An interpretable model or method has several appealing features, such as reliability to adversarial examples, transparency of decision-making, and communication facilitator. However, interpretability is a subjective concept, and even its definition can be diverse. The same model may be deemed as interpretable by a study team, but regarded as a black-box algorithm by another squad. Simplicity, accuracy and generalizability are some additional important aspects of evaluating interpretability. In this work, we present a general, flexible and harmonious framework to construct interpretable functions in regression analysis with a focus on continuous outcomes. We formulate a functional skeleton in light of users' expectations of interpretability. A new measure based on Mallows's $C_p$-statistic is proposed for model selection to balance approximation, generalizability, and interpretability. We apply this approach to derive a sample size formula in adaptive clinical trial designs to demonstrate the general workflow, and to explain operating characteristics in a Bayesian Go/No-Go paradigm to show the potential advantages of using meaningful intermediate variables. Generalization to categorical outcomes is illustrated in an example of hypothesis testing based on Fisher's exact test. A real data analysis of NHANES (National Health and Nutrition Examination Survey) is conducted to investigate relationships between some important laboratory measurements. We also discuss some extensions of this method.

Sample size calculation and power estimate are an integral part of clinical trials. With accelerated development to address the unmet medical needs, the fast-paced development may lead to uncertainties in initial planning and assumptions of clinical trials. Promising zone design presents sponsors an opportunity to re-estimate the sample size based on the interim data to mitigate risks, reduce uncertainties, and increase probability of trial success.

This paper aims to use the GRAPHITE trial (NCT03657160) as a real data application to showcase the practical considerations in implementation of promising zone design for interim sample size re-estimation (SSR), in light of sample size adaptation rules, maximum sample size allowed, multiplicity adjustment, and sponsor access to interim results. GRAPHITE is a phase 3 trial with vedolizumab for prophylaxis of acute graft vs host disease (aGvHD) after allogeneic hematopoietic stem cell transplant (allo-HSCT). The primary efficacy endpoint is lower intestinal aGVHD-free survival by Day +180 after allo-HSCT. A simulation study was conducted to demonstrate the evaluation of operating characteristics by various true underlying treatment effects at the design stage.

The application of promising zone design for interim SSR is novel and has successfully helped the sponsor achieve the balance between minimizing the risks and maintaining scientific integrity. This work aims to highlight the necessity of empirical guidance to gain better insights for clinical researchers in practice and is expected to facilitate the understanding and implementation of promising zone design for interim SSR in phase 3 trials.

Intravenous lipids are critical to the care of extremely premature and other high-risk infants.

This study evaluated safety and efficacy of parenteral nutrition (PN) with composite intravenous lipid emulsion (CO-ILE) with fish oil compared with pure soybean oil lipid emulsion (SOLE).

Randomized, controlled, double-blind, multicenter study (NCT02579265) in neonates/infants anticipated to require ≥28 d of PN due to gastrointestinal malformations or injury. Duration of the initial and extended treatment phase was 28 d and 84 d, respectively (for patients with PN indication after day 28).

Eighty-three patients (mean postnatal age 11.4 d, 54 preterm) received CO-ILE and 78 patients received SOLE (mean postnatal age 8.3 d, 59 preterm). Thirty-three patients per group completed 28 d of treatment. Risk of having conjugated bilirubin values >2 mg/dL confirmed by a second sample 7 d after the first during the initial treatment phase (primary outcome) was 2.4% (2 of 83) with CO-ILE and 3.8% (3 of 78) with SOLE (risk ratio: 0.59; 95% confidence interval [CI]: 0.09, 3.76). Between days 29 and 84, the number of patients with confirmed conjugated bilirubin values >2 mg/dL did not increase in the CO-ILE group (n = 2) and increased in the SOLE group (n = 9). At the end of the initial treatment phase, conjugated bilirubin concentrations were 45.6% lower under CO-ILE than under SOLE (P = 0.006). There was no clinical or laboratory evidence of essential fatty acid deficiency in patients in the CO-ILE group. Median time to discharge alive was 56.7 d and 66.4 d with CO-ILE and SOLE, respectively (hazard ratio: 1.16; 95% CI: 0.81, 1.68).

CO-ILE was associated with a possible lower risk of cholestasis and significantly lower conjugated bilirubin concentration at the end of the initial treatment phase in high-risk neonates and infants as compared with patients treated with SOLE. In summary, these data indicate that CO-ILE can be considered safe and may be preferable over SOLE in high-risk neonates. This trial was registered at clinicaltrials.gov as NCT02579265.

The two-trials rule for drug approval requires "at least two adequate and well-controlled studies, each convincing on its own, to establish effectiveness." This is usually implemented by requiring two significant pivotal trials and is the standard regulatory requirement to provide evidence for a new drug's efficacy. However, there is need to develop suitable alternatives to this rule for a number of reasons, including the possible availability of data from more than two trials. I consider the case of up to three studies and stress the importance to control the partial Type-I error rate, where only some studies have a true null effect, while maintaining the overall Type-I error rate of the two-trials rule, where all studies have a null effect. Some less-knownP $$ P $$ -value combination methods are useful to achieve this: Pearson's method, Edgington's method and the recently proposed harmonic meanχ 2 $$ {\chi}^2 $$ -test. I study their properties and discuss how they can be extended to a sequential assessment of success while still ensuring overall Type-I error control. I compare the different methods in terms of partial Type-I error rate, project power and the expected number of studies required. Edgington's method is eventually recommended as it is easy to implement and communicate, has only moderate partial Type-I error rate inflation but substantially increased project power.

Inferentially seamless 2/3 designs are increasingly popular in clinical trials. It is important to understand their relative advantages compared with separate phase 2 and phase 3 trials, and to understand the consequences of design choices such as the proportion of patients included in the phase 2 portion of the design. Extending previous work in this area, we perform a simulation study across multiple numbers of arms and efficacy response curves. We consider a design space crossing the choice of a separate versus seamless design with the choice of allocating 0%-100% of available patients in phase 2, with the remainder in phase 3. The seamless designs achieve greater power than their separate trial counterparts. Importantly, the optimal seamless design is more robust than the optimal separate program, meaning that one range of values for the proportion of patients used in phase 2 (30%-50% of the total phase 2/3 sample size) is nearly optimal for a wide range of response scenarios. In contrast, a percentage of patients used in phase 2 for separate trials may be optimal for some alternative scenarios but decidedly inferior for other alternative scenarios. When operationally and scientifically viable, seamless trials provide superior performance compared with separate phase 2 and phase 3 trials. The results also provide guidance for the implementation of these trials in practice.

The 2-in-1 design is becoming popular in oncology drug development, with the flexibility in using different endpoints at different decision time. Based on the observed interim data, sponsors can choose to seamlessly advance a small phase 2 trial to a full-scale confirmatory phase 3 trial with a pre-determined maximum sample size or remain in a phase 2 trial. While this approach may increase efficiency in drug development, it is rigid and requires a pre-specified fixed sample size. In this paper, we propose a flexible 2-in-1 design with sample size adaptation, while retaining the advantage of allowing an intermediate endpoint for interim decision-making. The proposed design reflects the needs of the recent FDA's Project FrontRunner initiative, which encourages the use of an earlier surrogate endpoint to potentially support accelerated approval with conversion to standard approval with long-term endpoints from the same randomized study. Additionally, we identify the interim decision cut-off to allow a conventional test procedure at the final analysis. Extensive simulation studies showed that the proposed design requires much a smaller sample size and shorter timeline than the simple 2-in-1 design, while achieving similar power. We present a case study in multiple myeloma to demonstrate the benefits of the proposed design.

Seamless study designs have the potential to accelerate clinical development. The use of innovative seamless designs has been increasing in the oncology area; however, while the concept of seamless designs becomes more popular and accepted, many challenges remain in both the design and conduct of these trials. This may be especially true when seamless designs are used in late phase development supporting regulatory decision-making. The Innovative Design Scientific Working Group (IDSWG) Oncology team conducted a survey to understand the current use of seamless study designs for registration purposes in oncology clinical development. The survey was designed to provide insights into the benefits and to identify the roadblocks. A total of 16 questions were included in the survey that was distributed using the ASA Biopharmaceutical Section and IDSWG email listings from August to September 2022. A total of 51 responses were received, with 39 (76%) respondents indicating that their organizations had seamless oncology studies in planning or implementation for registration purposes. Detailed survey results are presented in the manuscript. Overall, while seamless designs offer advantages in terms of timeline reduction and cost saving, they also present challenges related to additional complexity and the need for efficient surrogate clinical endpoints in oncology drug development.

There is growing interest in platform trials that allow for adding of new treatment arms as the trial progresses as well as being able to stop treatments part way through the trial for either lack of benefit/futility or for superiority. In some situations, platform trials need to guarantee that error rates are controlled. This paper presents a multi-stage design, that allows additional arms to be added in a platform trial in a preplanned fashion, while still controlling the family-wise error rate, under the assumption of known number and timing of treatments to be added, and no time trends. A method is given to compute the sample size required to achieve a desired level of power and we show how the distribution of the sample size and the expected sample size can be found. We focus on power under the least favorable configuration which is the power of finding the treatment with a clinically relevant effect out of a set of treatments while the rest have an uninteresting treatment effect. A motivating trial is presented which focuses on two settings, with the first being a set number of stages per active treatment arm and the second being a set total number of stages, with treatments that are added later getting fewer stages. Compared to Bonferroni, the savings in the total maximum sample size are modest in a trial with three arms, <1% of the total sample size. However, the savings are more substantial in trials with more arms.

Adaptive randomized clinical trials are of major interest when dealing with a time-to-event outcome in a prolonged observation window. No consensus exists either to define stopping boundaries or to combinep $$ p $$ values or test statistics in the terminal analysis in the case of a frequentist design and sample size adaptation. In a one-sided setting, we compared three frequentist approaches using stopping boundaries relying onα $$ \alpha $$ -spending functions and a Bayesian monitoring setting with boundaries based on the posterior distribution of the log-hazard ratio. All designs comprised a single interim analysis with an efficacy stopping rule and the possibility of sample size adaptation at this interim step. Three frequentist approaches were defined based on the terminal analysis: combination of stagewise statistics (Wassmer) or ofp $$ p $$ values (Desseaux), or on patientwise splitting (Jörgens), and we compared the results with those of the Bayesian monitoring approach (Freedman). These different approaches were evaluated in a simulation study and then illustrated on a real dataset from a randomized clinical trial conducted in elderly patients with chronic lymphocytic leukemia. All approaches controlled for the type I error rate, except for the Bayesian monitoring approach, and yielded satisfactory power. It appears that the frequentist approaches are the best in underpowered trials. The power of all the approaches was affected by the violation of the proportional hazards (PH) assumption. For adaptive designs with a survival endpoint and a one-sided alternative hypothesis, the Wassmer and Jörgens approaches after sample size adaptation should be preferred, unless violation of PH is suspected.

Multi-arm multi-stage (MAMS) platform trials efficiently compare several treatments with a common control arm. Crucially MAMS designs allow for adjustment for multiplicity if required. If for example, the active treatment arms in a clinical trial relate to different dose levels or different routes of administration of a drug, the strict control of the family-wise error rate (FWER) is paramount. Suppose a further treatment becomes available, it is desirable to add this to the trial already in progress; to access both the practical and statistical benefits of the MAMS design. In any setting where control of the error rate is required, we must add corresponding hypotheses without compromising the validity of the testing procedure.To strongly control the FWER, MAMS designs use pre-planned decision rules that determine the recruitment of the next stage of the trial based on the available data. The addition of a treatment arm presents an unplanned change to the design that we must account for in the testing procedure. We demonstrate the use of the conditional error approach to add hypotheses to any testing procedure that strongly controls the FWER. We use this framework to add treatments to a MAMS trial in progress. Simulations illustrate the possible characteristics of such procedures.

Statistical significance of both the original and the replication study is a commonly used criterion to assess replication attempts, also known as the two-trials rule in drug development. However, replication studies are sometimes conducted although the original study is non-significant, in which case Type-I error rate control across both studies is no longer guaranteed. We propose an alternative method to assess replicability using the sum of p -values from the two studies. The approach provides a combined p -value and can be calibrated to control the overall Type-I error rate at the same level as the two-trials rule but allows for replication success even if the original study is non-significant. The unweighted version requires a less restrictive level of significance at replication if the original study is already convincing which facilitates sample size reductions of up to 10%. Downweighting the original study accounts for possible bias and requires a more stringent significance level and larger sample sizes at replication. Data from four large-scale replication projects are used to illustrate and compare the proposed method with the two-trials rule, meta-analysis and Fisher's combination method.

Meta-analyses of case series of non-arteritic central retinal artery occlusion (CRAO) indicate beneficial effects of intravenous thrombolysis when initiated early after symptom onset. Randomized data are lacking to address this question.

The REperfusion therapy with intravenous alteplase for recovery of VISION in acute central retinal artery occlusion (REVISION) investigates intravenous alteplase within 4.5 h of monocular vision loss due to acute CRAO.

This study is the randomized (1:1), double-blind, placebo-controlled, multicenter adaptive phase III trial.

Primary outcome is functional recovery to normal or mildly impaired vision in the affected eye defined as best-corrected visual acuity of the Logarithm of the Minimum Angle of Resolution of 0.5 or less at 30 days (intention-to-treat analysis). Secondary efficacy outcomes include modified Rankin Score at 90 days and quality of life. Safety outcomes include symptomatic intracranial hemorrhage, major bleeding (International Society on Thrombosis and Haemostasis definition) and mortality. Exploratory analyses of optical coherence tomography/angiography, ultrasound and magnetic resonance imaging (MRI) biomarkers will be conducted.

Using an adaptive design with interim analysis at 120 patients, up to 422 participants (211 per arm) would be needed for 80% power (one-sided alpha = 0.025) to detect a difference of 15%, assuming functional recovery rates of 10% in the placebo arm and 25% in the alteplase arm.

By enrolling patients within 4.5 h of CRAO onset, REVISION uses insights from meta-analyses of CRAO case series and randomized thrombolysis trials in acute ischemic stroke. Increased rates of early reperfusion and good neurological outcomes in stroke may translate to CRAO with its similar pathophysiology.

ClinicalTrials.gov: NCT04965038; EU Trial Number: 2023-507388-21-00.

A platform trial approach allows adding arms to on-going trials to speed up intervention discovery programs. A control arm remains open for recruitment in a platform trial while intervention arms may be added after the onset of the study and could be terminated early for efficacy and/or futility when early stopping is allowed. The topic of utilising non-concurrent control data in the analysis of platform trials has been explored and discussed extensively. A less familiar issue is the presence of heterogeneity, which may exist for example due to modification of enrolment criteria and recruitment strategy.

We conduct a simulation study to explore the impact of heterogeneity on the analysis of a two-stage platform trial design. We consider heterogeneity in treatment effects and heteroscedasticity in outcome data across stages for a normally distributed endpoint. We examine the performance of some hypothesis testing procedures and modelling strategies. The use of non-concurrent control data is also considered accordingly. Alongside standard regression analysis, we examine the performance of a novel method that was known as the pairwise trials analysis. It is similar to a network meta-analysis approach but adjusts for treatment comparisons instead of individual studies using fixed effects.

Several testing strategies with concurrent control data seem to control the type I error rate at the required level when there is heteroscedasticity in outcome data across stages and/or a random cohort effect. The main parameter of treatment effects in some analysis models correspond to overall treatment effects weighted by stage wise sample sizes; while others correspond to the effect observed within a single stage. The characteristics of the estimates are not affected significantly by the presence of a random cohort effect and/ or heteroscedasticity.

In view of heterogeneity in treatment effect across stages, the specification of null hypotheses in platform trials may need to be more subtle. We suggest employing testing procedure of adaptive design as opposed to testing the statistics from regression models; comparing the estimates from the pairwise trials analysis method and the regression model with interaction terms may indicate if heterogeneity is negligible.

This study is to give a systematic account of sample size adaptation designs (SSADs) and to provide direct proof of the efficiency advantage of general SSADs over group sequential designs (GSDs) from a different perspective. For this purpose, a class of sample size mapping functions to define SSADs is introduced. Under the two-stage adaptive clinical trial setting, theorems are developed to describe the properties of SSADs. Sufficient conditions are derived and used to prove analytically that SSADs based on the weighted combination test can be uniformly more efficient than GSDs in a range of likely values of the true treatment differenceδ $$ \delta $$ . As shown in various scenarios, given a GSD, a fully adaptive SSAD can be obtained that has sufficient statistical power similar to that of the GSD but has a smaller average sample size for allδ $$ \delta $$ in the range. The associated sample size savings can be substantial. A practical design example and suggestions on the steps to find efficient SSADs are also provided.

Enisamium is an orally available therapeutic that inhibits influenza A virus and SARS-CoV-2 replication. We evaluated the clinical efficacy of enisamium treatment combined with standard care in adult, hospitalized patients with moderate COVID-19 requiring external oxygen. Hospitalized patients with laboratory-confirmed SARS-CoV-2 infection were randomly assigned to receive either enisamium (500 mg per dose, four times a day) or a placebo. The primary outcome was an improvement of at least two points on an eight-point severity rating (SR) scale within 29 days of randomization. We initially set out to study the effect of enisamium on patients with a baseline SR of 4 or 5. However, because the study was started early in the COVID-19 pandemic, and COVID-19 had been insufficiently studied at the start of our study, an interim analysis was performed alongside a conditional power analysis in order to ensure patient safety and assess whether the treatment was likely to be beneficial for one or both groups. Following this analysis, a beneficial effect was observed for patients with an SR of 4 only, i.e., patients with moderate COVID-19 requiring supplementary oxygen. The study was continued for these COVID-19 patients. Overall, a total of 592 patients were enrolled and randomized between May 2020 and March 2021. Patients with a baseline SR of 4 were divided into two groups: 142 (49.8%) were assigned to the enisamium group and 143 (50.2%) to the placebo group. An analysis of the population showed that if patients were treated within 4 days of the onset of COVID-19 symptoms (n = 33), the median time to improvement was 8 days for the enisamium group and 13 days for the placebo group (p = 0.005). For patients treated within 10 days of the onset of COVID-19 symptoms (n = 154), the median time to improvement was 10 days for the enisamium group and 12 days for the placebo group (p = 0.002). Our findings suggest that enisamium is safe to use with COVID-19 patients, and that the observed clinical benefit of enisamium is worth reporting and studying in detail.

Due to the dependency structure in the sampling process, adaptive trial designs create challenges in point and interval estimation and in the calculation of P-values. Optimal adaptive designs, which are designs where the parameters governing the adaptivity are chosen to maximize some performance criterion, suffer from the same problem. Various analysis methods which are able to handle this dependency structure have already been developed. In this work, we aim to give a comprehensive summary of these methods and show how they can be applied to the class of designs with planned adaptivity, of which optimal adaptive designs are an important member. The defining feature of these kinds of designs is that the adaptive elements are completely prespecified. This allows for explicit descriptions of the calculations involved, which makes it possible to evaluate different methods in a fast and accurate manner. We will explain how to do so, and present an extensive comparison of the performance characteristics of various estimators between an optimal adaptive design and its group-sequential counterpart.

Single-arm phase II trials are very common in oncology. A fixed sample trial may lack sufficient power if the true efficacy is less than the assumed one. Adaptive designs have been proposed in the literature. We propose a Simon's design based, adaptive sequential design. Simon's design is the most used fixed sample design for single-arm phase II oncology trials. A prominent feature of Simon's design is that it minimizes the sample size when there is no clinically meaningful efficacy. We identify Simon's trial as a special group sequential design. Established methods for sample size re-estimation (SSR) can be readily applied to Simon's design. Simulations show that simply adding SSR to Simon's design may still not provide desirable power. We propose some expansions to Simon's design. The expanded design with SSR can provide even more power.

Before the era of immunotherapies and antibody-drug conjugates, there were limited chemotherapeutic options for patients with recurrent and metastatic cervical cancer. Combination therapies with cisplatin have shown some superiority over monotherapy. This study examined platinum-free treatment regimens, comparing a combination of topotecan and paclitaxel (TP) with topotecan and cisplatin (TC) in patients with recurrent or metastatic cervical cancer, with or without prior platinum-based treatment.

The AGO-Zervix-1 Study (NCT01405235) is a prospective, randomized phase III study in which patients were randomly assigned at a 1:1 ratio to treatment within the control arm with topotecan (0.75 mg/m2) on days 1-3 and cisplatin (50 mg/m2) on day 1 every 3 weeks and in the study arm topotecan (1.75 mg/m2) and paclitaxel (70 mg/m2) on days 1, 8, and 15 every 4 weeks or treatment. The primary study aim was overall survival; progression-free survival, toxicity, and quality of life were secondary aims. The interim and final analysis is here reported after recruitment of 173 of 312 planned patients.

Median overall survival in the TP arm was 9.6 months, compared with 12.0 months in the TC arm (log-rank test, P = 0.33). Median progression-free survival rates were 4.4 months with TP and 4.2 months with TC (log-rank test, P = 0.47). Leukopenia and nausea/vomiting were more frequent in the cisplatin-containing arm. Otherwise, toxicity profiles were comparable. There were no differences in FACT-G-assessed quality of life.

Platinum-based combination chemotherapy remains the standard of care chemotherapy regimen for patients with recurrent or metastatic cervical cancer.

The propensity score-integrated composite likelihood (PSCL) method is one method that can be utilized to design and analyze an application when real-world data (RWD) are leveraged to augment a prospectively designed clinical study. In the PSCL, strata are formed based on propensity scores (PS) such that similar subjects in terms of the baseline covariates from both the current study and RWD sources are placed in the same stratum, and then composite likelihood method is applied to down-weight the information from the RWD. While PSCL was originally proposed for a fixed design, it can be extended to be applied under an adaptive design framework with the purpose to either potentially claim an early success or to re-estimate the sample size. In this paper, a general strategy is proposed due to the feature of PSCL. For the possibility of claiming early success, Fisher's combination test is utilized. When the purpose is to re-estimate the sample size, the proposed procedure is based on the test proposed by Cui, Hung, and Wang. The implementation of these two procedures is demonstrated via an example.

A generalized phase 1-2-3 design, Gen 1-2-3, that includes all phases of clinical treatment evaluation is proposed. The design extends and modifies the design of Chapple and Thall (2019), denoted by CT. Both designs begin with a phase 1-2 trial including dose acceptability and optimality criteria, and both select an optimal dose for phase 3. The Gen 1-2-3 design has the following key differences. In stage 1, it uses phase 1-2 criteria to identify a set of candidate doses rather than 1 dose. In stage 2, which is intermediate between phase 1-2 and phase 3, it randomizes additional patients fairly among the candidate doses and an active control treatment arm and uses survival time data from both stage 1 and stage 2 patients to select an optimal dose. It then makes a Go/No Go decision of whether or not to conduct phase 3 based on the predictive probability that the selected optimal dose will provide a specified substantive improvement in survival time over the control. A simulation study shows that the Gen 1-2-3 design has desirable operating characteristics compared to the CT design and 2 conventional designs.

Adaptive clinical trials allow researchers to make preplanned modifications based on accumulating data from an ongoing trial while preserving the trial's integrity and validity. These modifications may include early termination in cases of successes or lack of efficacy, refining the sample size, altering treatments or doses, or focusing recruitment efforts on individuals most likely to benefit. In this issue of NEJM Evidence, Geisler et al.1 report results from the Apixaban for Treatment of Embolic Stroke of Undetermined Source (ATTICUS) trial, a multicenter randomized trial of apixaban compared with aspirin in patients with cardioembolism risk factors.

BACKGROUND: Rivaroxaban and dabigatran were not superior to aspirin in trials of patients with embolic stroke of undetermined source (ESUS). It is unknown whether apixaban is superior to aspirin in patients with ESUS and known risk factors for cardioembolism. METHODS: We conducted a multicenter, randomized, open-label, blinded-outcome trial of apixaban (5 mg twice daily) compared with aspirin (100 mg once daily) initiated within 28 days after ESUS in patients with at least one predictive factor for atrial fibrillation or a patent foramen ovale. Cardiac monitoring was mandatory, and aspirin treatment was switched to apixaban in case of atrial fibrillation detection. The primary outcome was any new ischemic lesion on brain magnetic resonance imaging (MRI) during 12-month follow-up. Secondary outcomes included major and clinically relevant nonmajor bleeding. RESULTS: A total of 352 patients were randomly assigned to receive apixaban (178 patients) or aspirin (174 patients) at a median of 8 days after ESUS. At 12-month follow-up, MRI follow-up was available in 325 participants (92.3%). New ischemic lesions occurred in 23 of 169 (13.6%) participants in the apixaban group and in 25 of 156 (16.0%) participants in the aspirin group (adjusted odds ratio, 0.79; 95% confidence interval, 0.42 to 1.48; P=0.57). Major and clinically relevant nonmajor bleeding occurred in five and seven participants, respectively (1-year cumulative incidences, 2.9 and 4.2; hazard ratio, 0.68; 95% confidence interval, 0.22 to 2.16). Serious adverse event rates were 43.9 per 100 person-years in those given apixaban and 45.7 per 100 person-years in those given aspirin. The Apixaban for the Treatment of Embolic Stroke of Undetermined Source trial was terminated after a prespecified interim analysis as a result of futility. CONCLUSIONS: Apixaban treatment was not superior to cardiac monitoring-guided aspirin in preventing new ischemic lesions in an enriched ESUS population. (Funded by Bristol-Myers Squibb and Medtronic Europe; ClinicalTrials.gov number, NCT02427126.)

Oxygen is essential for cellular energy metabolism. Neurons are particularly vulnerable to hypoxia. Increasing oxygen supply shortly after stroke onset could preserve the ischemic penumbra until revascularization occurs.

PROOF investigates the use of normobaric oxygen (NBO) therapy within 6 h of symptom onset/notice for brain-protective bridging until endovascular revascularization of acute intracranial anterior-circulation occlusion.

Randomized (1:1), standard treatment-controlled, open-label, blinded endpoint, multicenter adaptive phase IIb trial.

Primary outcome is ischemic core growth (mL) from baseline to 24 h (intention-to-treat analysis). Secondary efficacy outcomes include change in NIHSS from baseline to 24 h, mRS at 90 days, cognitive and emotional function, and quality of life. Safety outcomes include mortality, intracranial hemorrhage, and respiratory failure. Exploratory analyses of imaging and blood biomarkers will be conducted.

Using an adaptive design with interim analysis at 80 patients per arm, up to 456 participants (228 per arm) would be needed for 80% power (one-sided alpha 0.05) to detect a mean reduction of ischemic core growth by 6.68 mL, assuming 21.4 mL standard deviation.

By enrolling endovascular thrombectomy candidates in an early time window, the trial replicates insights from preclinical studies in which NBO showed beneficial effects, namely early initiation of near 100% inspired oxygen during short temporary ischemia. Primary outcome assessment at 24 h on follow-up imaging reduces variability due to withdrawal of care and early clinical confounders such as delayed extubation and aspiration pneumonia.

ClinicalTrials.gov: NCT03500939; EudraCT: 2017-001355-31.

High-level evidence regarding the technique of abdominal wall closure for patients undergoing emergency midline laparotomy is sparse. Therefore, we conducted a randomized controlled trial (RCT) to evaluate the efficacy and safety of two commonly applied abdominal wall closure strategies after primary emergency midline laparotomy.

CONTINT was a multi-center pragmatic open-label exploratory randomized controlled parallel trial. Two different abdominal wall closure strategies in patients undergoing primary midline laparotomy for an emergency surgical intervention with a suspected septic focus in the abdominal cavity were compared: the continuous, all-layer suture and the interrupted suture technique. The primary composite endpoint was burst abdomen within 30 days after surgery or incisional hernia within 12 months. As reliable data on this composite primary endpoint were not available for patients undergoing emergency surgery, it was planned to initially recruit 80 patients and conduct an interim analysis after these had completed the 12 months follow-up.

From August 31, 2009, to June 28, 2012, 124 patients were randomized of whom 119 underwent surgery and were analyzed according to the intention-to-treat (ITT) principal. The primary composite endpoint did not differ between the continuous suture (C: 27.1%) and the interrupted suture group (I: 30.0%). None of the individual components of the primary endpoint (reoperation due to burst abdomen after 30 days (C: 13.5%, I: 15.1%) and reoperation due to incisional hernia (C: 3.0%, I:11.1%)) differed between groups. Time needed for fascial closure was longer in the interrupted suture group (C: 12.8 ± 4.5 min, I: 17.4 ± 6.1 min). BMI was associated with burst abdomen during the first 30 days with an OR of 1.17 (95% CI 1.04-1.32).

This RCT showed no difference between continuous suture with slowly absorbable suture versus interrupted rapidly absorbable sutures after primary emergency midline laparotomy in rates of postoperative burst abdomen and incisional hernia after one year. However, the trial was stopped after the interim analysis due to futility as there was no chance to show superiority of one suture technique.

Adaptive designs are increasingly developed and used to improve all phases of clinical trials and in biomedical studies in various ways to address different statistical issues. We first present an overview of adaptive designs and note their numerous advantages over traditional clinical trials. In particular, we provide a concrete demonstration that shows how recent adaptive design strategies can further improve an adaptive trial implemented 13 years ago. Despite their usefulness, adaptive designs are still not widely implemented in clinical trials. We offer a few possible reasons and propose some ways to use them more broadly in practice, which include greater availability of software tools and interactive websites to generate optimal adaptive trials freely and effectively, including the use of metaheuristics to facilitate the search for an efficient trial design. To this end, we present several web-based tools for finding various adaptive and nonadaptive optimal designs and discuss nature-inspired metaheuristics. Metaheuristics are assumptions-free general purpose optimization algorithms widely used in computer science and engineering to tackle all kinds of challenging optimization problems, and their use in designing clinical trials is just emerging. We describe a few recent such applications and some of their capabilities for designing various complex trials. Particle swarm optimization is an exemplary nature-inspired algorithm, and similar to others, it has a simple definition but many moving parts, making it hard to study its properties analytically. We investigated one of its hitherto unstudied issues on how to bring back out-of-range candidates during the search for the optimum of the search domain and show that different strategies can impact the success and time of the search. We conclude with a few caveats on the use of metaheuristics for a successful search.

Conditional (European Medicines Agency) or accelerated (U.S. Food and Drug Administration) approval of drugs allows earlier access to promising new treatments that address unmet medical needs. Certain post-marketing requirements must typically be met in order to obtain full approval, such as conducting a new post-market clinical trial. We study the applicability of the recently developed harmonic mean χ 2 $$ {\chi}^2 $$ -test to this conditional or accelerated approval framework. The proposed approach can be used both to support the design of the post-market trial and the analysis of the combined evidence provided by both trials. Other methods considered are the two-trials rule, Fisher's criterion and Stouffer's method. In contrast to some of the traditional methods, the harmonic mean χ 2 $$ {\chi}^2 $$ -test always requires a post-market clinical trial. If the p $$ p $$ -value from the pre-market clinical trial is ≪ 0.025 $$ \ll 0.025 $$ , a smaller sample size for the post-market clinical trial is needed than with the two-trials rule. For illustration, we apply the harmonic mean χ 2 $$ {\chi}^2 $$ -test to a drug which received conditional (and later full) market licensing by the EMA. A simulation study is conducted to study the operating characteristics of the harmonic mean χ 2 $$ {\chi}^2 $$ -test and two-trials rule in more detail. We finally investigate the applicability of these two methods to compute the power at interim of an ongoing post-market trial. These results are expected to aid in the design and assessment of the required post-market studies in terms of the level of evidence required for full approval.

A popular design for clinical trials assessing targeted therapies is the two-stage adaptive enrichment design with recruitment in stage 2 limited to a biomarker-defined subgroup chosen based on data from stage 1. The data-dependent selection leads to statistical challenges if data from both stages are used to draw inference on treatment effects in the selected subgroup. If subgroups considered are nested, as when defined by a continuous biomarker, treatment effect estimates in different subgroups follow the same distribution as estimates in a group-sequential trial. This result is used to obtain tests controlling the familywise type I error rate (FWER) for six simple subgroup selection rules, one of which also controls the FWER for any selection rule. Two approaches are proposed: one based on multivariate normal distributions suitable if the number of possible subgroups, k, is small, and one based on Brownian motion approximations suitable for large k. The methods, applicable in the wide range of settings with asymptotically normal test statistics, are illustrated using survival data from a breast cancer trial.

We congratulate Dr. Nigel Stallard on his stimulating paper on adaptive enrichment designs with a continuous biomarker. Dr. Stallard details a framework for a large and interesting class of enrichment procedures. His work has motivated us to offer some thoughts in response. Dr. Stallard's strategy is to use the maximum of a test statistic over a set of possible threshold values to define the enriched population to be sampled in a second stage. This reminds us of procedures for identifying a change point, a biomarker value beyond which the effect of treatment is increased. For simplicity we focus our comments on Dr. Stallard's Rule 1 for selecting the second-stage sampling threshold. Using this rule, we present the likelihood ratio approach for adaptive testing and compare it to Dr. Stallard's approach for a few scenarios.

We assessed whether the bicarbonate-rich mineral water Staatl. Fachingen STILL is superior over conventional mineral water in relieving heartburn.

Multicentre, double-blind, randomised, placebo-controlled trial STOMACH STILL in adult patients with frequent heartburn episodes since ≥6 months and without moderate/severe reflux oesophagitis. Patients drank 1.5 L/day verum or placebo over the course of the day for 6 weeks. Primary endpoint was the percentage of patients with reduction of ≥5 points in the Reflux Disease Questionnaire (RDQ) score for 'heartburn'. Secondary endpoints included symptom reduction (RDQ), health-related quality of life (HRQOL, Quality of Life in Reflux and Dyspepsia (QOLRAD)), intake of rescue medication and safety/tolerability.

Of 148 randomised patients (verum: n=73, placebo: n=75), 143 completed the trial. Responder rates were 84.72% in the verum and 63.51% in the placebo group (p=0.0035, number needed to treat=5). Symptoms improved under verum compared with placebo for the dimension 'heartburn' (p=0.0003) and the RDQ total score (p=0.0050). HRQOL improvements under verum compared with placebo were reported for 3 of 5 QOLRAD domains, that is, 'food/drink problems' (p=0.0125), 'emotional distress' (p=0.0147) and 'vitality' (p=0.0393). Mean intake of rescue medication decreased from 0.73 tablets/day at baseline to 0.47 tablets/day in week 6 in the verum group, whereas in the placebo group it remained constant during the trial. Only three patients had treatment-related adverse events (verum: n=1, placebo: n=2).

STOMACH STILL is the first controlled clinical trial demonstrating superiority of a mineral water over placebo in relieving heartburn, accompanied by an improved HRQOL.

EudraCT 2017-001100-30.

In the precision medicine era, (prespecified) subgroup analyses are an integral part of clinical trials. Incorporating multiple populations and hypotheses in the design and analysis plan, adaptive designs promise flexibility and efficiency in such trials. Adaptations include (unblinded) interim analyses (IAs) or blinded sample size reviews. An IA offers the possibility to select promising subgroups and reallocate sample size in further stages. Trials with these features are known as adaptive enrichment designs. Such complex designs comprise many nuisance parameters, such as prevalences of the subgroups and variances of the outcomes in the subgroups. Additionally, a number of design options including the timepoint of the sample size review and timepoint of the IA have to be selected. Here, for normally distributed endpoints, we propose a strategy combining blinded sample size recalculation and adaptive enrichment at an IA, that is, at an early timepoint nuisance parameters are reestimated and the sample size is adjusted while subgroup selection and enrichment is performed later. We discuss implications of different scenarios concerning the variances as well as the timepoints of blinded review and IA and investigate the design characteristics in simulations. The proposed method maintains the desired power if planning assumptions were inaccurate and reduces the sample size and variability of the final sample size when an enrichment is performed. Having two separate timepoints for blinded sample size review and IA improves the timing of the latter and increases the probability to correctly enrich a subgroup.

Recent FDA guidance on adaptive clinical trial designs defines bias as "a systematic tendency for the estimate of treatment effect to deviate from its true value," and states that it is desirable to obtain and report estimates of treatment effects that reduce or remove this bias. The conventional end-of-trial point estimates of the treatment effects are prone to bias in many adaptive designs, because they do not take into account the potential and realized trial adaptations. While much of the methodological developments on adaptive designs have tended to focus on control of type I error rates and power considerations, in contrast the question of biased estimation has received relatively less attention. This article is the first in a two-part series that studies the issue of potential bias in point estimation for adaptive trials. Part I provides a comprehensive review of the methods to remove or reduce the potential bias in point estimation of treatment effects for adaptive designs, while part II illustrates how to implement these in practice and proposes a set of guidelines for trial statisticians. The methods reviewed in this article can be broadly classified into unbiased and bias-reduced estimation, and we also provide a classification of estimators by the type of adaptive design. We compare the proposed methods, highlight available software and code, and discuss potential methodological gaps in the literature.

Covariate adjustment via a regression approach is known to increase the precision of statistical inference when fixed trial designs are employed in randomized controlled studies. When an adaptive multi-arm design is employed with the ability to select treatments, it is unclear how covariate adjustment affects various aspects of the study. Consider the design framework that relies on pre-specified treatment selection rule(s) and a combination test approach for hypothesis testing. It is our primary goal to evaluate the impact of covariate adjustment on adaptive multi-arm designs with treatment selection. Our secondary goal is to show how the Uniformly Minimum Variance Conditionally Unbiased Estimator can be extended to account for covariate adjustment analytically. We find that adjustment with different sets of covariates can lead to different treatment selection outcomes and hence probabilities of rejecting hypotheses. Nevertheless, we do not see any negative impact on the control of the familywise error rate when covariates are included in the analysis model. When adjusting for covariates that are moderately or highly correlated with the outcome, we see various benefits to the analysis of the design. Conversely, there is negligible impact when including covariates that are uncorrelated with the outcome. Overall, pre-specification of covariate adjustment is recommended for the analysis of adaptive multi-arm design with treatment selection. Having the statistical analysis plan in place prior to the interim and final analyses is crucial, especially when a non-collapsible measure of treatment effect is considered in the trial.

This study was conducted to determine the effect of hypothermic temperature control after in-hospital cardiac arrest (IHCA) on mortality and functional outcome as compared with normothermia.

An investigator initiated, open-label, blinded-outcome-assessor, multicenter, randomized controlled trial comparing hypothermic temperature control (32-34°C) for 24 h with normothermia after IHCA in 11 hospitals in Germany. The primary endpoint was all-cause mortality after 180 days. Secondary end points included in-hospital mortality and favorable functional outcome using the Cerebral Performance Category scale after 180 days. A Cerebral Performance Category score of 1 or 2 was defined as a favorable functional outcome.

A total of 1055 patients were screened for eligibility and 249 patients were randomized: 126 were assigned to hypothermic temperature control and 123 to normothermia. The mean age of the cohort was 72.6±10.4 years, 64% (152 of 236) were male, 73% (166 of 227) of cardiac arrests were witnessed, 25% (57 of 231) had an initial shockable rhythm, and time to return of spontaneous circulation was 16.4±10.5 minutes. Target temperature was reached within 4.2±2.8 hours after randomization in the hypothermic group and temperature was controlled for 48 hours at 37.0°±0.9°C in the normothermia group. Mortality by day 180 was 72.5% (87 of 120) in hypothermic temperature control arm, compared with 71.2% (84 of 118) in the normothermia group (relative risk, 1.03 [95% CI, 0.79-1.40]; P=0.822). In-hospital mortality was 62.5% (75 of 120) in the hypothermic temperature control as compared with 57.6% (68 of 118) in the normothermia group (relative risk, 1.11 [95% CI, 0.86-1.46, P=0.443). Favorable functional outcome (Cerebral Performance Category 1 or 2) by day 180 was 22.5% (27 of 120) in the hypothermic temperature control, compared with 23.7% (28 of 118) in the normothermia group (relative risk, 1.04 [95% CI, 0.78-1.44]; P=0.822). The study was prematurely terminated because of futility.

Hypothermic temperature control as compared with normothermia did not improve survival nor functional outcome at day 180 in patients presenting with coma after IHCA. The HACA in-hospital trial (Hypothermia After Cardiac Arrest in-hospital) was underpowered and may have failed to detect clinically important differences between hypothermic temperature control and normothermia.

URL: https://www.

gov; Unique Identifier: NCT00457431.