090072. URL: https://clinicaltrials.gov/ct2/show/NCT05090072.

Optimal futility stopping boundaries for binary endpoints.

BACKGROUND: Group sequential designs incorporating the option to stop for futility at the time point of an interim analysis can save time and resources. Thereby, the choice of the futility boundary importantly impacts the design's resulting performance characteristics, including the power and probability to correctly or wrongly stop for futility. Several authors contributed to the topic of selecting good futility boundaries. For binary endpoints, Simon's designs (Control Clin Trials 10:1-10, 1989) are commonly used two-stage designs for single-arm phase II studies incorporating futility stopping. However, Simon's optimal design frequently yields an undesirably high probability of falsely declaring futility after the first stage, and in Simon's minimax design often a high proportion of the planned sample size is already evaluated at the interim analysis leaving only limited benefit in case of an early stop. METHODS: This work focuses on the optimality criteria introduced by Schüler et al. (BMC Med Res Methodol 17:119, 2017) and extends their approach to binary endpoints in single-arm phase II studies. An algorithm for deriving optimized futility boundaries is introduced, and the performance of study designs implementing this concept of optimal futility boundaries is compared to the common Simon's minimax and optimal designs, as well as modified versions of these designs by Kim et al. (Oncotarget 10:4255-61, 2019). RESULTS: The introduced optimized futility boundaries aim to maximize the probability of correctly stopping for futility in case of small or opposite effects while also setting constraints on the time point of the interim analysis, the power loss, and the probability of stopping the study wrongly, i.e. stopping the study even though the treatment effect shows promise. Overall, the operating characteristics, such as maximum sample size and expected sample size, are comparable to those of the classical and modified Simon's designs and sometimes better. Unlike Simon's designs, which have binding stopping rules, the optimized futility boundaries proposed here are not adjusted to exhaust the full targeted nominal significance level and are thus still valid for non-binding applications. CONCLUSIONS: The choice of the futility boundary and the time point of the interim analysis have a major impact on the properties of the study design. Therefore, they should be thoroughly investigated at the planning stage. The introduced method of selecting optimal futility boundaries provides a more flexible alternative to Simon's designs with non-binding stopping rules. The probability of wrongly stopping for futility is minimized and the optimized futility boundaries don't exhibit the unfavorable properties of an undesirably high probability of falsely declaring futility or a high proportion of the planned sample evaluated at the interim time point.

Optimal timing for an accelerated interim futility analysis incorporating real world data.

BACKGROUND: Randomized controlled trials include interim monitoring guidelines to stop early for safety, efficacy, or futility. Futility monitoring facilitates re-allocation of limited resources. However, conventional methods for interim futility monitoring require a trial to accrue nearly half of the outcome data to make a reliable early stopping decision, limiting its benefit. As early stopping for futility will not inflate type-I error, these analyses are an appealing venue for incorporating external data to improve efficiency. METHODS: We propose a Bayesian approach to futility monitoring leveraging real world data using Semi-Supervised MIXture Multi-source Exchangeability Models, which accounts for both measured and unmeasured differences between data sources. We implement futility monitoring using predictive probabilities and investigate the optimal timing with respect to the expected sample size under the null hypothesis. Because we only incorporate external data during the interim futility analysis the proposed design is not limited by type-I error inflation. RESULTS: When the external and trial data are exchangeable, the proposed method provides a roughly 70 person reduction in expected sample size under the null. Under scenarios where exchangeability does not hold, our approach still provides a 10-20 person reduction in expected sample size under the null with about 80% power. CONCLUSIONS: External data borrowing in interim futility monitoring is a promising venue to improve trial efficiency without type-I error inflation. Approaches that are acceptable to regulatory authorities and leverage the complementary strengths of real world and trial data are vital to more efficiently allocate limited resources amongst clinical trials.

Systemic biomarker associated with poor outcome after futile reperfusion.

BACKGROUND: Successful recanalization does not lead to complete tissue reperfusion in a considerable percentage of ischemic stroke patients. This study aimed to identify biomarkers associated with futile recanalization. Leukoaraiosis predicts poor outcomes of this phenomenon. Soluble tumour necrosis factor-like weak inducer of apoptosis (sTWEAK), which is associated with leukoaraiosis degrees, could be a potential biomarker. METHODS: This study includes two cohorts of ischemic stroke patients in a multicentre retrospective observational study. Effective reperfusion, defined as a reduction of ≥8 points in the National Institutes of Health Stroke Scale (NIHSS) within the first 24 h, was used as a clinical marker of effective reperfusion. RESULTS: In the first cohort study, female sex, age, and high NIHSS at admission (44.7% vs. 81.1%, 71.3 ± 13.7 vs. 81.1 ± 6.7; 16 [13, 21] vs. 23 [17, 28] respectively; p < .0001) were confirmed as predictors of futile recanalization. ROC curve analysis showed that leukocyte levels (sensitivity of 99%, specificity of 55%) and sTWEAK level (sensitivity of 92%, specificity of 88%) can discriminate between poor and good outcomes. Both biomarkers simultaneously are higher associated with outcome after effective reperfusion (OR: 2.17; CI 95% 1.63-4.19; p < .0001) than individually (leukocytes OR: 1.38; CI 95% 1.00-1.64, p = .042; sTWEAK OR: 1.00; C I95% 1.00-1.01, p = .019). These results were validated using a second cohort, where leukocytes and sTWEAK showed a sensitivity of 100% and specificity of 66.7% and 75% respectively. CONCLUSIONS: Leukocyte and sTWEAK could be biomarkers of reperfusion failure and subsequent poor outcomes. Further studies will be necessary to explore its role in reperfusion processes.

[Time-limited trials (TLT) in the intensive care unit : Recommendations from the ethics section of the DIVI and the ethics section of the DGIIN]. / Zeitlich begrenzter Therapieversuch ("time-limited trial", TLT) auf der Intensivstation : Handlungsempfehlung der Sektion Ethik der DIVI und der Sektion Ethik der DGIIN.

The rise in intensive care treatment procedures is accompanied by an increase in the complexity of decisions regarding the selection, administration and duration of treatment measures. Whether a treatment goal is desirable in an individual case and the treatment plan required to achieve it is acceptable for the patient depends on the patient's preferences, values and life plans. There is often uncertainty as to whether a patient-centered treatment goal can be achieved. The use of a time-limited treatment trial (TLT) as a binding agreement between the intensive care unit (ICU) team and the patient or their legal representative on a treatment concept over a defined period of time in the ICU can be helpful to reduce uncertainties and to ensure the continuation of intensive care measures in the patients' best interest.

Design and monitoring of clinical trials with an interim analysis and a negative binomial endpoint.

There are very rich publications devoted to group sequential design, adaptive design and trial monitoring for continuous, binary and time to event endpoints. Many authors also discuss fixed design, blinded sample size re-estimation design and group sequential design for studies with a negative binomial outcome. Nonetheless, literature is sparse in adaptive design for a trial with a negative binomial endpoint. The features of such an endpoint in a flexible trial design setting remains inadequately understood. In this research, we seek to bridge this knowledge gap by offering a thorough examination of utilizing data components from a two-stage adaptive design for unblinded conditional power calculation and corresponding sample size re-estimation. We also provide expression for calculating the probability of meeting the futility criterion to determine the appropriate timing for the interim analysis. To evaluate the performance of the design, we conduct simulations to assess its operation characteristics. Finally, we provide a helpful and illustrative example to demonstrate the practical applications of the methods.

Futile treatment - when is enough, enough?

Objective This paper examines two aspects of treatment decision making: withdrawal of treatment decisions made by a patient; and decisions to not proceed with treatment by a health professional. The paper aims to provide an overview of the law relating to the provision of treatment, then highlight the uncertainty as to the meaning of and costs associated with futile treatment. Methods The paper reviews the current legal and medical literature on futile treatment. Results Continuing treatment which is futile is not in the patient's best interests. Futility may be understood in both quantitative and qualitative terms. Recent legal cases have expanded the definition of futility to focus not on the nature of the treatment itself, but also on the health of the patient to whom treatment is provided. Conclusions As Australia's population ages, there is likely to be an increased focus on the allocation of scarce health resources. This will, inevitably, place constraints on the number and variety of treatments offered to patients. The level of constraint will be felt acutely where a proposed treatment offers little clinical efficacy. It is time to try to understand and agree on a workable definition of futility.

Futility and poor outcomes are not the same thing: A clinical perspective of refined outcomes definitions in liver transplantation.

The term "futility" in liver transplantation is used inappropriately and inaccurately, as it is frequently applied to patient populations with suboptimal outcomes that are often not truly "futile." The term "futile" is used interchangeably with poor outcomes. Not all poor outcomes fulfill a definition of futility when considering all viewpoints. Definitions of "futility" are variable throughout the medical literature. We review futility in the context of liver transplantation, encompassing various viewpoints, with a goal to propose focused outcome definitions, including futility, that encompass broader viewpoints, and improve the utilization of "futility" to truly futile situations, and improve communication between providers and patients/families. Focused, appropriate definitions will help the transplant community develop better models to more accurately predict and avoid futile transplants, and better predict an individual patient's posttransplant outcome.

Seamless phase 2/3 design for trials with multiple co-primary endpoints using Bayesian predictive power.

Seamless phase 2/3 design has become increasingly popular in clinical trials with a single endpoint. Trials that define success based on the achievement of all co-primary endpoints (CPEs) encounter the challenge of inflated type 2 error rates, often leading to an overly large sample size. To tackle this challenge, we introduced a seamless phase 2/3 design strategy that employs Bayesian predictive power (BPP) for futility monitoring and sample size re-estimation at interim analysis. The correlations among multiple CPEs are incorporated using a Dirichlet-multinomial distribution. An alternative approach based on conditional power (CP) was also discussed for comparison. A seamless phase 2/3 vaccine trial employing four binary endpoints under the non-inferior hypothesis serves as an example. Our results spotlight that, in scenarios with relatively small phase 2 sample sizes (e.g., 50 or 100 subjects), the BPP approach either outperforms or matches the CP approach in terms of overall power. Particularly, with n1 = 50 and ρ = 0, BPP showcases an overall power advantage over CP by as much as 8.54%. Furthermore, when the phase 2 stage enrolled more subjects (e.g., 150 or 200), especially with a phase 2 sample size of 200 and ρ = 0, the BPP approach evidences a peak difference of 5.76% in early stop probability over the CP approach, emphasizing its better efficiency in terminating futile trials. It's noteworthy that both BPP and CP methodologies maintained type 1 error rates under 2.5%. In conclusion, the integration of the Dirichlet-Multinominal model with the BPP approach offers improvement in certain scenarios over the CP approach for seamless phase 2/3 trials with multiple CPEs.

Group sequential designs for cancer immunotherapy trial with delayed treatment effect.

Cancer immunotherapy trials are frequently characterized by delayed treatment effects such that the proportional hazards assumption is violated and the log-rank test suffers a substantial loss of statistical power. To increase the efficacy of the trial design, a variety of weighted log-rank tests have been proposed for fixed sample and group sequential trial designs. However, in such a group sequential design, it is often not recommended for futility interim monitoring due to possible delayed treatment effect which could result a high false-negative rate. To resolve this problem, we propose a group sequential design using a piecewise weighted log-rank test which provides an event-driven approach based on number of events after the delayed time. That is, the interim looks will not be conducted until the planned number of events observed after the delay time. Thus, it avoids the possibility of false-negative rate due to the delayed treatment effect. Furthermore, with an event-driven approach, the proposed group sequential design is robust against the underlying survival, accrual and censoring distributions. The group sequential designs using Fleming-Harrington-(ρ,γ) weighted log-rank test and a new weighted log-rank test are also discussed.

Bayesian hierarchical modeling in interim futility analysis for two parallel clinical trials.

Incorporating interim analysis into a trial design is gaining popularity in the field of confirmatory clinical trials, where two studies may be conducted in parallel (ie, twin studies) in order to provide substantial evidence per the requirement of FDA guidance. Interim futility analysis provides a chance to check for the "disaster" scenario when the treatment has a high probability to be not more efficacious than the control. Therefore, it is an efficient tool to mitigate risk of running a complete and expansive trial under such scenario. There is no agreement among trial designers that interim analysis should be based on individual study data or pooled data under the twin study scenario. In fact, it is a dilemma for most scientists when specifying the interim analysis strategy at the design stage as the true treatment effects of the twin studies are unknown no matter how similar they are intended to be. To address the issue, we developed a Bayesian hierarchical modeling method to allow dynamic data borrowing between twin studies and demonstrated a favorable characteristic of the new method over the separate and pooled analyses. We evaluated a wide spectrum of the heterogeneity hyperparameters and visualized its critical impact on the Bayesian model's characteristic. Based on the evaluation, we made a suggestion on the heterogeneity hyperparameter selection independent of any a priori knowledge. We also applied our method to a case study where predictive powers of different methods are compared.