Multi-arm Trials Research Articles

Sexual function after treatment with non-invasive radiofrequency device for improvement of the genitourinary syndrome of menopause: A multi-arm randomized clinical trial.

Several anatomical and functional changes occur during menopause and lead to female sexual dysfunction (FSD). The use of energy-based devices to improve women's sexual health brings an innovative scenario. To evaluate the effect of non-invasive radiofrequency (RF) treatment compared to vaginal estrogen therapy (E) and vaginal moisturizer (M) in postmenopausal women with FSD. Thirty-two sexually active postmenopausal women aged 45-75years were enrolled in a single center randomized controlled trial with three intervention arms: non-invasive RF, vaginal estrogen (E), or vaginal moisturizer (M) treatment. Assessments at baseline, and 4months were conducted using the Female Sexual Function Index (FSFI). The primary outcome of this RCT was an assessment of the effect of RF on FSD compared to that of E and M. According to the total FSFI score, 100% of participants in the RF and E arms and 90% in the M arm had sexual problems at the baseline. The mean age of the participants was 58+/-5.3, 57.9+/-6.3, and 59.6+/-6.0years in the RF, E, and M arms, respectively (p=0.741). After 4months of follow-up, FSD had ameliorated by 146.1% in the RF arm (improvement of 17.32 points in the total FSFI score), with no significant improvement in the other arms (p=0.009). We observed improvements in sexual desire (1.32 points in the partial FSFI score), arousal (2.37 points in the partial FSFI score), and orgasm (2.8 points in the partial FSFI score) only in the RF arm (p=0.004, p<0.001, and p<0.001, respectively). The use of an energy-based device independently of hormonal therapy to improve female SF is very promising. Our findings may contribute to treatment decisions when there is failure of vaginal estrogen therapy, a need for a combination of treatments, or a patient preference for the use of energy-based devices, in postmenopausal women with FSD. Non-invasive RF treatment for FSD showed superior efficacy compared to vaginal estrogen therapy and vaginal moisturizer after 4months of follow-up. Further studies with longer follow-up periods are needed to corroborate these findings and evaluate the long-term effects of non-invasive RF therapy on sexual function.

BayCAR: a Bayesian-based covariate-adaptive randomization method for multi-arm trials

BayCAR: a Bayesian-based covariate-adaptive randomization method for multi-arm trials

Effect of a Feedback Visit and a Clinical Decision Support System Based on Antibiotic Prescription Audit in Primary Care: Multiarm Cluster-Randomized Controlled Trial.

While numerous antimicrobial stewardship programs aim to decrease inappropriate antibiotic prescriptions, evidence of their positive impact is needed to optimize future interventions. This study aimed to evaluate 2 multifaceted antibiotic stewardship interventions for inappropriate systemic antibiotic prescription in primary care. An open-label, cluster-randomized controlled trial of 2501 general practitioners (GPs) working in western France was conducted from July 2019 to January 2021. Two interventions were studied: the standard intervention, consisting of a visit by a health insurance representative who gave prescription feedback and provided a leaflet for treating cystitis and tonsillitis; and a clinical decision support system (CDSS)-based intervention, consisting of a visit with prescription feedback and a CDSS demonstration on antibiotic prescribing. The control group received no intervention. Data on systemic antibiotic dispensing was obtained from the National Health Insurance System (Système National d'Information Inter-Régimes de l'Assurance Maladie) database. The overall antibiotic volume dispensed per GP at 12 months was compared between arms using a 2-level hierarchical analysis of covariance adjusted for annual antibiotic prescription volume at baseline. Overall, 2501 GPs were randomized (n=1099, 43.9% women). At 12 months, the mean volume of systemic antibiotics per GP decreased by 219.2 (SD 61.4; 95% CI -339.5 to -98.8; P<.001) defined daily doses in the CDSS-based visit group compared with the control group. The decrease in the mean volume of systemic antibiotics dispensed per GP was not significantly different between the standard visit group and the control group (-109.7, SD 62.4; 95% CI -232.0 to 12.5 defined daily doses; P=.08). A visit by a health insurance representative combining feedback and a CDSS demonstration resulted in a 4.4% (-219.2/4930) reduction in the total volume of systemic antibiotic prescriptions in 12 months. ClinicalTrials.gov NCT04028830; https://clinicaltrials.gov/study/NCT04028830.

Vascular risk factors are associated with grey matter atrophy in secondary progressive multiple sclerosis.

Comorbidities including vascular risk factors can be associated with whole and regional brain atrophy in multiple sclerosis (MS). This has been examined in mixed MS cohorts in prospective or observational studies; however, the association between vascular comorbidities (VCM) in secondary progressive MS (SPMS) and brain atrophy has been less well studied. The aim was to investigate the cross-sectional and longitudinal association between VCM, comorbidity burden and brain atrophy in SPMS. Post hoc analysis of 445 participants from the MS-Secondary Progressive multi-arm trial (MS-SMART)-a multi-arm multicentre phase-2b randomised placebo-controlled trial of three agents in SPMS (NCT01910259). VCM (hypertension, hyperlipidaemia) but also asthma, hypothyroidism and osteoporosis were recorded. Regional and whole brain volume (WBV), and percentage brain volume change were calculated using SIENAX and SIENA, respectively. Multiple linear regression was used to investigate the cross-sectional and longitudinal relationships between VCM, overall comorbidity count and whole brain, grey matter (GM) and white matter (WM) atrophy. The cohort was predominantly female (67%), mean age 55 with median EDSS 6.0. In total, 13% and 9% had hypertension and hyperlipidaemia, respectively. In cross-sectional regression models, VCM was associated with decreased cortical GM volume [(hypertension β = -0.30, 95%CI -0.54 to -0.06, p = 0.01) (hyperlipidaemia β = -0.37, 95%CI -0.64 to -0.09, p = 0.008)]; but not WBV. Having ≥2 comorbidities was also associated with decreased cortical GM volume (β = -0.36, 95%CI -0.61 to -0.10, p = 0.007). No relationship was observed between VCM/comorbidity count and whole brain or GM atrophy rate over 96 weeks. People with SPMS with VCM or increased overall comorbidity burden showed reduced whole brain and especially cortical grey matter volumes, but no significant impact on subsequent 2-year atrophy rate was detected.

Training and validating a treatment recommender with partial verification evidence.

Current clinical decision support systems (DSS) are trained and validated on observational data from the clinic in which the DSS is going to be applied. This is problematic for treatments that have already been validated in a randomized clinical trial (RCT), but have not yet been introduced in any clinic. In this work, we report on a method for training and validating the DSS core before introduction to a clinic, using the RCT data themselves. The key challenges we address are of missingness, foremost: missing rationale when assigning a treatment to a patient (the assignment is at random), and missing verification evidence, since the effectiveness of a treatment for a patient can only be verified (ground truth) if the treatment was indeed assigned to the patient - but then the assignment was at random. We use the data of a multi-armed clinical trial that investigated the effectiveness of single treatments and combination treatments for 240+ tinnitus patients recruited and treated in 5 clinical centres. To deal with the 'missing rationale for treatment assignment' challenge, we re-model the target variable that measures the outcome of interest, in order to suppress the effect of the individual treatment, which was at random, and control on the effect of treatment in general. To deal with missing features for many patients, we use a learning core that is robust to missing features. Further, we build ensembles that parsimoniously exploit the small patient numbers we have for learning. To deal with the 'missing verification evidence' challenge, we introduce counterfactual treatment verification, a verification scheme that juxtaposes the effectiveness of the recommendations of our approach to the effectiveness of the RCT assignments in the cases of agreement/disagreement between the two. We demonstrate that our approach leverages the RCT data for learning and verification, by showing that the DSS suggests treatments that improve the outcome. The results are limited through the small number of patients per treatment; while our ensemble is designed to mitigate this effect, the predictive performance of the methods is affected by the smallness of the data. We provide a basis for the establishment of decision supporting routines on treatments that have been tested in RCTs but have not yet been deployed clinically. Practitioners can use our approach to train and validate a DSS on new treatments by simply using the RCT data available to them. More work is needed to strengthen the robustness of the predictors. Since there are no further data available to this purpose, but those already used, the potential of synthetic data generation seems an appropriate alternative.

Optimizing Neoadjuvant Treatment Response Prediction for Triple-Negative Breast Cancer Using Clinical Trial Data and Deep Auxiliary Learning.

Triple-negative breast cancer (TNBC) is an aggressive subtype of breast cancer with limited treatment options and poor prognosis. Developing predictive models for TNBC treatment responses is crucial but challenging due to data scarcity and the reliance on cell line data, which limits clinical translational value. Leveraging omics data from clinical trials, particularly through auxiliary learning, offers a potential solution to enhance predictive accuracy and reduce data requirements. In this study, we propose a new approach utilizing deep auxiliary task reweighting learning methods to automatically reweight auxiliary tasks, thereby optimizing the performance of the primary task of predicting TNBC treatment responses. We benchmark various auxiliary learning methods, including ARML, AdaLoss, GradNorm, and OL AUX, against traditional supervised machine learning algorithms and single-task learning baselines. Our results characterize the performance of auxiliary learning across various contexts, including utilizing parallel treatment arms within a multi-arm clinical trial, leveraging treatment arms from different clinical trials, and integrating multiple arms with the same treatment regimens across separate clinical trials. The last scenario also provides an opportunity for validating prediction models on an independent dataset, demonstrating the superior performance of the auxiliary learning models in predicting pathological complete response (pCR) in TNBC patients treated with standardized combinational chemotherapy with Taxane, Anthracycline, and Cyclophosphamide (TAC). Source code and additional resources can be accessed at https://github.com/moonchangin/DeepAux TxPred TNBC.

CTNI-60. SAFETY LEAD-IN RESULTS FOR QBS10072S IN THE INDIVIDUAL SCREENING TRIAL OF INNOVATIVE GLIOBLASTOMA THERAPY (INSIGHT) TRIAL, A RANDOMIZED ADAPTIVE PLATFORM TRIAL FOR NEWLY DIAGNOSED MGMT UNMETHYLATED GLIOBLASTOMA

Abstract BACKGROUND QBS10072S, is a L-type amino acid transporter 1 (LAT-1) targeted alkylating agent that utilizes LAT1 as both a transport and targeting mechanism. QBS10072S can cross the blood-brain barrier, exhibits potential for cytotoxicity with a mechanism distinct from temozolomide and has low off-target effects. QBS10072S is being evaluated in the Individualized Screening Trial of Innovative Glioblastoma Therapy (INSIGhT) trial (NCT02977780), a randomized phase 2 adaptive multi-arm platform trial. METHODS Adult patients with newly diagnosed MGMT-unmethylated glioblastoma are eligible for INSIGhT. Since there is no safety data available for QBS10072S in combination with radiotherapy, a safety lead-in evaluation was conducted utilizing a 3 + 3 design. Starting dose for QBS10072S was 12mg/m2 in combination with radiation therapy and adjuvantly as monotherapy in 28 day cycles. Patients were monitored for dose-limiting toxicities (DLTs) to identify the maximum tolerated dose and recommended phase 2 dose (RP2D). RESULTS Nine patients were enrolled December 2022 to October 2023 for safety lead-in testing, and median age was 57 years (41-67) with 6 (66%) males, 6 (66%) patients with gross total resection, and median KPS 90 (range 80-100). Pharmacokinetic studies indicated that drug exposure was lower than prior studies, and three patients received 12mg/m2, 15mg/m2, and 18mg/m2, respectively, per dose escalation protocol. No DLTs nor adverse events leading to study drug discontinuation were reported during the evaluation period. Four patients had possible treatment-related CTCAE grade ≥ 3 toxicity including lymphopenia (n=3) and fatigue (n=1). The RP2D was determined to be 18mg/m2. Seven patients were discontinued from study treatment due to progressive disease and two patients remain on treatment. CONCLUSION QBS10072S, concurrent and adjuvant to radiation therapy, was well-tolerated in patients with newly diagnosed unmethylated MGMT glioblastoma. QBS10072S 18mg/m2 is being evaluated as a treatment arm in phase 2 testing in the ongoing INSIGhT trial.

Comparative efficacy of PsyPills and OCAT mobile psychological interventions in reducing depressive, anxiety and stress symptoms: A blinded randomized clinical trial

BackgroundDigital applications, such as in smartphone apps format, have shown high suggestive evidence for their efficacy in reducing general distress, but rigorous studies of their efficacy in symptom change and the mechanisms involved are still needed. MethodsIn the current multi-arm parallel-group randomized trial, participants aged 18–65 with smartphone access were recruited through social media. They were randomly assigned to two app interventions (PsyPills and OCAT) or an active placebo group (shamOCAT). The primary outcome was psychological distress measured up to one month. ResultsA total of 229 participants from diverse regional and demographic groups of the general population of Romania were randomly allocated into the three groups (PsyPills n = 80; OCAT n = 70; shamOCAT n = 79) and included in intention-to-treat analyses. Both the PsyPills (MD = −522; 95%CI = −10.00 to −0.44; d = 0.48) and OCAT (MD = −6.30; 95%CI = −11.39 to −1.21; d = 0.58) reduced significantly, with medium effect sizes, the psychological distress levels compared with the control group at follow-up. For the separate outcomes, only PsyPills showed significant medium reduction effects for anxiety symptoms (MD = −2.17; 95%CI = −3.83 to −0.50; d = −0.60), while OCAT showed reduction effects of small size for depression (MD = −1.50 (95%CI = −3.53 to 0.54, d = −0.34), that was however statistically nonsignificant. LimitationsWe registered high attrition and low adherence rates. Also, lower-than-planned effects might have been statistically underpowered to detect. ConclusionThe results support the high potential of both apps as scalable tools to provide low-intensity self-guided interventions for common psychological problems in the general population and expand opportunities for further research (e.g., confirm and capitulate on the differential effects).

Key considerations for digital decentralised clinical trials from a feasibility study assessing pacing interventions for long COVID

Post COVID-19 condition or long COVID is highly prevalent and often debilitating, with key symptoms including fatigue, breathlessness, and brain fog. There is currently a lack of evidence-based treatments for this highly complex syndrome. There is a need for clinical trial platforms to rapidly evaluate nonpharmacological treatments to support affected individuals with symptom management. We co-produced a mixed methods feasibility study to evaluate a multi-arm digital decentralised clinical trial (DCT) platform to assess non-pharmacological interventions for Long COVID, using pacing interventions as an exemplar. The study demonstrated that the platform was able to successfully e-consent participants, randomise them into one of four intervention arms, capture baseline data, and capture outcomes relevant to a health economic evaluation. The study also highlighted several challenges, including difficulties with recruitment, imposter participants, and high attrition rates. We highlight how these challenges can potentially be mitigated to make a fully powered DCT more feasible.

Multi-arm multi-stage survival trial design with arm-specific stopping rule

ABSTRACT Traditional two-arm randomized trial designs have played a pivotal role in establishing the efficacy of medical interventions. However, their efficiency is often compromised when confronted with multiple experimental treatments or limited resources. In response to these challenges, the multi-arm multi-stage designs have emerged, enabling the simultaneous evaluation of multiple treatments within a single trial. In such an approach, if an arm meets efficacy success criteria at an interim stage, the whole trial stops and the arm is selected for further study. However when multiple treatment arms are active, stopping the trial at the moment one arm achieves success diminishes the probability of selecting the best arm. To address this issue, we have developed a group sequential multi-arm multi-stage survival trial design with an arm-specific stopping rule. The proposed method controls the familywise type I error in a strong sense and selects the best promising treatment arm with a high probability.

Network meta-analysis: Looping back.

This discussion contribution provides some subjective early history of network meta-analysis and also proposes a new bipartite graph structure to better represent multi-arm trials.

Simultaneous Inference Using Multiple Marginal Models.

This tutorial describes single-step low-dimensional simultaneous inference with a focus on the availability of adjusted p values and compatible confidence intervals for more than just the usual mean value comparisons. The basic idea is, first, to use the influence of correlation on the quantile of the multivariate t-distribution: the higher the less conservative. In addition, second, the estimability of the correlation matrix using the multiple marginal models approach (mmm) using multiple models in the class of linear up to generalized linear mixed models. The underlying maxT-test using mmm is discussed by means of several real data scenarios using selected R packages. Surprisingly, different features are highlighted, among them: (i) analyzing different-scaled, correlated, multiple endpoints, (ii) analyzing multiple correlated binary endpoints, (iii) modeling dose as qualitative factor and/or quantitative covariate, (iv) joint consideration of several tuning parameters within the poly-k trend test, (v) joint testing of dose and time, (vi) considering several effect sizes, (vii) joint testing of subgroups and overall population in multiarm randomized clinical trials with correlated primary endpoints, (viii) multiple linear mixed effect models, (ix) generalized estimating equations, and (x) nonlinear regression models.

In-silico trial emulation to predict the cardiovascular protection of new lipid-lowering drugs: an illustration through the design of the SIRIUS programme.

Inclisiran, an siRNA targeting hepatic PCSK9 mRNA, administered twice-yearly (after initial and 3-month doses), substantially and sustainably reduced LDL-cholesterol (LDL-C) in Phase III trials. Whether lowering LDL-C with inclisiran translates into a reduced risk of major adverse cardiovascular events (MACE) is not yet established. In-silico trials applying a disease computational model to virtual patients receiving new treatments allow to emulate large scale long-term clinical trials. The SIRIUS in-silico trial programme aims to predict the efficacy of inclisiran on CV events in individuals with established atherosclerotic cardiovascular disease (ASCVD). A knowledge-based mechanistic model of ASCVD was built, calibrated, and validated to conduct the SIRIUS programme (NCT05974345) aiming to predict the effect of inclisiran on CV outcomes. The SIRIUS Virtual Population included patients with established ASCVD (previous myocardial infarction (MI), previous ischemic stroke (IS), previous symptomatic lower limb peripheral arterial disease (PAD) defined as either intermittent claudication with ankle-brachial index <0.85, prior peripheral arterial revascularization procedure, or vascular amputation) and fasting LDL-C ≥ 70 mg/dL, despite stable (≥4 weeks) well-tolerated lipid-lowering therapies.SIRIUS is an in-silico multi-arm trial programme. It follows an idealized crossover design where each virtual patient is its own control, comparing inclisiran to (i) placebo as adjunct to high-intensity statin therapy with or without ezetimibe, (ii) ezetimibe as adjunct to high-intensity statin therapy, (iii) evolocumab as adjunct to high-intensity statin therapy and ezetimibe.The co-primary efficacy outcomes are based on the time to the first occurrence of any component of 3P-MACE (composite of CV death, nonfatal MI, or nonfatal IS) and time to occurrence of CV death over 5 years. The SIRIUS in-silico trial programme will provide early insights regarding potential effect of inclisiran on MACE in ASCVD patients, several years before the availability of the results from ongoing CV outcomes trials (ORION-4 and VICTORION-2-P). Clinicaltrials.gov identifier: NCT05974345.

A Bayesian adaptive phase III design for multi-arm trials with time-to-event endpoint for nonproportional hazards utilizing the generalized gamma distribution

In case of trials with time-to-event endpoints, sample size calculations are well-studied under the assumption of proportional hazards or when the endpoint of interest follows an exponential distribution. When prior evidence suggests otherwise, using traditional approaches may lead to inefficiently designed and underpowered studies. In such situations, a recently introduced frequentist approach proposes a sample size calculation for a fixed two-arm trial based on the accelerated failure time model thereby allowing nonproportional hazards and can be utilized for any distribution from the generalized gamma family. Advances in the field of clinical trials research have focused on the need for adaptive phase III designs with complex features, however, existing methods in literature have ignored the nonproportional hazards scenario. In this article, we propose a Bayesian adaptive design for a multi-arm phase III trial with nonproportional hazards allowing many complex features such as incorporation of prior knowledge of early phase studies, arms dropping, and response adaptive randomization. We extend the frequentist approach utilizing a generalized gamma distribution to a Bayesian setting while simultaneously addressing one of the key limitations of the frequentist approach. Extensive simulations are performed to study the operation characteristics of the proposed design using two examples representing real-life applications.

Optimal drainage time after evacuation of chronic subdural haematoma (DRAIN TIME 2): a multicentre, randomised, multiarm and multistage non-inferiority trial in Denmark

Postoperative drainage after surgical evacuation of chronic subdural haematoma reduces the risk of recurrence, but the optimum drainage time is uncertain. We aimed to investigate the shortest possible drainage time without increasing the haematoma recurrence rate. We conducted a randomised, multi-arm and multistage non-inferiority trial at four neurosurgical centres in Denmark. We enrolled adult patients (aged ≥18 years) with symptomatic chronic subdural haematoma. All patients were treated according to the national standard practice with a burr hole above the maximum width of the haematoma. Patients were randomly assigned in a 1:1:1 ratio via a centralised web server to receive 6 h, 12 h, or 24 h of postoperative passive subdural drainage. Randomisation was done by an independent on-call neurosurgeon and was masked until 6 h after surgery. The primary outcome was symptomatic haematoma recurrence at 3 months after surgery; the rate of recurrence was assessed in a regression model for non-inferiority testing, with no missing data. Personnel assessing the primary outcome were masked to group allocation. Non-inferiority was assessed with a prespecified margin of 7%, in a modified intention-to-treat population-defined as patients with randomly assigned treatment excluding those withdrawing from study participation after randomisation, or experiencing acute rebleedings or accidental drain removal. This trial is registered with ISRCTN (number 15186366); the trial was stopped after the first interim analysis on the advice of an independent safety advisory committee. Between March 1, 2021, and June 30, 2022, 347 patients were enrolled and 331 were followed up to 3 months, 105 were assigned to 6 h of drainage, 111 to 12 h of drainage, and 115 to 24 h of drainage. At admission, 83 (25%) participants were women and 248 (75%) were men, mean age was 75·7 years (SD 10·5), median modified Rankin Scale score was 4 (IQR 3-5), and median Glasgow Coma Scale score was 15 (IQR 14-15). At 3 months after surgery, haematoma recurrence was reported in 28 (27%) of 105 patients who were assigned to 6 h drainage (predicted haematoma recurrence rate 27·0%, 95% CI 18·5 to 35·4), 22 (20%) of 111 assigned to 12 h drainage (19·5%, 12·0 to 27·0), and 12 (10%) of 115 assigned to 24 h drainage (10·4%, 4·8 to 16·0). The risk of haematoma recurrence was increased by 16·5 percentage points (95% CI 6·5 to 26·6) in patients drained for 6 h compared with 24 h, and by 9·1 percentage points (-0·4 to 18·5) in patients drained for 12 h compared with 24 h. Therefore, non-inferiority of 6 h and 12 h of drainage to 24 h of drainage was not established. 20 patients had died by 3 months, seven in the 6 h group, eight in the 12 h group, and five in the 24 h group. The most frequent known causes of death were haematoma recurrence (three in 12 h group), comorbidity (three in 12 h group), and pneumonia (one each in 6 h and 12 h groups, two in 24 h group). The most frequent complication was postoperative infection, reported in 20 (20%) patients in the 6 h group, 25 (23%) in the 12 h group, and 19 (17%) in the 24 h group. The most common infection source was the urinary tract. Patients surgically treated for symptomatic chronic subdural haematoma and postoperatively drained for 6 h or 12 h had higher rates of haematoma recurrence than did patients drained for 24 h. The findings from this non-inferiority trial provide evidence to support 24 h of postoperative drainage as the standard drain time when a fixed drain time approach is used. To provide solid evidence of generalisability of the results to countries other than Denmark, a multinational randomised controlled trial will be needed. None.

Adding experimental treatment arms to multi-arm multi-stage platform trials in progress.

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.

A randomized controlled trial of Golden Ratio, Feng Shui, and evidence based design in healthcare.

In a global effort to design better hospital buildings for people and organizations, some design principles are still surrounded by great mystery. The aim of this online study was to compare anxiety in an existing single-bed inpatient hospital room with three redesigns of this room in accordance with the principles of Golden Ratio, Feng Shui, and Evidence-Based Design. In this online multi-arm parallel-group randomized trial participants were randomly assigned (1:1:1:1) to one of four conditions, namely Golden Ratio condition, Feng Shui condition, Evidence-Based Design condition, or the control condition. The primary outcomes were anxiety, sense of control, social support, positive distraction, and pleasantness of the room. Between June 24, 2022, and August 22, 2022, 558 individuals were randomly assigned to one of the four conditions, 137 participants to the control condition, 138 participants to the Golden Ratio condition, 140 participants to the Feng Shui condition, and 143 participants to the Evidence-Based Design condition. Compared with baseline, participants assigned to the Evidence-Based Design condition experienced less anxiety (mean difference -1.35, 95% CI -2.15 to -0.55, Cohen's d = 0.40, p < 0.001). Results also showed a significant indirect effect of the Feng Shui condition on anxiety through the pleasantness of the room (B = -0.85, CI = -1.29 to -0.45) and social support (B = -0.33, CI = -0.56 to -0.13). Pleasantness of the room and social support were mediators of change in anxiety in the Evidence-Based Design and Feng Shui conditions. In contrast, application of the design principle Golden Ratio showed no effect on anxiety and remains a myth. To our knowledge, this is the first randomized controlled trial linking design principles directly to anxiety in hospital rooms. The findings of our study suggest that Feng Shui and Evidence-Based Design hospital rooms can mitigate anxiety by creating a pleasant looking hospital room that fosters access to social support. The trial is registered with ISRCTN, ISRCTN10480033.

Maternal and child health intervention to promote behaviour change: a population-level cluster-randomised controlled trial in Honduras

ObjectivesTo assess the efficacy of a sustained educational intervention to affect diverse outcomes across the pregnancy and infancy timeline.SettingA multi-arm cluster-randomised controlled trial in 99 villages in Honduras’ Copán region,...

GsMAMS: an R package for Designing Multi-Arm Multi-Stage Clinical Trials

gsMAMS: an R package for Designing Multi-Arm Multi-Stage Clinical Trials

Bayesian Optimal Designs for Multi-Arm Multi-Stage Phase II Randomized Clinical Trials with Multiple Endpoints

There is a growing need to evaluate of multiple competing drugs in phase II trials where the number of patients is often limited, and simultaneous assessment of both efficacy and toxicity is crucial. To avoid the waste of research resources, it is indeed more efficient to screen multiple drugs at once in a platform phase II setting. We aim to adapt the Bayesian optimal phase II (BOP2) design to multi-arm trials for both uncontrolled and controlled settings. The binary efficacy and toxicity endpoints are modeled by a Dirichlet distribution as a vector of four outcomes. Posterior marginal distributions at each analysis are used to derive the monitoring threshold that varies during the trial. We control the family-wise Type I error rate for multiple comparison against a common reference value or a shared control. We conduct simulation studies under both uncontrolled and controlled settings to evaluate the operating characteristics of the proposed design. Our simulations demonstrate that the design exhibits better operating characteristics compared to a design using a constant threshold and is less sensitive to changes in accrual rate relative to what was planned. The design had promising operating characteristics and could be used in phase II oncology clinical trials for evaluating multiple drugs at a time.