Clinical Trial Simulation Research Articles

Virtual clinical trials identify effective combination therapies in ovarian cancer

A major issue in oncology is the high failure rate of translating preclinical results in successful clinical trials. Using a virtual clinical trial simulations approach, we present a mathematical framework to estimate the added value of combinatorial treatments in ovarian cancer. This approach was applied to identify effective targeted therapies that can be combined with the platinum-taxane regimen and overcome platinum resistance in high-grade serous ovarian cancer. We modeled and evaluated the effectiveness of three drugs that target the main platinum resistance mechanisms, which have shown promising efficacy in vitro, in vivo, and early clinical trials. Our results show that drugs resensitizing chemoresistant cells are superior to those aimed at triggering apoptosis or increasing the bioavailability of platinum. Our results further show that the benefit of using biomarker stratification in clinical trials is dependent on the efficacy of the drug and tumor composition. The mathematical framework presented herein is suitable for systematically testing various drug combinations and clinical trial designs in solid cancers.

Exposure-Response Analyses and Disease Modeling for Selection and Confirmation of Optimal Dosing Regimen of Isatuximab in Combination Treatment in Patients with Multiple Myeloma

Background: Isatuximab (Isa) is an anti-CD38 monoclonal antibody with multiple modes of action for killing tumor cells through direct tumor targeting and immune cell engagement. Exposure-Response (E-R) analysis and disease modelling of tumor burden were performed to evaluate the relationship between Isa exposure and efficacy outcomes. This analysis also supports the Isatuximab dosing regimen selection/confirmation when administered in combination in relapsed/refractory multiple myeloma (RRMM) patients. Methods: To determine the optimal dose for the Phase 3 ICARIA-MM study, E-R analyses were conducted in 44 and 52 RRMM evaluable patients from two Phase 1 trials of Isa in combination with pomalidomide/dexamethasone (Pd) (NCT02283775) or with lenalidomide/dexamethasone (Rd) (NCT01749969), respectively. Isa was administered intravenously at doses from 3 to 20 mg/kg every 2 weeks or weekly for 4 weeks then every 2 weeks (QW/Q2W). In the E-R analyses, several Isa exposure parameters were tested to evaluate if they were predictors of response (partial response [PR] or better). Baseline covariates were also considered in the model to evaluate potential confounding effects. In addition, a second type of analysis was performed on 2 pooled datasets of 153 (single agent and combination with Pd) and 162 (single agent and combination with Rd) evaluable RRMM patients: disease response was also captured by modeling the dynamics of the serum M-protein using a joint model of tumor growth inhibition and drop-out. Trial simulations were then performed to evaluate different dosing regimens of interest. To confirm the dose, E-R analyses were conducted in 297 evaluable RRMM patients from the phase 3 trial (ICARIA-MM; NCT02990338) comparing Isa-Pd (N=148) and Pd (N=149). Isa was administered intravenously at 10 mg/kg QW/Q2W in the Isa-Pd arm. Cox and Weibull (Progression-Free Survival [PFS]) or logistic regression (overall response rate [ORR]) models were used. Results: In the two combination Phase 1 trials, Isa appeared to be well tolerated with no clear dose response relationship between 10 and 20 mg/kg. E-R analyses suggested that higher Isa exposure represented by log Ctrough at 4 weeks (CT4W) was associated with increased ORR. Both models predicted an increased ORR with decreased beta-2 microglobulin and lower number of prior lines of therapy (Rd only), together with higher log CT4W. Clinical trial simulations showed that the probability of success to reach the targeted ORR was high with 10 mg/kg QW/Q2W for both Pd (83% for ORR ≥60%) and Rd (96% for ORR ≥50%). Serum M-protein reduction was greater at doses ≥10 mg/kg compared to doses <10 mg/kg, with minimal differences between 10 and 20 mg/kg QW/Q2W. These analyses supported the rationale for 10 mg/kg QW/Q2W selection for the Phase 3 combination study. In the Phase 3 ICARIA-MM study, the E-R analysis showed that higher CT4W was associated with higher ORR (predicted ORR below and above the median CT4W being 52% and 72%, respectively). The final model also included time since diagnosis and Revised International Staging System (R-ISS) stage at baseline and an interaction between time since diagnosis and R-ISS; ORR increased as time since diagnosis increased and decreased with higher R-ISS stage. For PFS, in addition to CT4W, the model included plasmacytomas, serum albumin level and R-ISS at baseline. The predicted distribution of PFS indicated that patients with greater Isa CT4W had longer PFS, patients with R-ISS Stage III and plasmacytomas had shorter PFS. Matched analyses to Pd indicated that even patients with concentrations ≤median CT4W benefited from Isa treatment (PFS hazard ratio: 0.773). Conclusion: Model-based drug development was successfully applied to support Phase 3 Isa dosing regimen selection for use in combination with Pd in RRMM patients. E-R and clinical data from the Phase 3 ICARIA-MM study confirmed the efficacy and safety of 10 mg/kg QW/Q2W dose/regimen selected. Disclosures Rachedi: Sanofi: Employment. Koiwai:Sanofi: Employment. Gaudel-Dedieu:Sanofi: Employment. Sebastien:Sanofi: Employment. Thai:Sanofi: Employment. El-Cheikh:Sanofi: Employment. Brillac:Sanofi: Employment. Fau:Sanofi: Employment. Nguyen:Sanofi: Employment. Liu:Sanofi: Employment. Campana:Sanofi: Employment. van de Velde:Sanofi: Employment. Semiond:Sanofi: Employment.

Open Systems Pharmacology Community-An Open Access, Open Source, Open Science Approach to Modeling and Simulation in Pharmaceutical Sciences.

Systems pharmacology integrates structural biological and pharmacological knowledge and experimental data, enabling dissection of organism and drug properties and providing excellent predictivity. ...

Impact of Phase 1 study design on estimation of QT interval prolongation risk using exposure-response analysis.

The International Council for Harmonisation (ICH) guidelines have been revised allowing for modeling of concentration-QT (C-QT) data from Phase I dose-escalation studies to be used as primary analysis for QT prolongation risk assessment of new drugs. This work compares three commonly used Phase I dose-escalation study designs regarding their efficiency to accurately identify drug effects on QT interval through C-QT modeling. Parallel group design and 4-period crossover designs with sequential or interleaving cohorts were evaluated. Clinical trial simulations were performed for each design and across different scenarios (e.g. different magnitudes of drug effect, QT variability), assuming a pre-specified linear mixed effect (LME) model for the relationship between drug concentration and change from baseline QT (ΔQT). Analyses suggest no systematic bias in either the predictions of placebo-adjusted ΔQT (ΔΔQT) or the LME model parameter estimates across all evaluated designs. Additionally, false negative rates remained similar and adequately controlled across all evaluated designs. However, compared to the crossover designs, the parallel design had significantly less power to correctly exclude a clinically significant QT effect, especially in the presence of substantial intercept inter-individual variability. In such cases, parallel design is associated with increased uncertainty around ΔΔQT prediction, mainly attributed to the uncertainty around the estimation of the treatment-specific intercept in the model. Throughout all the evaluated scenarios, the crossover design with interleaving cohorts had consistently the best performance characteristics. The results from this investigation will further facilitate informed decision-making during Phase I study design and the interpretation of the associated C-QT modeling output.

Quantification of video-oculography as a biomarker for remyelination – An example from fampridine in internuclear ophthalmoplegia

A recent study showed that video-oculography was sensitive to the effect of fampridine on ocular movement speed and amplitude in internuclear ophthalmoplegia (INO), resulting from improved nerve transduction (Kanhai et al. 2019, doi: https://doi.org/10.1111/cns.13096). This can be used as a biomarker of remyelination for the investigation of new therapeutic compounds in multiple sclerosis (MS). We aimed to quantify the acute effect of fampridine on peak velocity versional dysconjugacy index (PV-VDI) and first peak amplitude (FPA-VDI) of eye movements. Population modelling was used to quantify FPA-VDI and PV-VDI for each eye in 23 patients with INO (10 unilateral, 13 bilateral), aged 31–72 yo. Correlations between eyes were taken into account, and fampridine potency and maximum efficacy were estimated according to a sigmoidal relationship. Fampridine effect on PV-VDI and FPA-VDI in each eye was accurately described and predicted (Fig. 1). Fampridine could maximally improve PV-VDI and FPA-VDI by 32.8% and 30.7% respectively, with 8.43 mg/L the concentration at which 50% of efficacy was reached. Repetitive testing worsened scores by around 0.8% for each additional test. Strong correlation was found for baseline PV-VDI and FPA-VDI between each other and between occasions. All parameters were estimated with good precision (RSE <30%). Baseline characteristics and fampridine effect were quantified for the new remyelination biomarker of video-oculography. Model-based drug development allows more efficient clinical trials using optimal design and clinical trial simulation exercises (Milligan et al. 2013, doi:https://doi.org/10.1038/clpt.2013.54), our model enables this for the investigation of new compounds in diseases involving demyelination.

EP.88Development of clinical trial simulation tool for Duchenne muscular dystrophy through the Duchenne Regulatory Science Consortium

Developing clinical trial protocols that give definitive answers as to whether potential new therapies are effective for rare diseases is challenging due to the small population sizes, limitations on availability of natural history data and limited understanding of disease progression. In Duchenne muscular dystrophy (DMD) this has contributed to few trials meeting primary endpoints, and led to questions as to how best to evaluate efficacy of therapeutic candidates. The Duchenne Regulatory Science Consortium (D-RSC) is a public-private-partnership that aims to develop quantitative tools to accelerate drug development and seeks approval of such tools through regulatory pathways at FDA (Food and Drug Administration) and EMA (European Medicines Authority) to confirm utility and value. D-RSC is developing a multivariate model-based clinical trial simulation (CTS) tool, based on longitudinal models of endpoints that span the course of disease (velocity of completion of supine to stand test, 4-stair climb test, and 10-meter walk/run test, NorthStar Ambulatory Assessment, forced vital capacity and Brooke scale). Models include covariates that predict differences in DMD progression such as use of steroids, height, weight, genetics and baseline function. D-RSC has integrated data from 14 independent studies using CDISC standards, the largest DMD clinical database currently available. The final analysis data set after exclusions, excluding missing observations, includes 1139 individuals with a total of 24210 observations of the endpoints from 4 to 34 years of age. The CTS tool that will be developed based on the disease progression models will help drug developers determine optimal clinical study characteristics, including selection of endpoints, inclusion criteria, trial design, size and duration. This will allow development of trial protocols using the lowest number of patients and in the least time possible to reach conclusive decisions on drug efficacy.

A QSP Model for Predicting Clinical Responses to Monotherapy, Combination and Sequential Therapy Following CTLA-4, PD-1, and PD-L1 Checkpoint Blockade

Over the past decade, several immunotherapies have been approved for the treatment of melanoma. The most prominent of these are the immune checkpoint inhibitors, which are antibodies that block the inhibitory effects on the immune system by checkpoint receptors, such as CTLA-4, PD-1 and PD-L1. Preclinically, blocking these receptors has led to increased activation and proliferation of effector cells following stimulation and antigen recognition, and subsequently, more effective elimination of cancer cells. Translation from preclinical to clinical outcomes in solid tumors has shown the existence of a wide diversity of individual patient responses, linked to several patient-specific parameters. We developed a quantitative systems pharmacology (QSP) model that looks at the mentioned checkpoint blockade therapies administered as mono-, combo- and sequential therapies, to show how different combinations of specific patient parameters defined within physiological ranges distinguish different types of virtual patient responders to these therapies for melanoma. Further validation by fitting and subsequent simulations of virtual clinical trials mimicking actual patient trials demonstrated that the model can capture a wide variety of tumor dynamics that are observed in the clinic and can predict median clinical responses. Our aim here is to present a QSP model for combination immunotherapy specific to melanoma.

Abstract 3176: Biomarker identification using xenograft mouse model based clinical trial simulation and artificial intelligence data analytics

Abstract The growing number of anti-cancer drugs available at different stages of clinical development combined with the broadening potential use of combination therapy further complexifies the early identification of companion markers, markers of synergy as well as novel indications for existing and new drug combinations. Well characterized patient derived xenograft mouse models (PDX), combined with Artificial Intelligence tools that can integrate and analyze the broad range of generated data can help address this challenge. PDX experiments can providing an opportunity to simulate a clinical assessment using multiple mice models.In this study, we developed a PDX platform combined with the KEM® Artificial Intelligence data analytics, that is based on Formal Concept Analysis, to simulate a clinical trial and identify biomarkers of response. The platform was tested on colon cancer patient derived PDX. Respectively mRECIST response and survival of respectively 21 and 26 PDXs against Oxalipaltin combined with 5-Fluorouracil and folinic acid (Folfox) was experimentally assessed against a placebo, simulating a clinical trial-like setting with 2 arms. Biomarkers of response (mRECIST) and survival were identified using KEM®, combined with statistical modelling (Cox survival-modelling). 24 candidate biomarker genes were identified including PGAP3, ERBB2, NOTCH2, WDR70, and ZNF227. Alone or combined, these biomarkers are significantly linked to an increase or decrease of the survival PDX (p ranging from 2.2e-10 to 0.048, odd-ratio ranging from 0.12 to 10.00), with the potential to be used as inclusion or exclusion biomarkers. This work demonstrates the ability of a combined PDX / Artificial Intelligence platform to simulate clinical trials and identify biomarkers of drug efficacy and synergy, thus fostering the design of precision medicine clinical trials. Citation Format: M Afshar, Francis Bichat, Olivier Duchamp, A Etcheto, Damien France, M Kindermans, Caroline Mignard, F Parmentier, Hery Ratsima. Biomarker identification using xenograft mouse model based clinical trial simulation and artificial intelligence data analytics [abstract]. In: Proceedings of the American Association for Cancer Research Annual Meeting 2019; 2019 Mar 29-Apr 3; Atlanta, GA. Philadelphia (PA): AACR; Cancer Res 2019;79(13 Suppl):Abstract nr 3176.

Bayesian approach to investigate a two-statemixed model of COPD exacerbations

Chronic obstructive pulmonary disease (COPD) is a chronic obstructivedisease of the airways. An exacerbation of COPD is defined as shortness of breath,cough, and sputum production. New therapies for COPD exacerbations are examined inclinical trials frequently based on the number of exacerbations that implieslong-term study due to the high variability in occurrence and duration of theevents. In this work, we expanded the two-state model developed by Cook et al. wherethe patient transits from an asymptomatic (state 1) to a symptomatic state (state 2)and vice versa, through investigating different semi-Markov models in a Bayesiancontext using data from actual clinical trials. Of the four models tested, thelog-logistic model was shown to adequately characterize the duration and number ofCOPD exacerbations. The patient disease stage was found a significant covariate withan effect of accelerating the transition from asymptomatic to symptomatic state. Inaddition, the best dropout model (log-logistic) was incorporated in the finaltwo-state model to describe the dropout mechanism. Simulation based diagnostics suchas posterior predictive check (PPC) and visual predictive check (VPC) were used toassess the behaviour of the model. The final model was applied in three clinicaltrial data to investigate its ability to detect the drug effect: the drug effect wascaptured in all three datasets and in both directions (from state 1 to state 2 andvice versa). A practical design investigation was also carried out and showed thelimits of reducing the number of subjects and study length on the drug effectidentification. Finally, clinical trial simulation confirmed that the model canpotentially be used to predict medium term (6–12 months) clinical trial outcomeusing the first 3 months data, but at the expense of showing a non-significant drugeffect.

PF645 EXPOSURE‐RESPONSE ANALYSIS &amp; DISEASE MODELING FOR SELECTION OF OPTIMAL DOSING REGIMEN OF ISATUXIMAB AS SINGLE AGENT IN PATIENTS WITH MULTIPLE MYELOMA

Background:Isatuximab is an anti‐CD38 monoclonal antibody with multiple modes of action for killing tumor cells through direct tumor targeting and immune cell engagement [1].Aims:Exposure‐Response (E‐R) analysis and disease modelling of tumor burden were performed to evaluate the relationship between isatuximab exposure and efficacy outcome and to support dosing regimen selection for isatuximab as a single agent in relapsed/refractory multiple myeloma (RRMM) patients.Methods:The E‐R analyses were conducted in 194 RRMM patients evaluable for pharmacokinetics (PK) from two monotherapy trials, a phase 1/2 trial (NCT01084252)[2] and a phase 1 trial (NCT02514668). Isatuximab was administered intravenously at doses from 1 to 20 mg/kg once a week or every 2 weeks. Several isatuximab exposure parameters were tested and their association with the probability of achieving an objective response (CR, VGPR or PR) was examined by logistic regression modeling. Baseline covariates were also considered in the model to reduce their potential confounding effects. Disease progression was captured in a subset of 122 evaluable patients with the dynamics of the serum M‐protein and accounted for dropout using a joint model. Trial simulations were then performed to evaluate different dosing regimens of interest using both models.Results:The E‐R relationship was best described by Emax model, in which Ctrough at 4 weeks and percent of bone marrow plasma cells were significant predictors of overall response rate (ORR). Patients with bone marrow plasma cells lower than 50% were more likely to respond. For a given bone marrow plasma cell value, higher probability of response to treatment was obtained with higher Ctrough at 4 weeks. Longitudinal data of M‐protein was adequately described by a tumor growth inhibition model [3,4] and provided more insights into the response of patients over time. Therefore, a high loading dose of 20 mg/kg weekly over 4 weeks was chosen for maximizing the tumor response and a maintenance dose of 20 mg/kg every 2 weeks appeared sufficient to sustain efficacy. Clinical trial simulation demonstrated that this dosing regimen presented a probability of success of 76% to reach 30% ORR (5,000 trials with 100 patients) and would allow 52% reduction of serum M‐protein from baseline at 2 months of treatment. In addition, isatuximab appeared to be well tolerated at this dose level.Summary/Conclusion:Model‐based drug development has been successfully applied to support phase II isatuximab monotherapy dosing regimen selection in RRMM patients. This approach increases both the robustness of dose selection decision making and the chances of success for future clinical trials.

Robust designs accounting for model uncertainty in longitudinal studies with binary outcomes.

To optimize designs for longitudinal studies analyzed by mixed-effect models with binary outcomes, the Fisher information matrix can be used. Optimal design approaches, however, require a priori knowledge of the model. We aim to propose, for the first time, a robust design approach accounting for model uncertainty in longitudinal trials with two treatment groups, assuming mixed-effect logistic models. To optimize designs given one model, we compute several optimality criteria based on Fisher information matrix evaluated by the new approach based on Monte-Carlo/Hamiltonian Monte-Carlo. We propose to use the DDS-optimality criterion, as it ensures a compromise between the precision of estimation of the parameters, and hence the Wald test power, and the overall precision of parameter estimation. To account for model uncertainty, we assume candidate models with their respective weights. We compute robust design across these models using compound DDS-optimality. Using the Fisher information matrix, we propose to predict the average power over these models. Evaluating this approach by clinical trial simulations, we show that the robust design is efficient across all models, allowing one to achieve good power of test. The proposed design strategy is a new and relevant approach to design longitudinal studies with binary outcomes, accounting for model uncertainty.

Towards regulatory endorsement of drug development tools to promote the application of model-informed drug development in Duchenne muscular dystrophy.

Drug development for rare diseases is challenged by small populations and limited data. This makes development of clinical trial protocols difficult and contributes to the uncertainty around whether or not a potential therapy is efficacious. The use of data standards to aggregate data from multiple sources, and the use of such integrated databases to develop statistical models can inform protocol development and reduce the risks in developing new therapies. Achieving regulatory endorsement of such models through defined pathways at the US Food and Drug Administration and European Medicines Authority allows such tools to be used by the drug development community for defined contexts of use without further need for discussion of the underlying model(s). The Duchenne Regulatory Science Consortium (D-RSC) has brought together multiple stakeholders to develop a clinical trial simulation tool for Duchenne muscular dystrophy using such an approach. Here we describe the work of D-RSC as an example of how such an approach may be effective at reducing uncertainty in drug development for rare diseases, and thus bringing effective therapies to patients faster.

Use of prior knowledge and extrapolation in paediatric drug development: A case study with deferasirox

The characterisation of pharmacokinetics, pharmacodynamics and dose-exposure-response relationships requires data arising from well-designed study protocols and a relatively large sample from the target patient population. Such a prerequisite is unrealistic for paediatric rare diseases, where the patient population is often vulnerable and very small. In such cases, different sources of data and knowledge need to be considered to ensure trial designs are truly informative and oncoming data can be analysed efficiently. Here, we use clinical trial simulations to assess the contribution of historical data for (1) the analysis of sparse samples from a limited number of children and (2) the optimisation of study design when an increase in the number of subjects is not feasible. The evaluation of the pharmacokinetics of deferasirox in paediatric patients affected by haemoglobinopathies was used as case study. Our investigation shows that the incorporation of prior knowledge increases parameter precision and probability of successful convergence from only 12% with no priors to 56% and 75% for weakly and highly informative priors, respectively. In addition, results suggest that even when only one sample is collected per subject, as implemented in the original trial and in many other examples in clinical research, there is a 60% probability of biased parameter estimates (>25%). In conjunction with adult prior information and optimisation techniques, the probability of bias could be limited to <20% by increasing the number of samples/subject from 1 to 3. The methodology described here can be easily applied to other studies in small populations.

Decision Science for Generic Drug Development and Review.

Decision Science for Generic Drug Development and Review.

PCN246 A SYSTEMATIC APPROACH FOR QUANTIFYING HETEROGENEITY WITHIN CLINICAL TRIAL POPULATIONS

Heterogeneity in clinical trial populations can contribute to variability in observed treatment effect. According to the Cochrane Handbook, sources can be either clinical or methodological diversity.1 Two proven quantitative approaches to addressing heterogeneity include use of clustering algorithms and of propensity score modeling techniques.2,3 We propose here a quantitative, systematic, scalable approach to assessing clinical trial population heterogeneity. Analysis was conducted in a pooled dataset of 7 clinical trials (n=719) for relapsed/refractory AML from 2012-2017 Medidata archive of >3000 trials.4 Based on NCCN guidelines for AML, top 4 treatment choices were identified by patient count and propensity scores for treatment receipt calculated for individual respondents.5 Frequency distributions of propensity scores were generated, and percent area of overlap between distributions was calculated. K-nearest neighbor classification was conducted to identify potential clusters. Covariates included age, gender, therapy duration, and comorbidities. As a starting point, k was defined as √n. Number and size of potential patient clusters were calculated. As the purpose was to identify potential classifications, no testing/training split was employed, and advanced feature reduction was not required. Propensity score distributions yielded a high degree of overlap. K-nearest neighbor classification indicated potential differences in groupings for shorter versus longer duration of treatment. This approach enables rapid visual and quantifiable assessment of potential underlying differences in both treatment assignation as well as clinical response. This can guide application of advanced analytics to correct for this, particularly in populations where heterogeneity may not be anticipated. Standardized and routine estimation of study population heterogeneity has potential application for sub-population identification, clinical trial simulation, and cross-study comparison.

Simulation Practices for Adaptive Trial Designs in Drug and Device Development

Adaptive clinical trials are the cornerstone of modern drug and device development. The most recent US legislation calls for higher efficiency in designing clinical trials with emphasis on expanding the utilization of complex innovative designs that cannot be developed without simulations. It is well recognized that clinical trial simulation is a fundamental tool to explore, compare, and understand the operating characteristics, statistical properties, and adaptive decisions embedded in different designs to answer the given research questions. This article provides insights from industry on the development of a simulation report from a group of statisticians brought together under the sponsorship of the Drug Information Association Adaptive Design Scientific Working Group. This effort intends to illustrate the key common elements required to ensure higher consistency and clarity in conducting and reporting simulations of adaptive clinical trials, eliminate unnecessary barriers in communicating technical design aspects to different audiences, and facilitate the assessment of pros and cons of candidate designs. The design-dependent elements of a simulation report applicable to specific types of adaptive trials are presented with the examples of dose-escalation designs, dose-ranging studies, trials with sample size re-estimation and early stopping rules, and confirmatory multistage designs.

Power behind the throne: A clinical trial simulation study evaluating the impact of controllable design factors on the power of antidepressant trials.

To evaluate the impact of controllable design factors on the power of antidepressants trials. Using clinical trial simulation (CTS), we analyzed the combined impact on the power of trials of controllable design factors (sample size, outcome metrics, and disease severity at inclusion) and uncontrollable parameters (heterogeneity of diseases labeled "depression" in the source population and selective effects of drugs on items of the Hamilton Depression Rating Scale [HDRS], the most used outcome measurement tool). We elaborated 3,840 scenarios calibrated with real data, particularly the publication bias-corrected effect size. For an effect size of 0.26, simulations revealed that in trials with ≤650 participants, power was less than 80%. Among the tested outcome metrics, the "remission" outcome provided more robustness for sample heterogeneity, whereas the continuous outcome "HDRS changes" provided more robustness when investigating drugs with a selective effect on the HDRS items. For the "remission" outcome, the power of trials increased with increasing HDRS threshold at inclusion but decreased with the outcomes "response" and "HDRS changes. Drugs with a selective effect on the HDRS items could not reach the same power as for the reference drug. Our study allows for drawing recommendations to avoid underpowered trials of antidepressants.

Highly Efficient Clinical Trials Simulator (HECT): Software application for planning and simulating platform adaptive trials

Background: Adaptive designs and platform designs are among two common clinical trial innovations that are increasingly being used to manage medical intervention portfolios and attain faster regulatory approvals. Planning of adaptive and platform trials necessitate simulations to understand how a set of adaptation rules will likely affect the properties of the trial. Clinical trial simulations, however, remain a black box to many clinical trials researchers who are not statisticians. Results: In this article we introduce a simple intuitive open-source browser-based clinical trial simulator for planning adaptive and platform trials. The software application is implemented in RShiny and features a graphical user interface that allows the user to set key clinical trial parameters and explore multiple scenarios such as varying treatment effects, control response and adherence, as well as number of interim looks and adaptation rules. The software provides simulation options for a number of designs such as dropping treatment arms for futility, adding a new treatment arm (i.e., platform design), and stopping a trial early based on superiority. All available adaptations are based on underlying Bayesian probabilities. The software comes with a number of graphical outputs to examine properties of individual simulated trials. The main output is a comparison of trial design performance across several simulations, graphically summarizing type I error (false positive risk), power, and expected cost/time to completion of the considered designs. Conclusion: We have developed and validated an intuitive highly efficient clinical trial simulator for planning of clinical trials. The software is open-source and caters to clinical trial investigators who do not have the statistical capacity for trial simulations available in their team. The software can be accessed via any web browser via the following link: https://mtek.shinyapps.io/hect/

Highly Efficient Clinical Trials Simulator (HECT): Software application for planning and simulating platform adaptive trials.

Background: Adaptive designs and platform designs are among two common clinical trial innovations that are increasingly being used to manage medical intervention portfolios and attain faster regulatory approvals. Planning of adaptive and platform trials necessitate simulations to understand how a set of adaptation rules will likely affect the properties of the trial. Clinical trial simulations, however, remain a black box to many clinical trials researchers who are not statisticians. Results: In this article we introduce a simple intuitive open-source browser-based clinical trial simulator for planning adaptive and platform trials. The software application is implemented in RShiny and features a graphical user interface that allows the user to set key clinical trial parameters and explore multiple scenarios such as varying treatment effects, control response and adherence, as well as number of interim looks and adaptation rules. The software provides simulation options for a number of designs such as dropping treatment arms for futility, adding a new treatment arm (i.e., platform design), and stopping a trial early based on superiority. All available adaptations are based on underlying Bayesian probabilities. The software comes with a number of graphical outputs to examine properties of individual simulated trials. The main output is a comparison of trial design performance across several simulations, graphically summarizing type I error (false positive risk), power, and expected cost/time to completion of the considered designs. Conclusion: We have developed and validated an intuitive highly efficient clinical trial simulator for planning of clinical trials. The software is open-source and caters to clinical trial investigators who do not have the statistical capacity for trial simulations available in their team. The software can be accessed via any web browser via the following link: https://mtek.shinyapps.io/hect/.

Highly Efficient Clinical Trials Simulator (HECT): Software application for planning and simulating platform adaptive trials

Background: Adaptive designs and platform designs are among two common clinical trial innovations that are increasingly being used to manage medical intervention portfolios and attain faster regulatory approvals. Planning of adaptive and platform trials necessitate simulations to understand how a set of adaptation rules will likely affect the properties of the trial. Clinical trial simulations, however, remain a black box to many clinical trials researchers who are not statisticians. Results: In this article we introduce a simple intuitive open-source browser-based clinical trial simulator for planning adaptive and platform trials. The software application is implemented in RShiny and features a graphical user interface that allows the user to set key clinical trial parameters and explore multiple scenarios such as varying treatment effects, control response and adherence, as well as number of interim looks and adaptation rules. The software provides simulation options for a number of designs such as dropping treatment arms for futility, adding a new treatment arm (i.e., platform design), and stopping a trial early based on superiority. All available adaptations are based on underlying Bayesian probabilities. The software comes with a number of graphical outputs to examine properties of individual simulated trials. The main output is a comparison of trial design performance across several simulations, graphically summarizing type I error (false positive risk), power, and expected cost/time to completion of the considered designs. Conclusion: We have developed and validated an intuitive highly efficient clinical trial simulator for planning of clinical trials. The software is open-source and caters to clinical trial investigators who do not have the statistical capacity for trial simulations available in their team. The software can be accessed via any web browser via the following link: https://mtek.shinyapps.io/hect/