Placebo rates in Crohn's disease randomized clinical trials: An individual patient data meta-analysis.

BACKGROUND & AIMS: Understanding placebo rates is critical for efficient clinical trial design. We assessed placebo rates and associated factors using individual patient data (IPD) from Crohn's disease (CD) trials. METHODS: We conducted a meta-analysis of phase 2/3 placebo-controlled trials evaluating advanced therapies in moderate-to-severe CD (2010-2021). Deidentified IPD were obtained through Vivli Inc. and Yale University Open Data Access Project. Primary outcomes were clinical response and remission. Pooled placebo rates and 95% confidence intervals (CIs) were estimated using one- and two-stage meta-analytical approaches. Regression analyses identified patient-level factors associated with placebo rates. RESULTS: Using IPD from eight induction (n=1147) and four maintenance (n=524) trials, overall placebo clinical response and remission rates for induction were 27% (95%CI=23-32%) and 10% (95%CI=8-14%) respectively, and 32% (95%CI=23-42%) and 22% (95%CI=14-33%) for maintenance, respectively. Among bio-naïve patients, placebo response and remission rates during induction were 29% (95%CI=24-35%) and 11% (95%CI=8-15%) respectively, and 26% (95% CI=20-33%) and 10% (95% CI=8-14%) for bio-exposed, respectively. During maintenance, bio-naïve response and remission rates were 41% (95%CI=34-48%) and 32% (95%CI=24-40%), respectively, and 29% (95%CI=24-34%) and 16% (95%CI=13-21%) for bio-exposed, respectively. Higher baseline C-reactive protein concentration predicted lower placebo rates, while higher baseline albumin levels and body mass index increased the odds of placebo outcomes. Increased baseline Crohn's Disease Activity Index and 2-item patient-reported outcome scores predicted higher response rates in induction, lower response rates in maintenance, and lower remission rates in induction and maintenance. CONCLUSION: Patient- and trial-level characteristics influence placebo rates in CD trials. Careful implementation of eligibility criteria, outcome definitions, and patient stratification may reduce placebo rates.

A quantitative systems pharmacology workflow toward optimal design and biomarker stratification of atopic dermatitis clinical trials.

BACKGROUND: The development of atopic dermatitis (AD) drugs is challenged by many disease phenotypes and trial design options, which are hard to explore experimentally. OBJECTIVE: We aimed to optimize AD trial design using simulations. METHODS: We constructed a quantitative systems pharmacology model of AD and standard of care (SoC) treatments and generated a phenotypically diverse virtual population whose parameter distribution was derived from known relationships between AD biomarkers and disease severity and calibrated using disease severity evolution under SoC regimens. RESULTS: We applied this workflow to the immunomodulator OM-85, currently being investigated for its potential use in AD, and calibrated the investigational treatment model with the efficacy profile of an existing trial (thereby enriching it with plausible marker levels and dynamics). We assessed the sensitivity of trial outcomes to trial protocol and found that for this particular example the choice of end point is more important than the choice of dosing regimen and patient selection by model-based responder enrichment could increase the expected effect size. A global sensitivity analysis revealed that only a limited subset of baseline biomarkers is needed to predict the drug response of the full virtual population. CONCLUSIONS: This AD quantitative systems pharmacology workflow built around knowledge of marker-severity relationships as well as SoC efficacy can be tailored to specific development cases to optimize several trial protocol parameters and biomarker stratification and therefore has promise to become a powerful model-informed AD drug development and personalized medicine tool.

Clinical trial designs to assess treatment effects on glomerular filtration rate decline.

Glomerular filtration rate (GFR) decline is used as surrogate endpoint for kidney failure. Interventions that reduce chronic kidney disease (CKD) progression often exert acute GFR reductions which differ from their long-term benefits and complicate the estimation of long-term benefit. Here, we assessed the utility of two alternative trial designs (wash-out design and active run-in randomized withdrawal design) that attempt to exclude the impact of acute effects. Post-hoc analyses of two clinical trials that characterized the effect of an intervention with acute reductions in GFR were conducted. The two trials included a wash-out period (EMPA-REG Outcome testing empagliflozin vs placebo) or an active run-in period with a randomized withdrawal (SONAR testing atrasentan vs placebo). We compared the drug effect on GFR decline calculated from the first on-treatment visit to the end of treatment (chronic slope in a standard randomized trial design) with GFR change calculated from randomization to end of wash out, or GFR change from treatment-specific baseline GFR values (GFR at start-of-run-in for placebo and end-of-run-in for atrasentan) until end-of-treatment. The effect of empagliflozin versus placebo on chronic GFR slope was 1.72 (95% confidence interval 1.49-1.94) mL/min/1.73 m2/year, similar to total GFR decline from baseline to the end of wash-out period using a linear mixed model 1.64 (1.44-1.85) mL/min/1.73 m2/year). The effect of atrasentan versus placebo on chronic GFR slope was 0.72 (0.32-1.11) mL/min/1.73 m2/year, similar to total slope from a single slope model when estimated from treatment specific baseline GFR values 0.77 (0.39-1.14) mL/min/1.73 m2/year). Statistical power of the two designs outperformed the standard randomized design. Thus, wash-out and active-run-in randomized-withdrawal trial designs are appropriate models to compute treatment effects on GFR decline.

No Impact of Concomitant Medications on Efficacy and Safety of Biologics and Small Molecules for Ulcerative Colitis.

BACKGROUND AND AIMS: While participants with inflammatory bowel diseases (IBD) in clinical trials of biologics and small molecule drugs (henceforth, advanced therapies) frequently receive several medications concomitantly, it is unclear how they modify treatment effect. METHODS: Through an individual patient data pooled analysis of ten clinical trials of advanced therapies for moderate-to-severe ulcerative colitis (UC), we assessed whether concomitant exposure to corticosteroids, immunomodulators, 5-aminosalicylates, proton pump inhibitors (PPIs), histamine receptor antagonists (H2RA), opiates, antidepressants, and antibiotics modified the effect of the intervention on treatment efficacy and safety outcomes, using modified Poisson regression model. RESULTS: Of 6044 patients (4280 receiving intervention, 1764 receiving placebo), several received concomitant corticosteroids (47%), immunomodulators (28%), 5-aminosalicylates (68%), PPIs (14%), H2RAs (2%), opiates (7%), antidepressants (6%), and/or antibiotics (5%). After adjusting for confounders and examining treatment efficacy of intervention vs. placebo, we observed no impact of concomitant exposure to corticosteroids (ratio of relative risk of drug vs. placebo with vs. without concomitant exposure: RRR, 0.81 [95% CI,0.63-1.06], 5-aminosalicylates (RRR, 1.04[0.78-1.39]), PPIs (RRR, 0.87 [0.61-1.22]), H2RAs (RRR, 1.72[0.97-14.29]), opiates (RRR, 0.90[0.54-1.49]), antidepressants (RRR, 1.02[0.57-1.83]), and antibiotics (RRR, 0.72[0.44-1.16]) on likelihood of clinical remission. Concomitant exposure to immunomodulators was associated with lower likelihood of achieving clinical remission (RRR, 0.73[0.55-0.97]), particularly with non-TNF antagonists. CONCLUSIONS: In clinical trials of advanced therapies for UC, baseline concomitant exposure to multiple commonly used class of medications does not impact treatment efficacy or safety. These findings directly inform design of regulatory clinical trials with respect to managing concomitant medications at baseline.

Enzyme-mediated drug-drug interactions: a review of in vivo and in vitro methodologies, regulatory guidance, and translation to the clinic.

Enzyme-mediated pharmacokinetic drug-drug interactions can be caused by altered activity of drug metabolizing enzymes in the presence of a perpetrator drug, mostly via inhibition or induction. We identified a gap in the literature for a state-of-the art detailed overview assessing this type of DDI risk in the context of drug development. This manuscript discusses in vitro and in vivo methodologies employed during the drug discovery and development process to predict clinical enzyme-mediated DDIs, including the determination of clearance pathways, metabolic enzyme contribution, and the mechanisms and kinetics of enzyme inhibition and induction. We discuss regulatory guidance and highlight the utility of in silico physiologically-based pharmacokinetic modeling, an approach that continues to gain application and traction in support of regulatory filings. Looking to the future, we consider DDI risk assessment for targeted protein degraders, an emerging small molecule modality, which does not have recommended guidelines for DDI evaluation. Our goal in writing this report was to provide early-career researchers with a comprehensive view of the enzyme-mediated pharmacokinetic DDI landscape to aid their drug development efforts.

The Role of AI in Drug Discovery.

The emergence of Artificial Intelligence (AI) in drug discovery marks a pivotal shift in pharmaceutical research, blending sophisticated computational techniques with conventional scientific exploration to break through enduring obstacles. This review paper elucidates the multifaceted applications of AI across various stages of drug development, highlighting significant advancements and methodologies. It delves into AI's instrumental role in drug design, polypharmacology, chemical synthesis, drug repurposing, and the prediction of drug properties such as toxicity, bioactivity, and physicochemical characteristics. Despite AI's promising advancements, the paper also addresses the challenges and limitations encountered in the field, including data quality, generalizability, computational demands, and ethical considerations. By offering a comprehensive overview of AI's role in drug discovery, this paper underscores the technology's potential to significantly enhance drug development, while also acknowledging the hurdles that must be overcome to fully realize its benefits.

Patient autonomy and shared decision-making in the context of clinical trial participation.

AIMS: This study aimed to explore how incorporating shared decision-making (SDM) can address recruitment challenges in clinical trials. Specifically, it examines how SDM can align the trial process with patient preferences, enhance patient autonomy and increase active patient participation. Additionally, it identifies potential conflicts between SDM and certain clinical trial aspects, such as randomization or blinding, and proposes solutions to mitigate these issues. MATERIALS AND METHODS: We conducted a comprehensive review of existing literature on patient recruitment challenges in clinical trials and the role of SDM in addressing these challenges. We analysed case studies and trial reports to identify common obstacles and assess the effectiveness of SDM in improving patient accrual. Additionally, we evaluated three proposed solutions: adequate trial design, communication skill training and patient decision aids. RESULTS: Our review indicates that incorporating SDM can significantly enhance patient recruitment by promoting patient autonomy and engagement. SDM encourages physicians to adopt a more open and informative approach, which aligns the trial process with patient preferences and reduces psychological barriers such as fear and mental stress. However, implementing SDM can conflict with elements such as randomization and blinding, potentially complicating trial design and execution. DISCUSSION: The desire for patient autonomy and active engagement through SDM may clash with traditional clinical trial methodologies. To address these conflicts, we propose three solutions: redesigning trials to better accommodate SDM principles, providing communication skill training for physicians and developing patient decision aids. By focussing on patient wishes and emotions, these solutions can integrate SDM into clinical trials effectively. CONCLUSION: Shared decision-making provides a framework that can promote patient recruitment and trial participation by enhancing patient autonomy and engagement. With proper implementation of trial design modifications, communication skill training and patient decision aids, SDM can support rather than hinder clinical trial execution, ultimately contributing to the advancement of evidence-based medicine.

Hot topics in allergen immunotherapy, 2023: Current status and future perspective.

The importance of allergen immunotherapy (AIT) is multifaceted, encompassing both clinical and quality-of-life improvements and cost-effectiveness in the long term. Key mechanisms of allergen tolerance induced by AIT include changes in memory type allergen-specific T- and B-cell responses towards a regulatory phenotype with decreased Type 2 responses, suppression of allergen-specific IgE and increased IgG1 and IgG4, decreased mast cell and eosinophil numbers in allergic tissues and increased activation thresholds. The potential of novel patient enrolment strategies for AIT is taking into account recent advances in biomarkers discoveries, molecular allergy diagnostics and mobile health applications contributing to a personalized approach enhancement that can increase AIT efficacy and compliance. Artificial intelligence can help manage and interpret complex and heterogeneous data, including big data from omics and non-omics research, potentially predict disease subtypes, identify biomarkers and monitor patient responses to AIT. Novel AIT preparations, such as synthetic compounds, innovative carrier systems and adjuvants, are also of great promise. Advances in clinical trial models, including adaptive, complex and hybrid designs as well as real-world evidence, allow more flexibility and cost reduction. The analyses of AIT cost-effectiveness show a clear long-term advantage compared to pharmacotherapy. Important research questions, such as defining clinical endpoints, biomarkers of patient selection and efficacy, mechanisms and the modulation of the placebo effect and alternatives to conventional field trials, including allergen exposure chamber studies are still to be elucidated. This review demonstrates that AIT is still in its growth phase and shows immense development prospects.

Similarities and Differences Between Pragmatic Trials and Hybrid Effectiveness-Implementation Trials.

Pragmatism in clinical trials is focused on increasing the generalizability of research findings for routine clinical care settings. Hybridism in clinical trials (i.e., assessing both clinical effectiveness and implementation success) is focused on speeding up the process by which evidence-based practices are developed and adopted into routine clinical care. Even though pragmatic trial methodologies and implementation science evolved from very different disciplines, Pragmatic Trials and Hybrid Effectiveness-Implementation Trials share many similar design features. In fact, these types of trials can easily be conflated, creating the potential for investigators to mislabel their trial type or mistakenly use the wrong trial type to answer their research question. Blurred boundaries between trial types can hamper the evaluation of grant applications, the scientific interpretation of findings, and policy-making. Acknowledging that most trials are not pure Pragmatic Trials nor pure Hybrid Effectiveness-Implementation Trials, there are key differences in these trial types and they answer very different research questions. The purpose of this paper is to clarify the similarities and differences of these trial types for funders, researchers, and policy-makers. In addition, recommendations are offered to help investigators choose, label, and operationalize the most appropriate trial type to answer their research question. These recommendations complement existing reporting guidelines for clinical effectiveness trials (TIDieR) and implementation trials (StaRI).

Time to prerandomization seizure count design sufficiently assessed the safety and tolerability of perampanel for the treatment of primary generalized tonic-clonic seizures.

OBJECTIVE: Static assignment of participants in randomized clinical trials to placebo or ineffective treatment confers risk from continued seizures. An alternative trial design of time to exceed prerandomization monthly seizure count (T-PSC) has replicated the efficacy conclusions of traditionally designed trials, with shorter exposure to placebo and ineffective treatment. Trials aim to evaluate efficacy as well as safety and tolerability; therefore, we evaluated whether this T-PSC design also could replicate the trial's safety and tolerability conclusions. METHODS: We retrospectively applied the T-PSC design to analyze treatment-emergent adverse events (TEAEs) from a blinded, placebo-controlled trial of perampanel for primary generalized tonic-clonic seizures (NCT01393743). The safety analysis set consisted of 81 and 82 participants randomized to perampanel and placebo arms, respectively. We evaluated the incidences of TEAEs, treatment-related TEAEs, serious TEAEs, and TEAEs of special interest that occurred before T-PSC relative to those observed during the full-length trial. RESULTS: Of the 67 and 59 participants who experienced TEAEs in the perampanel and placebo arms during full-length trial, 66 (99%) and 54 (92%) participants experienced TEAEs with onset occurring before T-PSC, respectively. When limited to treatment-related TEAEs, 55 of 56 (98%) and 32 of 37 (86%) participants reported treatment-related TEAEs that occurred before T-PSC in the perampanel and placebo arms, respectively. There were more TEAEs after T-PSC with placebo as compared to perampanel (Fisher exact odds ratio = 8.6, p = .035), which resulted in overestimation of the difference in TEAE rate. There was a numerical reduction in serious TEAEs (3/13 occurred after T-PSC, one in placebo and two in perampanel). SIGNIFICANCE: Almost all TEAEs occurred before T-PSC. More treatment-related TEAEs occurred after T-PSC for participants randomized to placebo than perampanel, which may be due to either a shorter T-PSC or delayed time to TEAE for placebo.

Categorization of the amyotrophic lateral sclerosis population via the clinical determinant of post-onset ΔFS for study design and medical practice.

The amyotrophic lateral sclerosis (ALS) functional rating scale-revised (ALSFRS-R) has become the most widely utilized measure of disease severity in patients with ALS, with change in ALSFRS-R from baseline being a trusted primary outcome measure in ALS clinical trials. This is despite the scale having several established limitations, and although alternative scales have been proposed, it is unlikely that these will displace ALSFRS-R in the foreseeable future. Here, we discuss the merits of delta FS (ΔFS), the slope or rate of ALSFRS-R decline over time, as a relevant tool for innovative ALS study design, with an as yet untapped potential for optimization of drug effectiveness and patient management. In our view, categorization of the ALS population via the clinical determinant of post-onset ΔFS is an important study design consideration. It serves not only as a critical stratification factor and basis for patient enrichment but also as a tool to explore differences in treatment response across the overall population; thereby, facilitating identification of responder subgroups. Moreover, because post-onset ΔFS is derived from information routinely collected as part of standard patient care and monitoring, it provides a suitable patient selection tool for treating physicians. Overall, post-onset ΔFS is a very attractive enrichment tool that is, can and should be regularly incorporated into ALS trial design.

Statistical design and analysis of controlled human malaria infection trials.

BACKGROUND: Malaria is a potentially life-threatening disease caused by Plasmodium protozoa transmitted by infected Anopheles mosquitoes. Controlled human malaria infection (CHMI) trials are used to assess the efficacy of interventions for malaria elimination. The operating characteristics of statistical methods for assessing the ability of interventions to protect individuals from malaria is uncertain in small CHMI studies. This paper presents simulation studies comparing the performance of a variety of statistical methods for assessing efficacy of intervention in CHMI trials. METHODS: Two types of CHMI designs were investigated: the commonly used single high-dose design (SHD) and the repeated low-dose design (RLD), motivated by simian immunodeficiency virus (SIV) challenge studies. In the context of SHD, the primary efficacy endpoint is typically time to infection. Using a continuous time survival model, five statistical tests for assessing the extent to which an intervention confers partial or full protection under single dose CHMI designs were evaluated. For RLD, the primary efficacy endpoint is typically the binary infection status after a specific number of challenges. A discrete time survival model was used to study the characteristics of RLD versus SHD challenge studies. RESULTS: In a SHD study with the continuous time survival model, log-rank test and t-test are the most powerful and provide more interpretable results than Wilcoxon rank-sum tests and Lachenbruch tests, while the likelihood ratio test is uniformly most powerful but requires knowledge of the underlying probability model. In the discrete time survival model setting, SHDs are more powerful for assessing the efficacy of an intervention to prevent infection than RLDs. However, additional information can be inferred from RLD challenge designs, particularly using a likelihood ratio test. CONCLUSIONS: Different statistical methods can be used to analyze controlled human malaria infection (CHMI) experiments, and the choice of method depends on the specific characteristics of the experiment, such as the sample size allocation between the control and intervention groups, and the nature of the intervention. The simulation results provide guidance for the trade off in statistical power when choosing between different statistical methods and study designs.

Considerations for first field trials of low-threshold gene drive for malaria vector control.

Sustainable reductions in African malaria transmission require innovative tools for mosquito control. One proposal involves the use of low-threshold gene drive in Anopheles vector species, where a 'causal pathway' would be initiated by (i) the release of a gene drive system in target mosquito vector species, leading to (ii) its transmission to subsequent generations, (iii) its increase in frequency and spread in target mosquito populations, (iv) its simultaneous propagation of a linked genetic trait aimed at reducing vectorial capacity for Plasmodium, and (v) reduced vectorial capacity for parasites in target mosquito populations as the gene drive system reaches fixation in target mosquito populations, causing (vi) decreased malaria incidence and prevalence. Here the scope, objectives, trial design elements, and approaches to monitoring for initial field releases of such gene dive systems are considered, informed by the successful implementation of field trials of biological control agents, as well as other vector control tools, including insecticides, Wolbachia, larvicides, and attractive-toxic sugar bait systems. Specific research questions to be addressed in initial gene drive field trials are identified, and adaptive trial design is explored as a potentially constructive and flexible approach to facilitate testing of the causal pathway. A fundamental question for decision-makers for the first field trials will be whether there should be a selective focus on earlier points of the pathway, such as genetic efficacy via measurement of the increase in frequency and spread of the gene drive system in target populations, or on wider interrogation of the entire pathway including entomological and epidemiological efficacy. How and when epidemiological efficacy will eventually be assessed will be an essential consideration before decisions on any field trial protocols are finalized and implemented, regardless of whether initial field trials focus exclusively on the measurement of genetic efficacy, or on broader aspects of the causal pathway. Statistical and modelling tools are currently under active development and will inform such decisions on initial trial design, locations, and endpoints. Collectively, the considerations here advance the realization of developer ambitions for the first field trials of low-threshold gene drive for malaria vector control within the next 5 years.

Determining sample size in a personalized randomized controlled (PRACTical) trial.

In clinical settings with no commonly accepted standard-of-care, multiple treatment regimens are potentially useful, but some treatments may not be appropriate for some patients. A personalized randomized controlled trial (PRACTical) design has been proposed for this setting. For a network of treatments, each patient is randomized only among treatments which are appropriate for them. The aim is to produce treatment rankings that can inform clinical decisions about treatment choices for individual patients. Here we propose methods for determining sample size in a PRACTical design, since standard power-based methods are not applicable. We derive a sample size by evaluating information gained from trials of varying sizes. For a binary outcome, we quantify how many adverse outcomes would be prevented by choosing the top-ranked treatment for each patient based on trial results rather than choosing a random treatment from the appropriate personalized randomization list. In simulations, we evaluate three performance measures: mean reduction in adverse outcomes using sample information, proportion of simulated patients for whom the top-ranked treatment performed as well or almost as well as the best appropriate treatment, and proportion of simulated trials in which the top-ranked treatment performed better than a randomly chosen treatment. We apply the methods to a trial evaluating eight different combination antibiotic regimens for neonatal sepsis (NeoSep1), in which a PRACTical design addresses varying patterns of antibiotic choice based on disease characteristics and resistance. Our proposed approach produces results that are more relevant to complex decision making by clinicians and policy makers.

Bayesian response adaptive randomization design with a composite endpoint of mortality and morbidity.

Allocating patients to treatment arms during a trial based on the observed responses accumulated up to the decision point, and sequential adaptation of this allocation, could minimize the expected number of failures or maximize total benefits to patients. In this study, we developed a Bayesian response-adaptive randomization (RAR) design targeting the endpoint of organ support-free days (OSFD) for patients admitted to the intensive care units. The OSFD is a mixture of mortality and morbidity assessed by the number of days of free of organ support within a predetermined post-randomization time-window. In the past, researchers treated OSFD as an ordinal outcome variable where the lowest category is death. We propose a novel RAR design for a composite endpoint of mortality and morbidity, for example, OSFD, by using a Bayesian mixture model with a Markov chain Monte Carlo sampling to estimate the posterior probability distribution of OSFD and determine treatment allocation ratios at each interim. Simulations were conducted to compare the performance of our proposed design under various randomization rules and different alpha spending functions. The results show that our RAR design using Bayesian inference allocated more patients to the better performing arm(s) compared to other existing adaptive rules while assuring adequate power and type I error rate control across a range of plausible clinical scenarios.

Longitudinal course of neurofilament light chain levels in amyotrophic lateral sclerosis-insights from a completed randomized controlled trial with rasagiline.

BACKGROUND AND PURPOSE: Rasagiline might be disease modifying in patients with amyotrophic lateral sclerosis (ALS). The aim was to evaluate the effect of rasagiline 2 mg/day on neurofilament light chain (NfL), a prognostic biomarker in ALS. METHODS: In 65 patients with ALS randomized in a 3:1 ratio to rasagiline 2 mg/day (n = 48) or placebo (n = 17) in a completed randomized controlled multicentre trial, NfL levels in plasma were measured at baseline, month 6 and month 12. Longitudinal changes in NfL levels were evaluated regarding treatment and clinical parameters. RESULTS: Baseline NfL levels did not differ between the study arms and correlated with disease progression rates both pre-baseline (r = 0.64, p < 0.001) and during the study (r = 0.61, p < 0.001). NfL measured at months 6 and 12 did not change significantly from baseline in both arms, with a median individual NfL change of +1.4 pg/mL (interquartile range [IQR] -5.6, 14.2) across all follow-up time points. However, a significant difference in NfL change at month 12 was observed between patients with high and low NfL baseline levels treated with rasagiline (high [n = 13], -6.9 pg/mL, IQR -20.4, 6.0; low [n = 18], +5.9 pg/mL, IQR -1.4, 19.7; p = 0.025). Additionally, generally higher longitudinal NfL variability was observed in patients with high baseline levels, whereas disease progression rates and disease duration at baseline had no impact on the longitudinal NfL course. CONCLUSION: Post hoc NfL measurements in completed clinical trials are helpful in interpreting NfL data from ongoing and future interventional trials and could provide hypothesis-generating complementary insights. Further studies are warranted to ultimately differentiate NfL response to treatment from other factors.

A Tutorial on Value-Based Adaptive Designs: Could a Value-Based Sequential 2-Arm Design Have Created More Health Economic Value for the Big CACTUS Trial?

OBJECTIVES: Value-based trials aim to maximize the expected net benefit by balancing technology adoption decisions and clinical trial costs. Adaptive trials offer additional efficiency. This article provides guidance on determining whether a value-based sequential design is the best option for an adaptive 2-arm trial, illustrated through a case study. METHODS: We outlined 4 steps for the value-based sequential approach. The case study re-evaluates the Big CACTUS trial design using pilot trial data and a model-based health economic analysis. Expected net benefit is computed for (1) original fixed design, (2) value-based design with fixed sample size, and (3) optimal value-based sequential design with adaptive stopping. We compare pretrial modeling with the actual Big CACTUS trial results. RESULTS: Over 10 years, the adoption decision would affect approximately 215 378 patients. Pretrial modeling shows that the expected net benefit minus costs are (1) £102 million for the original fixed design, (2) £107 million (+5.3% higher) for the value-based design with optimal fixed sample size, and (3) £109 million (+6.7% higher) for the optimal value-based sequential design with maximum sample size of 435 per arm. Post hoc analysis using actual Big CACTUS trial data indicates that the value-adaptive trial with a maximum sample size of 95 participant pairs would not have stopped early. Bootstrap simulations reveal a 9.76% probability of early completion with n = 95 pairs compared with 31.50% with n = 435 pairs. CONCLUSIONS: The 4-step approach to value-based sequential 2-arm design with adaptive stopping was successfully implemented. Further application of value-based adaptive approaches could be useful to assess the efficiency of alternative study designs.

Distributive randomization: a pragmatic fractional factorial design to screen or evaluate multiple simultaneous interventions in a clinical trial.

BACKGROUND: In some medical indications, numerous interventions have a weak presumption of efficacy, but a good track record or presumption of safety. This makes it feasible to evaluate them simultaneously. This study evaluates a pragmatic fractional factorial trial design that randomly allocates a pre-specified number of interventions to each participant, and statistically tests main intervention effects. We compare it to factorial trials, parallel-arm trials and multiple head-to-head trials, and derive some good practices for its design and analysis. METHODS: We simulated various scenarios involving 4 to 20 candidate interventions among which 2 to 8 could be simultaneously allocated. A binary outcome was assumed. One or two interventions were assumed effective, with various interactions (positive, negative, none). Efficient combinatorics algorithms were created. Sample sizes and power were obtained by simulations in which the statistical test was either difference of proportions or multivariate logistic regression Wald test with or without interaction terms for adjustment, with Bonferroni multiplicity-adjusted alpha risk for both. Native R code is provided without need for compiling or packages. RESULTS: Distributive trials reduce sample sizes 2- to sevenfold compared to parallel arm trials, and increase them 1- to twofold compared to factorial trials, mostly when fewer allocations than for the factorial design are possible. An unexpectedly effective intervention causes small decreases in power (< 10%) if its effect is additive, but large decreases (possibly down to 0) if not, as for factorial designs. These large decreases are prevented by using interaction terms to adjust the analysis, but these additional estimands have a sample size cost and are better pre-specified. The issue can also be managed by adding a true control arm without any intervention. CONCLUSION: Distributive randomization is a viable design for mass parallel evaluation of interventions in constrained trial populations. It should be introduced first in clinical settings where many undercharacterized interventions are potentially available, such as disease prevention strategies, digital behavioral interventions, dietary supplements for chronic conditions, or emerging diseases. Pre-trial simulations are recommended, for which tools are provided.

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.

Innovative approaches for vaccine trials as a key component of pandemic preparedness - a white paper.

BACKGROUND: WHO postulates the application of adaptive design features in the global clinical trial ecosystem. However, the adaptive platform trial (APT) methodology has not been widely adopted in clinical research on vaccines. METHODS: The VACCELERATE Consortium organized a two-day workshop to discuss the applicability of APT methodology in vaccine trials under non-pandemic as well as pandemic conditions. Core aspects of the discussions are summarized in this article. RESULTS: An "ever-warm" APT appears ideally suited to improve efficiency and speed of vaccine research. Continuous learning based on accumulating APT trial data allows for pre-planned adaptations during its course. Given the relative design complexity, alignment of all stakeholders at all stages of an APT is central. Vaccine trial modelling is crucial, both before and in a pandemic emergency. Various inferential paradigms are possible (frequentist, likelihood, or Bayesian). The focus in the interpandemic interval may be on research gaps left by industry trials. For activation in emergency, template Disease X protocols of syndromal design for pathogens yet unknown need to be stockpiled and updated regularly. Governance of a vaccine APT should be fully integrated into supranational pandemic response mechanisms. DISCUSSION: A broad range of adaptive features can be applied in platform trials on vaccines. Faster knowledge generation comes with increased complexity of trial design. Design complexity should not preclude simple execution at trial sites. Continuously generated evidence represents a return on investment that will garner societal support for sustainable funding. Adaptive design features will naturally find their way into platform trials on vaccines.