Monitoring Of Clinical Trials Research Articles

A new trial monitoring plan (TMP) template for clinical trials: output from a Delphi process

BackgroundMonitoring is a crucial part of trial conduct and ensures that participants’ data is fairly represented, and future healthcare information is enhanced. This project aims to improve trial monitoring by creating a trial monitoring plan (TMP) template with input from individuals experienced in monitoring clinical trials.MethodsA review of monitoring plans received from UK Clinical Research Collaboration (UKCRC) registered clinical trials units (CTU)s created the basis for a preliminary TMP template and a Delphi survey. The TMP template was finalised after 2 rounds of a Delphi survey and a two-part consensus meeting including trialists with experience and expertise in monitoring clinical trials.ResultsWe received 31 monitoring plans from UKCRC-registered CTUs and reviewed over 800 monitoring items for inclusion in a TMP template, selecting items based on guidelines such as Good Clinical Practice (GCP) and our monitoring experiences. For certain items, further expert input was required. A total of 66 items were chosen for a Delphi survey involving 47 participants from 25 UK CTUs and industry. After the first round, all 66 items were retained, and six additional items were suggested by Delphi participants. In the second round, 37 items reached consensus for inclusion in the TMP template. The Delphi process was followed by a consensus meeting with 9 participants from 9 UK CTUs and industry. Participants in the consensus meeting voted on the 32 further items that had not reached the definition of consensus within the Delphi, regarding each item’s inclusion in or exclusion from the TMP template. The voting resulted in 18 items being excluded, leaving 14 items to be included in the TMP template. The process overall resulted in a standardised TMP template with input from many individuals with interest, experience, or expertise in monitoring clinical trials.ConclusionA TMP template was developed building on the currently used monitoring plans and with input from those experienced in clinical trial monitoring. Using a centrally developed good quality TMP template should contribute towards maintaining consistency in monitoring standards across all CTUs, resulting in higher research quality and improved quality assurance. Its use should provide reassurance to participants that their participation is carefully monitored to ensure that their data or any samples provided are treated with confidentiality, integrity, and respect and that their rights and well-being are protected.

Advances in molecular imaging of kidney diseases.

Background Diagnosing and monitoring kidney diseases traditionally rely on blood and urine analyses and invasive procedures such as kidney biopsies, the latter offering limited possibilities for longitudinal monitoring and a comprehensive understanding of disease dynamics. Current non-invasive methods lack specificity in capturing intrarenal molecular processes, hindering patient stratification and patient monitoring in clinical practice and clinical trials. Summary Molecular imaging enables non-invasive and quantitative assessment of physiological and pathological molecular processes. By using specific molecular probes and imaging technologies, e.g., magnetic resonance imaging (MRI), positron emission tomography (PET), single-photon emission computed tomography (SPECT), or ultrasound (US), molecular imaging allows the detection and longitudinal monitoring of disease activity with spatial and temporal resolution of different kidney diseases and disease-specific pathways. Several approaches have already shown promising results in kidneys and exploratory clinical studies, and validation is needed before implementation in clinical practice. Key messages Molecular imaging offers a non-invasive assessment of intrarenal molecular processes, overcoming the limitations of current diagnostic methods. It has the potential to serve as companion diagnostics, not only in clinical trials, aiding in patient stratification and treatment response assessment. By guiding therapeutic interventions, molecular imaging might contribute to the development of targeted therapies for kidney diseases.

Ensuring safety and monitoring adverse events in clinical trials: Challenges and innovations

Safety and adverse events (AEs) monitoring in clinical trials are critical to ensuring participant safety and the successful development of new therapies. Effective monitoring not only protects patients but also helps identify any risks associated with the investigational product. This paper elaborates on the methodologies used to detect, categorize, and report adverse events during clinical trials, and how these processes are governed by stringent regulatory frameworks. Additionally, the discussion highlights the role of pharmacovigilance systems, the ethical obligations researchers face in AE reporting, and the technological innovations that are transforming safety monitoring. Challenges, including under-reporting, data variability, and interpretation difficulties, are examined in depth. Ultimately, advancements in real-time data collection, adaptive trial designs, and artificial intelligence (AI) are promising to improve the accuracy and timeliness of adverse event monitoring, shaping the future of clinical research and patient safety.

Streamlining Clinical Trial Data from Raw to Regulatory Submission with R Shiny and Pharmaverse

In recent years, the pharmaceutical and biotech industries have increasingly adopted open-source tools to enhance the efficiency, transparency, and reproducibility of clinical trial data analysis. R Shiny, a web application framework for R, has become a key component in creating interactive dashboards and visualizations that support real-time data exploration and decision-making. As part of the Pharmaverse ecosystem, R Shiny plays a vital role in regulatory submissions, data analysis, and reporting, offering dynamic, user-friendly interfaces for clinical trial monitoring, patient safety reporting, and efficacy analysis. This abstract explores the application of R Shiny within the Pharmaverse, emphasizing its seamless integration with tools such as Tplyr and admiral for generating CDISC-compliant SDTM and ADaM datasets. Metadata management and validation are facilitated through OAK, ensuring consistency and traceability. The automated creation of Define.xml using tools like defineR further enhances transparency and regulatory compliance. Through case studies and practical examples, this abstract highlights how R Shiny and related tools improve data transparency, accelerate decision-making, and support regulatory compliance in clinical trials. By leveraging these open-source technologies, clinical trial teams can streamline data workflows, enhance collaboration, and improve outcomes, ultimately driving innovation within the pharmaceutical industry. The integration of these tools into a cohesive framework significantly enhances the submission process to regulatory bodies like the FDA and EMA, ensuring that clinical data meets both industry and regulatory standards.

Toxicity surveillance in FDA pivotal registration trials for immune checkpoint inhibitors: Are we missing the mark?

215 Background: Immune checkpoint inhibitors (ICIs) have changed the treatment landscape for a multitude of malignancies, leading to prolonged survival and durable responses. Despite this, a significant number of patients will develop immune-related adverse events (irAEs) with significant associated morbidity and mortality. A retrospective study has previously demonstrated that 28% of patients can develop IrAEs from 6 months up to 2 years after starting therapy (1). Surveillance for toxicity is essential for understanding the long-term risk associated with ICIs. We aimed to analyze the reporting timeframes used in clinical trials of immuno-oncology agents leading to FDA approval. Methods: In this retrospective study, we interrogated the HemeOnc knowledgebase to curate a list of all clinical trial publications leading to FDA approval from 2011 to 2024. We manually selected from this list any trial including an immune checkpoint inhibitor either as monotherapy or in combination with another systemic therapy. The adverse event follow-up time was then extrapolated by manually reviewing the trial publication, supplemental materials or the trial protocol depending on where this information was listed. All trials were categorized based on trial initiation dates. Results: We identified 175 clinical trials leading to FDA approvals, with trial initiation dates ranging from 2004 to 2019. 165 trials were included in the analysis with 10 being excluded due to lack of language regarding follow-up. 62% of the trials included were Phase III trials with the remaining 38% Phase I/II trials. The median follow-up days for all trials across this timeframe was 90, with a range of 28 to 135. The median follow-up time for AE monitoring increased by nearly 30%, rising from 70 days in 2004 to 90 days in 2013. Subsequently, we observed a consistent median follow-up time of 90 days (28 to 130 days) from that point onward, with no further increase. Conclusions: To our knowledge this is the first systematic review of IrAE monitoring in clinical trials leading to FDA approval. In our study we revealed that, despite advancements in the understanding of IrAEs and their potential for late presentation, registration trials leading to regulatory approval remain inadequately designed to capture chronic and delayed immunotherapy toxicities. As these therapies advance into earlier stages of disease with longer life expectancy, it will be critical for regulatory bodies, study sponsors, and trial investigators to either redesign clinical trials or for there to be an alternative method that comprehensively captures the incidence and pattern of late-presenting toxicities. 1. Durbin, ASCO 2023.

Bayesian Methods for Quality Tolerance Limit (QTL) Monitoring.

In alignment with the ICH guideline for Good Clinical Practice [ICH E6(R2)], quality tolerance limit (QTL) monitoring has become a standard component of risk-based monitoring of clinical trials by sponsor companies. Parameters that are candidates for QTL monitoring are critical to participant safety and quality of trial results. Breaching the QTL of a given parameter could indicate systematic issues with the trial that could impact participant safety or compromise the reliability of trial results. Methods for QTL monitoring should detect potential QTL breaches as early as possible while limiting the rate of false alarms. Early detection allows for the implementation of remedial actions that can prevent a QTL breach at the end of the trial. We demonstrate that statistically based methods that account for the expected value and variability of the data generating process outperform simple methods based on fixed thresholds with respect to important operating characteristics. We also propose a Bayesian method for QTL monitoring and an extension that allows for the incorporation of partial information, demonstrating its potential to outperform frequentist methods originating from the statistical process control literature.

LightNet: A Novel Lightweight Convolutional Network for Brain Tumor Segmentation in Healthcare.

Diagnosis, treatment planning, surveillance, and the monitoring of clinical trials for brain diseases all benefit greatly from neuroimaging-based tumor segmentation. Recently, Convolutional Neural Networks (CNNs) have demonstrated promising results in enhancing the efficiency of image-based brain tumor segmentation. Most current work on CNNs, however, is devoted to creating increasingly complicated convolution modules to improve performance, which in turn raises the computing cost of the model. This work proposes a simple and effective feed-forward CNN, LightNet (Light Network). Based on multi-path and multi-level, it replaces traditional convolutional methods with light operations, which reduces network parameters and redundant feature maps. In the up-sampling stage, a light channel attention module is added to achieve richer multi-scale and spatial semantic feature information extraction of brain tumor. The performance of the network is evaluated in the Multimodal Brain Tumor Segmentation Challenge (BraTS 2015) dataset, and results are presented here alongside other high-performing CNNs. Results show comparable accuracy with other methods but with increased efficiency, segmentation performance, and reduced redundancy and computational complexity. The result is a high-performing network with a balance between efficiency and accuracy, allowing, for example, better energy performance on mobile devices.

Determining the extent and frequency of on-site monitoring: a bayesian risk-based approach

BackgroundOn-site monitoring is a crucial component of quality control in clinical trials. However, many cast doubt on its cost-effectiveness due to various issues, such as a lack of monitoring focus that could assist in prioritizing limited resources during a site visit. Consequently, an increasing number of trial sponsors are implementing a hybrid monitoring strategy that combines on-site monitoring with centralised monitoring. One of the primary objectives of centralised monitoring, as stated in the clinical trial guidelines, is to guide and adjust the extent and frequency of on-site monitoring. Quality tolerance limits (QTLs) introduced in ICH E6(R2) and thresholds proposed by TransCelerate Biopharma are two existing approaches for achieving this objective at the trial- and site-levels, respectively. The funnel plot, as another threshold-based site-level method, overcomes the limitation of TransCelerate’s method by adjusting thresholds flexibly based on site sizes. Nonetheless, both methods do not transparently explain the reason for choosing the thresholds that they used or whether their choices are optimal in any certain sense. Additionally, related Bayesian monitoring methods are also lacking.MethodsWe propose a simple, transparent, and user-friendly Bayesian-based risk boundary for determining the extent and frequency of on-site monitoring both at the trial- and site-levels. We developed a four-step approach, including: 1) establishing risk levels for key risk indicators (KRIs) along with their corresponding monitoring actions and estimates; 2) calculating the optimal risk boundaries; 3) comparing the outcomes of KRIs against the optimal risk boundaries; and 4) providing recommendations based on the comparison results. Our method can be used to identify the optimal risk boundaries within an established risk level range and is applicable to continuous, discrete, and time-to-event endpoints.ResultsWe evaluate the performance of the proposed risk boundaries via simulations that mimic various realistic clinical trial scenarios. The performance of the proposed risk boundaries is compared against the funnel plot using real clinical trial data. The results demonstrate the applicability and flexibility of the proposed method for clinical trial monitoring. Moreover, we identify key factors that affect the optimality and performance of the proposed risk boundaries, respectively.ConclusionGiven the aforementioned advantages of the proposed risk boundaries, we expect that they will benefit the clinical trial community at large, in particular in the realm of risk-based monitoring.

ClinicalGAN: powering patient monitoring in clinical trials with patient digital twins

Conducting clinical trials is becoming increasingly challenging lately due to spiraling costs, increased time to market, and high failure rates. Patient recruitment and retention is one of the key challenges that impact 90% of the trials directly. While a lot of attention has been given to optimizing patient recruitment, limited progress has been made towards developing comprehensive clinical trial monitoring systems to determine patients at risk and potentially improve patient retention through the right intervention at the right time. Earlier research in patient retention primarily focused on using deterministic frameworks to model the inherently stochastic patient journey process. Existing generative approaches to model temporal data such as TimeGAN or CRBM , face challenges and fail to address key requirements such as personalized generation, variable patient journey, and multi-variate time-series needed to model patient digital twin. In response to these challenges, current research proposes ClinicalGAN to enable patient level generation, effectively creating a patient’s digital twin. ClinicalGAN provides capabilities for: (a) patient-level personalized generation by utilizing patient meta-data for conditional generation; (b) dynamic termination prediction to enable pro-active patient monitoring for improved patient retention; (c) multi-variate time-series training to incorporate relationship and dependencies among different tests measures captured during patient journey. The proposed solution is validated on two Alzheimer’s clinical trial datasets and the results are benchmarked across multiple dimensions of generation quality. Empirical results demonstrate that the proposed ClinicalGAN outperforms the SOTA approach by 3–4×\\documentclass[12pt]{minimal} \\usepackage{amsmath} \\usepackage{wasysym} \\usepackage{amsfonts} \\usepackage{amssymb} \\usepackage{amsbsy} \\usepackage{mathrsfs} \\usepackage{upgreek} \\setlength{\\oddsidemargin}{-69pt} \\begin{document}$$\ imes $$\\end{document} on average across all the generation quality metrics. Furthermore, the proposed architecture is shown to outperform predictive methods at the task of drop-off prediction significantly (5–10% MAPE scores).

Upstrapping to determine futility: predicting future outcomes nonparametrically from past data

BackgroundClinical trials often involve some form of interim monitoring to determine futility before planned trial completion. While many options for interim monitoring exist (e.g., alpha-spending, conditional power), nonparametric based interim monitoring methods are also needed to account for more complex trial designs and analyses. The upstrap is one recently proposed nonparametric method that may be applied for interim monitoring.MethodsUpstrapping is motivated by the case resampling bootstrap and involves repeatedly sampling with replacement from the interim data to simulate thousands of fully enrolled trials. The p-value is calculated for each upstrapped trial and the proportion of upstrapped trials for which the p-value criteria are met is compared with a pre-specified decision threshold. To evaluate the potential utility for upstrapping as a form of interim futility monitoring, we conducted a simulation study considering different sample sizes with several different proposed calibration strategies for the upstrap. We first compared trial rejection rates across a selection of threshold combinations to validate the upstrapping method. Then, we applied upstrapping methods to simulated clinical trial data, directly comparing their performance with more traditional alpha-spending and conditional power interim monitoring methods for futility.ResultsThe method validation demonstrated that upstrapping is much more likely to find evidence of futility in the null scenario than the alternative across a variety of simulations settings. Our three proposed approaches for calibration of the upstrap had different strengths depending on the stopping rules used. Compared to O’Brien-Fleming group sequential methods, upstrapped approaches had type I error rates that differed by at most 1.7% and expected sample size was 2–22% lower in the null scenario, while in the alternative scenario power fluctuated between 15.7% lower and 0.2% higher and expected sample size was 0–15% lower.ConclusionsIn this proof-of-concept simulation study, we evaluated the potential for upstrapping as a resampling-based method for futility monitoring in clinical trials. The trade-offs in expected sample size, power, and type I error rate control indicate that the upstrap can be calibrated to implement futility monitoring with varying degrees of aggressiveness and that performance similarities can be identified relative to considered alpha-spending and conditional power futility monitoring methods.

Internet of Things-Enabled Patch With Built-in Microsensors and Wireless Chip: Real-Time Remote Monitoring of Patch Treatment.

We aimed to design, develop, and evaluate an internet of things-enabled patch (IoT patch) for real-time remote monitoring of adherence (or patch wear time) during patch treatment in child participants in clinical trials. This study provides healthcare providers with a tool for objective, real-time, and remote assessment of adherence and for making required adjustments to treatment plans. The IoT patch had two temperature microsensors and a wireless chip. One sensor was placed closer to the skin than the other, resulting in a temperature difference depending on whether the patch was worn. When the patch was worn, it measured temperatures every 30 seconds and transmitted temperature data to a cloud server via a mobile application every 15 seconds. The patch was evaluated via 2 experiments with 30 healthy adults and 40 children with amblyopia. Excellent monitoring accuracy was observed in both adults (mean delay of recorded time data, 0.4 minutes) and children (mean, 0.5 minutes). The difference between manually recorded and objectively recorded patch wear times showed good agreement in both groups. Experiment 1 showed accurate monitoring over a wide range of temperatures (from 0 to 30°C). Experiment 2 showed no significant differences in wearability (ease-of-use and comfort scores) between the IoT and conventional patches. The IoT patch offers an accurate, real-time, and remote system to monitor adherence to patch treatment. The patch is comfortable and easy to use. The utilization of an IoT patch may increase adherence to patch treatment based on accurate monitoring. Results show that the IoT patch can enable real-time adherence monitoring in clinical trials, improving treatment precision, and patient compliance to enhance outcomes.

Strategies for Comprehensive Immune Monitoring of Clinical Trials to Advance Human Immunotherapy in Remote Settings

Abstract Comprehensive immune monitoring of clinical trials relies on the ability to generate high-quality data to gain insights into disease detection and treatment. Execution of longitudinal clinical trials in flow cytometry is complex due to logistical challenges in high-quality sample collection. Our group has developed a novel blood “immune signature” of cancer nonresponsiveness that predicts failure to make a clinical response to checkpoint immunotherapies targeting the PD-1/PD-L1 pathway. Recent advances in CyTOF™ technology allow us to expand clinical implementation to remote settings with a 50-plus-marker panel. CyTOF is the only technology that allows remote asynchronous sample collection with the largest immunophenotyping panel yielding reproducible results. This approach is uniquely enabled using a stable lyophilized large-scale staining panel and achieving comprehensive marker coverage in one panel minimizes blood sample required. CyTOF enables a simplified workflow including blood collection and processing in remote locations to enable the simplest clinical trial infrastructure complexity and cost, easily implemented in current hospital labs. This study is designed to demonstrate the clinical impact and utility of CyTOF for a highly simplified workflow for multi-site clinical trials. We report on the first phase of this project that implements this simplified workflow across remote clinical collection sites, data integration, and analysis.

Enhancing Dementia Risk Mitigation: Standardized Digital Monitoring in Clinical Trials and Platform Design for At-Risk Elderly Individuals.

Dementia is becoming a significant public health concern, affecting approximately 130,000 individuals in Austria, whereby nearly 40% of the cases are attributed to modifiable risk factors. Multidomain lifestyle interventions have thereby demonstrated significant effects in reducing the risk of dementia. The goal was to define an interoperability framework to conduct standardized monitoring in clinical trials for enhancing dementia risk mitigation. In addition, the identified standards should be integrated into the components of the project. A step-by-step approach was used, where initially data collection, aggregation and harmonization was carried out with retrospective data from various clinical centers. Afterwards, the interoperability framework was defined including the prospective data that is gathered during a clinical trial. A guideline for integrating healthcare standards was developed and incorporated into the technical components for the clinical trial. The interoperability framework was designed in a scalable way and will be regularly updated for future needs.

Miniaturized inkjet-printed flexible ion-selective sensing electrodes with the addition of graphene in PVC layer for fast response real-time monitoring applications

In this letter, we propose a miniaturization scheme of inkjet printed ionic sensing electrodes by adding graphene into the ion-selective PVC film not only to reduce the impedance of the ionic liquid layer of the electrode but also to increase the electrode capacitance for the reduction of the response time. Based on the scheme, we present a fully inkjet-printed electrochemical ion-selective sensor comprising a working electrode and reference electrode, which are inkjet-printed Ag NPs/PEDOT:PSS-graphene/PVC-graphene and Ag/AgCl(s)/ionic liquid PVC-graphene layer structures, respectively. The printed ion-selective working electrode has been miniaturized to a size of 22,400 μm2 equivalent to a square shape of ∼150 × 150 μm2 comparable to the size of a human cell. By adding graphene to the ion selective PVC film, more than 90 % charge transfer resistance reduction can be achieved and the shunt capacitance is increased by 3.4-fold in shunt capacitance compared to the film without graphene, thereby more than 33 % reduction of the response time required to reach equilibrium. Meanwhile, these miniaturized potassium sensors using the working electrodes with/without adding graphene have been integrated with in-lab signal-processing and wireless-transmission module to yield similar results to the one measured by commercial electrochemical workstation showing a great potential for real-time monitoring in portable clinical trials. Specifically, the proposed sensor utilizing graphene-enhanced electrodes demonstrates a linearity uncertainty of 2.9 mV, which is approximately half of the uncertainty observed in the sensors lacking graphene integration.

Challenges implementing the FDA guidance RE diet stabilization and monitoring in clinical trials for IEMs

Challenges implementing the FDA guidance RE diet stabilization and monitoring in clinical trials for IEMs

Abstract 7625: The liquid biomarker thymidine kinase activity reflecting proliferation rate can provide guidance in drug development and address Project Optimus

Abstract Thymidine Kinase (TK) plays a basic role in DNA synthesis and cell proliferation. The enzyme is highly cell cycle dependent and correlates with cell growth state. Numerous studies have consistently shown elevated TK activity (TKa) in the blood of patients with solid tumors and active disease. DiviTum® TKa, a clinically and analytically validated FDA cleared assay, measures and quantifies TKa in serum or plasma in humans and animals. TKa as a biomarker offers valuable information at all stages of drug development, from drug efficacy and dose response studies in cell culture and xenograft models, to patient selection and treatment monitoring in clinical trials. The launch of FDA Project Optimus highlights the need for new biomarkers to assist in drug development to identify optimal dose. Using TKa to measure the impact on cell proliferation as a drug evaluation endpoint can confirm mechanism of action efficacy and a minimally effective dose of a new compound rather than a maximally tolerated dose. TKa can be used as a pharmacodynamic tool to improve study endpoints. TKa data in cultured cells and mouse xenografts demonstrates that a complete shutdown of tumor cell proliferation can be observed well before and at much lower drug-doses than when cell death or toxicity is used as the dose determination endpoint. Increased dosing of CDK4/6 inhibitors demonstrates immediate impact on TKa levels in cell lines and provides signals of drug effect at doses where cell viability is unaffected. After initiation of samuraciclib treatment, a CDK7 inhibitor, a significant reduction in TKa levels in blood from metastatic breast cancer (MBC) patients was observed, indicating inhibition of cell cycle progression. For patients on samuraciclib therapy and with stable disease before progression, TKa levels were significantly lower than pre-treatment (p=0.0038). Post-progression TKa levels increased to significantly higher levels than during therapy including the last sample measured before progression (p=0.014). Baseline blood TKa levels are predictive for progression free survival (PFS) allowing for the early stratification of patients with long vs short PFS. In patients with MBC, TKa levels during the first treatment cycle are strongly predictive for PFS. Suppressed TKa levels after 2 and 4 weeks of CDK4/6 inhibitor therapy predict longer PFS than high TKa levels at the same time points (HR 5.65; p< 0.0001). MBC patients monitored >3 months and showing low TKa levels have very low likelihood of progression within 30- to 60 days, <2% and <6%, respectively. TKa is a translational liquid biomarker that bridges results between preclinical and clinical studies, providing fundamental information for drug development decision making. Citation Format: Mattias Bergqvist, Hanna Ritzén, Amy Williams. The liquid biomarker thymidine kinase activity reflecting proliferation rate can provide guidance in drug development and address Project Optimus [abstract]. In: Proceedings of the American Association for Cancer Research Annual Meeting 2024; Part 1 (Regular Abstracts); 2024 Apr 5-10; San Diego, CA. Philadelphia (PA): AACR; Cancer Res 2024;84(6_Suppl):Abstract nr 7625.

Frequent response monitoring in chronic lymphocytic leukemia clinical trials: what is the value?

Frequent response monitoring in chronic lymphocytic leukemia clinical trials: what is the value?

Design and monitoring of clinical trials with an interim analysis and a negative binomial endpoint

There are very rich publications devoted to group sequential design, adaptive design and trial monitoring for continuous, binary and time to event endpoints. Many authors also discuss fixed design, blinded sample size re-estimation design and group sequential design for studies with a negative binomial outcome. Nonetheless, literature is sparse in adaptive design for a trial with a negative binomial endpoint. The features of such an endpoint in a flexible trial design setting remains inadequately understood. In this research, we seek to bridge this knowledge gap by offering a thorough examination of utilizing data components from a two-stage adaptive design for unblinded conditional power calculation and corresponding sample size re-estimation. We also provide expression for calculating the probability of meeting the futility criterion to determine the appropriate timing for the interim analysis. To evaluate the performance of the design, we conduct simulations to assess its operation characteristics. Finally, we provide a helpful and illustrative example to demonstrate the practical applications of the methods.

Interactive medical and safety monitoring in clinical trials with clinDataReview: a validated and open-source reporting tool.

Continuous medical and safety monitoring of subject data during a clinical trial is a critical part of evaluating the safety of trial participants and as such is governed by protocol procedures and regulatory guidelines to meet the trial's intended objectives. We present an open-source validated graphical tool (clinDataReview R package) which provides access to the trial data with drill-down to individual patient profiles. The tool incorporates functionalities that facilitate detection of error and data inconsistencies requiring follow-up. It supports regular medical monitoring and oversight as well as safety monitoring committees with interactive tables and listings alongside graphical visualizations of the primary safety data in reports. An implementation example is given where the tool is used to deliver validated outputs following FDA/EMA guidelines. As such, this tool enables a more efficient, interactive, and reproducible review of safety data collected during an ongoing clinical trial.

Wearable Accelerometer and Gyroscope Sensors for Estimating the Severity of Essential Tremor.

Several validated clinical scales measure the severity of essential tremor (ET). Their assessments are subjective and can depend on familiarity and training with scoring systems. We propose a multi-modal sensing using a wearable inertial measurement unit for estimating scores on the Fahn-Tolosa-Marin tremor rating scale (FTM) and determine the classification accuracy within the tremor type. 17 ET participants and 18 healthy controls were recruited for the study. Two movement disorder neurologists who were blinded to prior clinical information viewed video recordings and scored the FTM. Participants drew a guided Archimedes spiral while wearing an inertial measurement unit placed at the mid-point between the lateral epicondyle of the humerus and the anatomical snuff box. Acceleration and gyroscope recordings were analyzed. The ratio of the power spectral density between frequency bands 0.5-4 Hz and 4-12 Hz, and the sum of power spectrum density over the entire spectrum of 2-74 Hz, for both accelerometer and gyroscope data, were computed. FTM was estimated using regression model and classification using SVM was validated using the leave-one-out method. Regression analysis showed a moderate to good correlation when individual features were used, while correlation was high ([Formula: see text] = 0.818) when suitable features of the gyro and accelerometer were combined. The accuracy for two-class classification of the combined features using SVM was 91.42% while for four-class it was 68.57%. Potential applications of this novel wearable sensing method using a wearable Inertial Measurement Unit (IMU) include monitoring of ET and clinical trials of new treatments for the disorder.