START NOW: a cognitive behavioral skills training for adolescent girls with conduct or oppositional defiant disorder - a randomized clinical trial.

BACKGROUND: Conduct disorder (CD) and oppositional defiant disorder (ODD) both convey a high risk for maladjustment later in life and are understudied in girls. Here, we aimed at confirming the efficacy of START NOW, a cognitive-behavioral, dialectical behavior therapy-oriented skills training program aiming to enhance emotion regulation skills, interpersonal and psychosocial adjustment, adapted for female adolescents with CD or ODD. METHODS: A total of 127 girls were included in this prospective, cluster randomized, multi-center, parallel group, quasi-randomized, controlled phase III trial, which tested the efficacy of START NOW (n = 72) compared with standard care (treatment as usual, TAU, n = 55). All female adolescents had a clinical diagnosis of CD or ODD, were 15.6 (±1.5) years on average (range: 12-20 years), and were institutionalized in youth welfare institutions. The two primary endpoints were the change in number of CD/ODD symptoms between (1) baseline (T1) and post-treatment (T3), and (2) between T1 and 12-week follow-up (T4). RESULTS: Both treatment groups showed reduced CD/ODD symptoms at T3 compared with T1 (95% CI: START NOW = -4.87, -2.49; TAU = -4.94, -2.30). There was no significant mean difference in CD/ODD symptom reduction from T1 to T3 between START NOW and TAU (-0.056; 95% CI = -1.860, 1.749; Hedge's g = -0.011). However, the START NOW group showed greater mean symptom reduction from T1 to T4 (-2.326; 95% CI = -4.274, -0.378; Hedge's g = -0.563). Additionally, secondary endpoint results revealed a reduction in staff reported aggression and parent-reported irritability at post assessment. CONCLUSIONS: Although START NOW did not result in greater symptom reduction from baseline to post-treatment compared with TAU, the START NOW group showed greater symptom reduction from baseline to follow-up with a medium effect size, which indicates a clinically meaningful delayed treatment effect.

Point estimation, confidence intervals, and P-values for optimal adaptive two-stage designs with normal endpoints.

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.

Two-stage group-sequential designs with delayed responses - what is the point of applying corresponding methods?

BACKGROUND: In group-sequential designs, it is typically assumed that there is no time gap between patient enrollment and outcome measurement in clinical trials. However, in practice, there is usually a lag between the two time points. This can affect the statistical analysis of the data, especially in trials with interim analyses. One approach to address delayed responses has been introduced by Hampson and Jennison (J R Stat Soc Ser B Stat Methodol 75:3-54, 2013), who proposed the use of error-spending stopping boundaries for patient enrollment, followed by critical values to reject the null hypothesis if the stopping boundaries are crossed beforehand. Regarding the choice of a trial design, it is important to consider the efficiency of trial designs, e.g. in terms of the probability of trial success (power) and required resources (sample size and time). METHODS: This article aims to shed more light on the performance comparison of group sequential clinical trial designs that account for delayed responses and designs that do not. Suitable performance measures are described and designs are evaluated using the R package rpact. By doing so, we provide insight into global performance measures, discuss the applicability of conditional performance characteristics, and finally whether performance gain justifies the use of complex trial designs that incorporate delayed responses. RESULTS: We investigated how the delayed response group sequential test (DR-GSD) design proposed by Hampson and Jennison (J R Stat Soc Ser B Stat Methodol 75:3-54, 2013) can be extended to include nonbinding lower recruitment stopping boundaries, illustrating that their original design framework can accommodate both binding and nonbinding rules when additional constraints are imposed. Our findings indicate that the performance enhancements from methods incorporating delayed responses heavily rely on the sample size at interim and the volume of data in the pipeline, with overall performance gains being limited. CONCLUSION: This research extends existing literature on group-sequential designs by offering insights into differences in performance. We conclude that, given the overall marginal differences, discussions regarding appropriate trial designs can pivot towards practical considerations of operational feasibility.

Sample size recalculation in three-stage clinical trials and its evaluation.

BACKGROUND: In clinical trials, the determination of an adequate sample size is a challenging task, mainly due to the uncertainty about the value of the effect size and nuisance parameters. One method to deal with this uncertainty is a sample size recalculation. Thereby, an interim analysis is performed based on which the sample size for the remaining trial is adapted. With few exceptions, previous literature has only examined the potential of recalculation in two-stage trials. METHODS: In our research, we address sample size recalculation in three-stage trials, i.e. trials with two pre-planned interim analyses. We show how recalculation rules from two-stage trials can be modified to be applicable to three-stage trials. We also illustrate how a performance measure, recently suggested for two-stage trial recalculation (the conditional performance score) can be applied to evaluate recalculation rules in three-stage trials, and we describe performance evaluation in those trials from the global point of view. To assess the potential of recalculation in three-stage trials, we compare, in a simulation study, two-stage group sequential designs with three-stage group sequential designs as well as multiple three-stage designs with recalculation. RESULTS: While we observe a notable favorable effect in terms of power and expected sample size by using three-stage designs compared to two-stage designs, the benefits of recalculation rules appear less clear and are dependent on the performance measures applied. CONCLUSIONS: Sample size recalculation is also applicable in three-stage designs. However, the extent to which recalculation brings benefits depends on which trial characteristics are most important to the applicants.

Extension of a conditional performance score for sample size recalculation rules to the setting of binary endpoints.

BACKGROUND: Sample size calculation is a central aspect in planning of clinical trials. The sample size is calculated based on parameter assumptions, like the treatment effect and the endpoint's variance. A fundamental problem of this approach is that the true distribution parameters are not known before the trial. Hence, sample size calculation always contains a certain degree of uncertainty, leading to the risk of underpowering or oversizing a trial. One way to cope with this uncertainty are adaptive designs. Adaptive designs allow to adjust the sample size during an interim analysis. There is a large number of such recalculation rules to choose from. To guide the choice of a suitable adaptive design with sample size recalculation, previous literature suggests a conditional performance score for studies with a normally distributed endpoint. However, binary endpoints are also frequently applied in clinical trials and the application of the conditional performance score to binary endpoints is not yet investigated. METHODS: We extend the theory of the conditional performance score to binary endpoints by suggesting a related one-dimensional score parametrization. We moreover perform a simulation study to evaluate the operational characteristics and to illustrate application. RESULTS: We find that the score definition can be extended without modification to the case of binary endpoints. We represent the score results by a single distribution parameter, and therefore derive a single effect measure, which contains the difference in proportions [Formula: see text] between the intervention and the control group, as well as the endpoint proportion [Formula: see text] in the control group. CONCLUSIONS: This research extends the theory of the conditional performance score to binary endpoints and demonstrates its application in practice.

Bright light therapy versus physical exercise to prevent co-occurring depression in adolescents and young adults with attention-deficit/hyperactivity disorder: a multicentre, three-arm, randomised controlled, pilot phase-IIa trial.

Depression is common in attention-deficit/hyperactivity disorder (ADHD), but preventive behavioural interventions are lacking. This randomised controlled, pilot phase-IIa trial aimed to study a physical exercise intervention (EI) and bright light therapy (BLT)-both implemented and monitored in an individual, naturalistic setting via a mobile health (m-health) system-for feasibility of trial design and interventions, and to estimate their effects on depressive symptoms in young people with ADHD. Two hundred seven participants aged 14-45 years were randomised to 10-week add-on intervention of either BLT (10,000 lx; daily 30-min sessions) (n = 70), EI (aerobic and muscle-strengthening activities 3 days/ week) (n = 69), or treatment-as-usual (TAU) (n = 68), of whom 165 (80%) were retained (BLT: n = 54; EI: n = 52; TAU: n = 59). Intervention adherence (i.e. ≥ 80% completed sessions) was very low for both BLT (n = 13, 22%) and EI (n = 4, 7%). Usability of the m-health system to conduct interventions was limited as indicated by objective and subjective data. Safety was high and comparable between groups. Changes in depressive symptoms (assessed via observer-blind ratings, Inventory of Depressive Symptomatology) between baseline and end of intervention were small (BLT: -0.124 [95% CI: -2.219, 1.971], EI: -2.646 [95% CI: -4.777, -0.515], TAU: -1.428 [95% CI: -3.381, 0.526]) with no group differences [F(2,153) = 1.45, p = 0.2384]. These findings suggest that the m-health approach did not achieve feasibility of EI and BLT in young people with ADHD. Prior to designing efficacy studies, strategies how to achieve high intervention adherence should be specifically investigated in this patient group. TRIAL REGISTRATION: ClinicalTrials.gov, NCT03371810, 13 December 2017.

Assessing the hierarchical beta-binomial model as a basic information sharing tool in basket trials.

The majority of statistical methods to share information in basket trials are based on a Bayesian hierarchical model with a common normal distribution for the logit-transformed response rates. The methods are of varying complexity, yet they all use this basic model. Generally, complexity is an obstacle for the application in clinical trials and that includes the use of the logit-transformation. The transformation complicates the model and impedes a direct interpretation of the hyperparameters. On the other hand, there exist basket trial designs which directly work on the probability scale of the response rate which facilitates the understanding of the model for many stakeholders. In order to reduce unnecessary complexity, we considered using a hierarchical beta-binomial model instead of the transformed models. This article investigates whether this approach is a practicable alternative to the commonly applied sharing tools based on a logit-transformation of the response rates. For this purpose, we performed a systematic comparison of the two models, starting with the distributional assumptions for the response rates, continuing with the Bayesian behavior together with binomial data in an independent setting and ended with a simulation study for the hierarchical model under various data and prior scenarios. All Bayesian comparisons require equal starting points, wherefore we propose a calibration procedure to choose similar priors for the models. The evaluation of the sharing property additionally required an evaluation measure for simulation results, which we derived in this work. The conclusion of the comparison is that the hierarchical beta-binomial model is a feasible alternative basic model to share information in basket trials.

Optimal sample size allocation for two-arm superiority and non-inferiority trials with binary endpoints.

The sample size of a clinical trial has to be large enough to ensure sufficient power for achieving the aim the study. On the other side, for ethical and economical reasons it should not be larger than necessary. The sample size allocation is one of the parameters that influences the required total sample size. For two-arm superiority and non-inferiority trials with binary endpoints, we performed extensive computations over a wide range of scenarios to determine the optimal allocation ratio that minimizes the total sample size if all other parameters are fixed. The results demonstrate, that for both superiority and non-inferiority trials the optimal allocation may deviate considerably from the case of equal sample size in both groups. However, the saving in sample size when allocating the total sample size optimally as compared to balanced allocation is typically small.

Effect of Individualized Versus Standardized Blood Pressure Management During Endovascular Stroke Treatment on Clinical Outcome: A Randomized Clinical Trial.

BACKGROUND: Optimal blood pressure (BP) management during endovascular stroke treatment is not well established. We studied whether an individualized approach for managing BP during endovascular stroke treatment gives a better clinical outcome than an approach with standardized systolic BP targets. METHODS: The INDIVIDUATE study (Individualized Blood Pressure Management During Endovascular Treatment of Acute Ischemic Stroke Under Procedural Sedation) is a randomized clinical trial with a prospective randomized open blinded end point (PROBE) design. Patients were recruited between October 1, 2020 and July 7, 2022 at a single center at a tertiary care university hospital. Patients were eligible, when they were suffering from acute ischemic stroke of the anterior circulation with occlusions of the internal carotid artery and middle cerebral artery and a National Institutes of Health Stroke Scale score of ≥8 receiving endovascular stroke treatment in procedural sedation. The intervention consists of an individualized BP management strategy, where preinterventional baseline systolic BP (SBP) values are used as intraprocedural BP targets. As a control, the standard treatment aims to maintain the intraprocedural SBP between 140 and 180 mm Hg. The main prespecified outcome is the proportion of favorable functional outcomes 90 days after stroke, defined as a modified Rankin Scale score of 0 to 2. RESULTS: Two hundred fifty patients were enrolled and included in the analysis, mean (SD) age was 77 (12) years, 142 (57%) patients were women, and mean (SD) National Institutes of Health Stroke Scale score on admission was 17 (5.2). In all, 123 (49%) patients were treated with individualized and 127 (51%) with standard BP management. Mean (SD) intraprocedural SBP was similar in the individualized versus standard BP management group (157 [19] versus 154 [18] mm Hg; P=0.16). The rate of favorable functional outcome after 3 months was not significantly different between the individualized versus the standard BP management group (25% versus 24%; adjusted odds ratio, 0.81 [95% CI, 0.41-1.61]; P=0.56). CONCLUSIONS: Among patients treated with endovascular stroke treatment due to an acute ischemic stroke of the anterior circulation, no significant difference was seen between the individualized BP management strategy, where intraprocedural SBP was targeted to baseline values, and the standardized regimen of targeting SBP between 140 and 180 mm Hg. REGISTRATION: URL: https://www. CLINICALTRIALS: gov; Unique identifier: NCT04578288.

Optimization of the two-stage group sequential three-arm gold-standard design for non-inferiority trials.

If design parameters are chosen appropriately, group sequential trial designs are known to be able to reduce the expected sample size under the alternative hypothesis compared to single-stage designs. The same holds true for the so-called 'gold-standard' design for non-inferiority trials, a design involving an experimental group, an active control group, and a placebo group. However, choosing design parameters that maximize the advantages of a two-stage approach for the three-arm gold-standard design for non-inferiority trials is not a straightforward task. In particular, optimal choices of futility boundaries for this design have not been thoroughly discussed in existing literature. We present a variation of the hierarchical testing procedure, which allows for the incorporation of binding futility boundaries at interim analyses. We show that this procedure maintains strong control of the family-wise type I error rate. Within this framework, we consider the futility and efficacy boundaries as well as the sample size allocation ratios as optimization parameters. This allows the investigation of the efficiency gain from including the option to stop for futility in addition to the ability to stop for efficacy. To analyze the extended designs, optimality criteria that include the design's performance under the alternative as well as the null hypothesis are introduced. On top of this, we discuss methods to limit the allocation of placebo patients in the trial while maintaining relatively good operating characteristics. The results of our numerical optimization procedure are discussed and a comparison of different approaches to designing a three-arm gold-standard non-inferiority trial is provided.

Two-stage designs with small sample sizes.

When applying group-sequential designs in clinical trials with normally distributed outcomes, approximate critical values are often applied. Here, normally distributed test statistics are assumed which, however, are in fact t-distributed. For small sample sizes, the approximation may lead to a serious inflation of the type I error rate. Recently, a method for computing the exact critical boundaries assuring type I error rate control was proposed and the critical boundaries for Pocock- and O'Brien-Fleming-like group-sequential designs were provided. For designs with one interim analysis, we present six alternative designs, which also control the type I error rate and in addition allow flexible design modifications. We compare the characteristics of these 6 two-stage designs. It is shown that considerable sample size savings can be achieved by including futility stopping and by optimizing the designs. Therefore, for clinical trials with small sample sizes as, for example, in the area of rare diseases, optimal two-stage designs with futility stopping may be a valuable alternative to classical group-sequential designs.

Estimation of treatment effects in early-phase randomized clinical trials involving external control data.

There are good reasons to perform a randomized controlled trial (RCT) even in early phases of clinical development. However, the low sample sizes in those settings lead to high variability of the treatment effect estimate. The variability could be reduced by adding external control data if available. For the common setting of suitable subject-level control group data only available from one external (clinical trial or real-world) data source, we evaluate different analysis options for estimating the treatment effect via hazard ratios. The impact of the external control data is usually guided by the level of similarity with the current RCT data. Such level of similarity can be determined via outcome and/or baseline covariate data comparisons. We provide an overview over existing methods, propose a novel option for a combined assessment of outcome and baseline data, and compare a selected set of approaches in a simulation study under varying assumptions regarding observable and unobservable confounder distributions using a time-to-event model. Our various simulation scenarios also reflect the differences between external clinical trial and real-world data. Data combinations via simple outcome-based borrowing or simple propensity score weighting with baseline covariate data are not recommended. Analysis options which conflate outcome and baseline covariate data perform best in our simulation study.

Subtyping of hepatocellular adenomas using Gd-EOB-DTPA: a qualitative and quantitative analysis.

BACKGROUND: The goal of medical imaging is not only to identify the entity "hepatocellular adenoma," but to detect typical magnetic resonance (MR) patterns of the subtypes so that lesions with a higher malignant transformation rate could be differentiated from those that should just be controlled. PURPOSE: To evaluate the differentiation between subtypes of hepatocellular adenomas using hepatobiliary specific contrast agent (Gd-EOB-DTPA) in MR imaging. MATERIAL/METHODS: A total of 11 patients with 39 lesions with histologically proven hepatocellular adenomas were evaluated. Of the, 34 were inflammatory hepatocellular adenomas (IHCA) and 5 were HNF1α adenomas. No ß-catenin-mutated adenoma was found. In all patients, a standard protocol considering the guidelines of the international consensus conference of Gd-EOB-DTPA was performed in a 1.5-T scanner. Besides a qualitative analysis of all sequences, we measured the quantitative signal intensity (SI) ratio in all examinations. RESULTS: Qualitative analysis showed that best sequences for differentiation of HNF1α adenomas from IHCA were T1-weighted (T1W) precontrast (P = 0.03) and portalvenous phase (P < 0.0001) as well as arterial phase (P = 0.002). All adenomas were hypointense in hepatobiliary phase (15 min). The quantitative analyses of the SI ratio and of lesion-to-liver contrast (LLC) ratio show statistically significant differences in T1W precontrast (SI: P = 0.035; LLC: P = 0.049) and portalvenous phase (SI: P = 0.002; LLC: P = 0.002). CONCLUSION: Subtyping of hepatocellular adenomas using Gd-EOB-DTPA is possible due to qualitative and quantitative analyses regarding T1W precontrast and portalvenous phase. In addition, the SI ratio and liver-to-lesion contrast ratio in the arterial phase gave additional qualitative information for differentiation.

Using real-world data to predict health outcomes-The prediction design: Application and sample size planning.

The gold standard for investigating the efficacy of a new therapy is a (pragmatic) randomized controlled trial (RCT). This approach is costly, time-consuming, and not always practicable. At the same time, huge quantities of available patient-level control condition data in analyzable format of (former) RCTs or real-world data (RWD) are neglected. Therefore, alternative study designs are desirable. The design presented here consists of setting up a prediction model for determining treatment effects under the control condition for future patients. When a new treatment is intended to be tested against a control treatment, a single-arm trial for the new therapy is conducted. The treatment effect is then evaluated by comparing the outcomes of the single-arm trial against the predicted outcomes under the control condition. While there are obvious advantages of this design compared to classical RCTs (increased efficiency, lower cost, alleviating participants' fear of being on control treatment), there are several sources of bias. Our aim is to investigate whether and how such a design-the prediction design-may be used to provide information on treatment effects by leveraging external data sources. For this purpose, we investigated under what assumptions linear prediction models could be used to predict the counterfactual of patients precisely enough to construct a test and an appropriate sample size formula for evaluating the average treatment effect in the population of a new study. A user-friendly R Shiny application (available at: https://web.imbi.uni-heidelberg.de/PredictionDesignR/) facilitates the application of the proposed methods, while a real-world application example illustrates them.

A Comprehensive Comparison of Additional Benefit Assessment Methods Applied by Institute for Quality and Efficiency in Health Care and European Society for Medical Oncology for Time-to-Event Endpoints After Significant Phase III Trials-A Simulation Study.

OBJECTIVES: After a successful Marketing Authorization Application for clinical trials with time-to-event endpoints, the degree of the added benefit from new treatments remains unknown and needs to be assessed. Unfortunately, until now no clear definition for added benefit determination of a treatment exists. Nevertheless, European authorities / societies have developed 2 "additional benefit assessment" methods, which have up to now not been compared: the European Society for Medical Oncology (ESMO) developed a dual rule considering relative and absolute benefit. The German Institute for Quality and Efficiency in Health Care (IQWiG) developed a method using upper 95% hazard ratio confidence interval. METHODS: We evaluate and compare both methods in an extensive simulation study including different censoring rates, failure time distributions, and treatment effects for sample size calculation. The methods' performance is assessed via Receiver Operating Characteristic curves, Spearman correlation, and percentage of achieved maximal scores. RESULTS: The results show that IQWiG's method has in many situations a lower maximal scoring proportion than ESMO's rule, that is, up to 28.5% versus 94.7%. Various failure time distributions lead to strongly changed maximal scoring percentages for ESMO. High positive correlation between the methods is present for moderate treatment effects. CONCLUSIONS: IQWiG's method is usually more conservative than ESMO's. ESMO's rule tends to be more susceptible for various failure time distributions. Using the lower confidence interval limit seems to be a better solution resulting in a higher true-positive rate without increasing the false-positive rate. Thus, IQWiG's method might need to be adapted accordingly to achieve a better overall classification.

Optimal unplanned design modification in adaptive two-stage trials.

Adaptive planning of clinical trials allows modifying the entire trial design at any time point mid-course. In this paper, we consider the case when a trial-external update of the planning assumptions during the ongoing trial makes an unforeseen design adaptation necessary. We take up the idea to construct adaptive designs with defined features by solving an optimization problem and apply it to the situation of unplanned design reassessment. By using the conditional error principle, we present an approach on how to optimally modify the trial design at an unplanned interim analysis while at the same time strictly protecting the type I error rate. This linking of optimal design planning and the conditional error principle allows sound reactions to unforeseen events that make a design reassessment necessary.

An adaptive design for early clinical development including interim decision for single-arm trial with external controls or randomized trial.

In early clinical development, randomized controlled trials (RCT) or single-arm trials with external controls (SATwEC) are design options, which allow adjustment for confounding: RCT via design, SATwEC via analysis using propensity score methods. SATwEC requires less investment than RCT. However, if the confounder space substantially differs between the experimental and external control group, the SATwEC might lead to inappropriate decisions for further development. We develop an adaptive two-stage design (ATD) for early clinical development that reduces the risk of unreliable decision-making at the end of a SATwEC. In Stage I, subjects are solely assigned to the experimental group. If at the interim the propensity score distributions of internal and external data are comparable based on the preference score, the subjects in stage II will again be solely assigned to the experimental arm; if not, a randomized stage II will be conducted. In a simulation study guided by a motivating example, data is generated using a time-to-event model with observable and unobservable confounders. The confounder space is varied to investigate the impact on false go/stop probabilities as well as a loss function, which reflects the quality of treatment effect estimates and decision-making. The proposed ATD provides a compromise between optimizing quality (as expressed by false go/stop probabilities and the loss function) and investment (defined by sample size and trial duration).

Effect of Early vs Standard Approach to Tracheostomy on Functional Outcome at 6 Months Among Patients With Severe Stroke Receiving Mechanical Ventilation: The SETPOINT2 Randomized Clinical Trial.

Importance: Many patients with severe stroke have impaired airway protective reflexes, resulting in prolonged invasive mechanical ventilation. Objective: To test whether early vs standard tracheostomy improved functional outcome among patients with stroke receiving mechanical ventilation. Design, Setting, and Participants: In this randomized clinical trial, 382 patients with severe acute ischemic or hemorrhagic stroke receiving invasive ventilation were randomly assigned (1:1) to early tracheostomy (≤5 days of intubation) or ongoing ventilator weaning with standard tracheostomy if needed from day 10. Patients were randomized between July 28, 2015, and January 24, 2020, at 26 US and German neurocritical care centers. The final date of follow-up was August 9, 2020. Interventions: Patients were assigned to an early tracheostomy strategy (n = 188) or to a standard tracheostomy (control group) strategy (n = 194). Main Outcomes and Measures: The primary outcome was functional outcome at 6 months, based on the modified Rankin Scale score (range, 0 [best] to 6 [worst]) dichotomized to a score of 0 (no disability) to 4 (moderately severe disability) vs 5 (severe disability) or 6 (death). Results: Among 382 patients randomized (median age, 59 years; 49.8% women), 366 (95.8%) completed the trial with available follow-up data on the primary outcome (177 patients [94.1%] in the early group; 189 patients [97.4%] in the standard group). A tracheostomy (predominantly percutaneously) was performed in 95.2% of the early tracheostomy group in a median of 4 days after intubation (IQR, 3-4 days) and in 67% of the control group in a median of 11 days after intubation (IQR, 10-12 days). The proportion without severe disability (modified Rankin Scale score, 0-4) at 6 months was not significantly different in the early tracheostomy vs the control group (43.5% vs 47.1%; difference, -3.6% [95% CI, -14.3% to 7.2%]; adjusted odds ratio, 0.93 [95% CI, 0.60-1.42]; P = .73). Of the serious adverse events, 5.0% (6 of 121 reported events) in the early tracheostomy group vs 3.4% (4 of 118 reported events) were related to tracheostomy. Conclusions and Relevance: Among patients with severe stroke receiving mechanical ventilation, a strategy of early tracheostomy, compared with a standard approach to tracheostomy, did not significantly improve the rate of survival without severe disability at 6 months. However, the wide confidence intervals around the effect estimate may include a clinically important difference, so a clinically relevant benefit or harm from a strategy of early tracheostomy cannot be excluded. Trial Registration: ClinicalTrials.gov Identifier: NCT02377167.

Monotonicity conditions for avoiding counterintuitive decisions in basket trials.

In a basket trial, a new treatment is tested in different subgroups, called the baskets. In oncology, the baskets usually comprise patients with different primary tumor sites but a common biomarker. Most basket trials are uncontrolled phase II trials and investigate a binary endpoint such as tumor response. To combine the data of baskets that show a similar response to the treatment, many basket trial designs use Bayesian borrowing methods. This increases the power compared to a basketwise analysis. However, it can lead to posterior probabilities that are not monotonically increasing in the number of responses. We show that, as a consequence, two types of counterintuitive decisions can arise-one that occurs within a single trial and one that occurs when the results are compared between different trials. We propose two monotonicity conditions for the inference in basket trials. Using a design recently proposed by Fujikawa and colleagues, we investigate the case of a single-stage basket trial with equal sample sizes in all baskets and show that, as the number of baskets increases, these conditions are violated for a wide range of different borrowing strengths. We show that in the investigated scenarios pruning baskets can help to ensure that the monotonicity conditions hold and investigate how this affects type I error rate and power.

Optimization of adaptive designs with respect to a performance score.

Adaptive designs are an increasingly popular method for the adaptation of design aspects in clinical trials, such as the sample size. Scoring different adaptive designs helps to make an appropriate choice among the numerous existing adaptive design methods. Several scores have been proposed to evaluate adaptive designs. Moreover, it is possible to determine optimal two-stage adaptive designs with respect to a customized objective score by solving a constrained optimization problem. In this paper, we use the conditional performance score by Herrmann et al. (2020) as the optimization criterion to derive optimal adaptive two-stage designs. We investigate variations of the original performance score, for example, by assigning different weights to the score components and by incorporating prior assumptions on the effect size. We further investigate a setting where the optimization framework is extended by a global power constraint, and additional optimization of the critical value function next to the stage-two sample size is performed. Those evaluations with respect to the sample size curves and the resulting design's performance can contribute to facilitate the score's usage in practice.