Academic literature on the topic 'Emulated clinical trials'
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Journal articles on the topic "Emulated clinical trials"
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Shuman, Benjamin R., Brad D. Hendershot, David C. Morgenroth, and Elizabeth Russell Esposito. "A patient-centered ‘test-drive’ strategy for ankle-foot orthosis prescription: Protocol for a randomized participant-blinded trial." PLOS ONE 19, no. 5 (May 2, 2024): e0302389. http://dx.doi.org/10.1371/journal.pone.0302389.
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Background Ankle-foot orthoses (AFOs) are commonly used to overcome mobility limitations related to lower limb musculoskeletal injury. Despite a multitude of AFOs to choose from, there is scant evidence to guide AFO prescription and limited opportunities for AFO users to provide experiential input during the process. To address these limitations in the current prescription process, this study evaluates a novel, user-centered and personalized ‘test-drive’ strategy using a robotic exoskeleton (‘AFO emulator’) to emulate commercial AFO mechanical properties (i.e., stiffness). The study will determine if brief, in-lab trials (with emulated or actual AFOs) can predict longer term preference, satisfaction, and mobility outcomes after community trials (with the actual AFOs). Secondarily, it will compare the in-lab experience of walking between actual vs. emulated AFOs. Methods and analysis In this participant-blinded, randomized crossover study we will recruit up to fifty-eight individuals with lower limb musculoskeletal injuries who currently use an AFO. Participants will walk on a treadmill with three actual AFOs and corresponding emulated AFOs for the "in-lab” assessments. For the community trial assessment, participants will wear each of the actual AFOs for a two-week period during activities of daily living. Performance-based and user-reported measures of preference and mobility will be compared between short- and long-term trials (i.e., in-lab vs. two-week community trials), and between in-lab trials (emulated vs. actual AFOs). Trial registration The study was prospectively registered at www.clininicaltrials.gov (Clinical Trials Study ID: NCT06113159). Date: November 1st 2023. https://classic.clinicaltrials.gov/ct2/show/NCT06113159.
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Wang, Shirley V., Sebastian Schneeweiss, Jessica M. Franklin, Rishi J. Desai, William Feldman, Elizabeth M. Garry, Robert J. Glynn, et al. "Emulation of Randomized Clinical Trials With Nonrandomized Database Analyses." JAMA 329, no. 16 (April 25, 2023): 1376. http://dx.doi.org/10.1001/jama.2023.4221.
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ImportanceNonrandomized studies using insurance claims databases can be analyzed to produce real-world evidence on the effectiveness of medical products. Given the lack of baseline randomization and measurement issues, concerns exist about whether such studies produce unbiased treatment effect estimates.ObjectiveTo emulate the design of 30 completed and 2 ongoing randomized clinical trials (RCTs) of medications with database studies using observational analogues of the RCT design parameters (population, intervention, comparator, outcome, time [PICOT]) and to quantify agreement in RCT-database study pairs.Design, Setting, and ParticipantsNew-user cohort studies with propensity score matching using 3 US claims databases (Optum Clinformatics, MarketScan, and Medicare). Inclusion-exclusion criteria for each database study were prespecified to emulate the corresponding RCT. RCTs were explicitly selected based on feasibility, including power, key confounders, and end points more likely to be emulated with real-world data. All 32 protocols were registered on ClinicalTrials.gov before conducting analyses. Emulations were conducted from 2017 through 2022.ExposuresTherapies for multiple clinical conditions were included.Main Outcomes and MeasuresDatabase study emulations focused on the primary outcome of the corresponding RCT. Findings of database studies were compared with RCTs using predefined metrics, including Pearson correlation coefficients and binary metrics based on statistical significance agreement, estimate agreement, and standardized difference.ResultsIn these highly selected RCTs, the overall observed agreement between the RCT and the database emulation results was a Pearson correlation of 0.82 (95% CI, 0.64-0.91), with 75% meeting statistical significance, 66% estimate agreement, and 75% standardized difference agreement. In a post hoc analysis limited to 16 RCTs with closer emulation of trial design and measurements, concordance was higher (Pearson r, 0.93; 95% CI, 0.79-0.97; 94% meeting statistical significance, 88% estimate agreement, 88% standardized difference agreement). Weaker concordance occurred among 16 RCTs for which close emulation of certain design elements that define the research question (PICOT) with data from insurance claims was not possible (Pearson r, 0.53; 95% CI, 0.00-0.83; 56% meeting statistical significance, 50% estimate agreement, 69% standardized difference agreement).Conclusions and RelevanceReal-world evidence studies can reach similar conclusions as RCTs when design and measurements can be closely emulated, but this may be difficult to achieve. Concordance in results varied depending on the agreement metric. Emulation differences, chance, and residual confounding can contribute to divergence in results and are difficult to disentangle.
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Amiot, Mathilde, Laurent Mortier, Stéphane Dalle, Olivier Dereure, Sophie Dalac, Caroline Dutriaux, Marie Thérèse Leccia, et al. "When to stop immunotherapy for advanced melanoma: Emulation of target trials." Journal of Clinical Oncology 42, no. 16_suppl (June 1, 2024): 9521. http://dx.doi.org/10.1200/jco.2024.42.16_suppl.9521.
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9521 Background: Immune checkpoint inhibitors (ICI) have demonstrated their effectiveness with a 7.5-year overall survival (OS) close to 50% for advanced stages. The design of clinical trials allowed treatment until progression or toxicity, or for a maximum duration of two years. Prolonged follow-up of responders after cessation shows sustained response and a low risk of relapse in the months following cessation. As of yet, the optimal duration of anti-PD-1 therapy for metastatic melanoma remains unestablished. The objective of this work was to evaluate the optimal duration of ICI. Methods: We conducted emulated trials using the cloning, weighting and censoring approach. Each emulated trial aimed at comparing the causal effect of stopping versus continuing ICI at a specific timepoint, in patients still under treatment and with disease control at that time. Results: The study comprised 1017 participants to the MELBASE cohort. Results of the 6-month discontinuation emulated trial showed a significantly lower OS if treatment was discontinued, compared to continuing treatment for at least three months. The 48-month survival difference was 37.8% (95% confidence interval [CI] 19.8 to 60.5), and the corresponding restricted mean survival time difference 8.3 months (95% CI: 4.1 to 12.7). The 12-month and 18-month discontinuation emulated trials both showed no evidence of benefit of either discontinuing or continuing ICI at any of those timepoints. The 24-month discontinuation emulated trial results were more in favor of stopping compared to continuing treatment at that decision point, with an absolute 48-month survival 10.5% higher (95% CI 4.4 to 18.1). Conclusions: These results suggest that a one-year course of immunotherapy is both necessary and sufficient for patients with advanced melanoma. Prolonged treatment beyond 2 years does not appear to be beneficial in terms of survival and could even be detrimental.[Table: see text]
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Zheng, Yingye, Paul D. Wagner, Amit G. Singal, Samir M. Hanash, Sudhir Srivastava, Ying Huang, Ying-Qi Zhao, et al. "Designing Rigorous and Efficient Clinical Utility Studies for Early Detection Biomarkers." Cancer Epidemiology, Biomarkers & Prevention 33, no. 9 (September 3, 2024): 1150–57. http://dx.doi.org/10.1158/1055-9965.epi-23-1594.
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Abstract Before implementing a biomarker in routine clinical care, it must demonstrate clinical utility by leading to clinical actions that positively affect patient-relevant outcomes. Randomly controlled early detection utility trials, especially those targeting mortality endpoint, are challenging due to their high costs and prolonged duration. Special design considerations are required to determine the clinical utility of early detection assays. This commentary reports on discussions among the National Cancer Institute’s Early Detection Research Network investigators, outlining the recommended process for carrying out single-organ biomarker-driven clinical utility studies. We present the early detection utility studies in the context of phased biomarker development. We describe aspects of the studies related to the features of biomarker tests, the clinical context of endpoints, the performance criteria for later phase evaluation, and study size. We discuss novel adaptive design approaches for improving the efficiency and practicality of clinical utility trials. We recommend using multiple strategies, including adopting real-world evidence, emulated trials, and mathematical modeling to circumvent the challenges in conducting early detection utility trials.
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Stewart, R. "How can electronic health records serve as a tool for clinical trials?" European Psychiatry 67, S1 (April 2024): S38. http://dx.doi.org/10.1192/j.eurpsy.2024.135.
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AbstractIncreasing volumes of information are being collected via electronic health records and there is growing multi-site expertise in utlising these for research. This emerging field of healthcare data science is not only concerned with the technical challenges associated with complex data, but also with the need for effective security and governance in the use of sensitive information with robust structures for stakeholder input and guidance. To date, most of the focus has been on supporting observational cohort studies nested within clinical records data - particularly investigating research questions around treatment response and course/prognosis. It is likely that electronic health records will become increasingly integrated with clinical trials, providing opportunities for pre-study feasibility scoping, targeted recruitment, and enhanced and extended follow-up. In addition, there is interest in emulated trials using routine data. For mental health data science, key challenges lie in the quality and quantity of data made accessible, with a particular need for natural language processing to derive structured data from extensive clinical text. Many of the challenges have been addressed for observational research, creating exciting prospects for a transformed trials landscape.Disclosure of InterestR. Stewart Grant / Research support from: Janssen, GSK, Takeda
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Christiaens, Antoine, Noémie Simon-Tillaux, Wade Thompson, Alan J. Sinclair, Séverine Henrard, Benoit B. Boland, Yannis Slaouti-Jégou, et al. "Impact of deintensifying hypoglycaemic drugs in older adults with type 2 diabetes: protocol for an emulation of a target trial." BMJ Open 13, no. 11 (November 2023): e073081. http://dx.doi.org/10.1136/bmjopen-2023-073081.
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IntroductionIn older adults with type 2 diabetes (T2D), overtreatment with hypoglycaemic drugs (HDs: sulfonylureas, glinides and/or insulins) is frequent and associated with increased 1-year mortality. Deintensification of HD is thus a key issue, for which evidence is though limited. The primary objective of this study will be to estimate the effect of deintensifying HD on clinical outcomes (hospital admission or death) within 3 months in older adults (≥75 years) with T2D.MethodsWe will emulate with real-world data a target trial, within The Health Improvement Network cohort, a large-scale database of data collected from electronic medical records of 2000 general practitioners in France. From 1 January 2010 to 28 February 2019, we will include eligible patients ≥75 years who will have T2D, a stable dose of HDs, glycated haemoglobin A1c (HbA1c) value <75 mmol/mol (9.0%) and no deintensification in the past year. The target trial will be sequentially emulated (ie, eligibility assessed) every month in the database. Patients will be classified at baseline of each sequential trial in the intervention arm (deintensification of HDs: decrease of ≥50% in the total dose of HDs, including complete cessation) or control arm (no deintensification of HDs). The pooled dataset for all sequential emulated trials will be analysed. The primary outcome will be time to first occurrence of hospital admission or death, within 3 months. Secondary outcomes will be hospitalisation, death, appropriateness of glycaemic control and occurrence of HbA1c >75 mmol/mol within 1 year. Participants will be followed from baseline to 12 months after randomisation, administrative censoring, or death, whichever occurs first. A pooled logistic regression will be used to estimate the treatment effect on the incidence of the outcomes.Dissemination and ethicsNo ethical approval is needed for using retrospectively this fully anonymised database. The results will be disseminated during conferences and through publications in scientific journals.
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Heyard, Rachel, Leonhard Held, Sebastian Schneeweiss, and Shirley V. Wang. "Design differences and variation in results between randomised trials and non-randomised emulations: meta-analysis of RCT-DUPLICATE data." BMJ Medicine 3, no. 1 (February 2024): e000709. http://dx.doi.org/10.1136/bmjmed-2023-000709.
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ObjectiveTo explore how design emulation and population differences relate to variation in results between randomised controlled trials (RCT) and non-randomised real world evidence (RWE) studies, based on the RCT-DUPLICATE initiative (Randomised, Controlled Trials Duplicated Using Prospective Longitudinal Insurance Claims: Applying Techniques of Epidemiology).DesignMeta-analysis of RCT-DUPLICATE data.Data sourcesTrials included in RCT-DUPLICATE, a demonstration project that emulated 32 randomised controlled trials using three real world data sources: Optum Clinformatics Data Mart, 2004-19; IBM MarketScan, 2003-17; and subsets of Medicare parts A, B, and D, 2009-17.Eligibility criteria for selecting studiesTrials where the primary analysis resulted in a hazard ratio; 29 RCT-RWE study pairs from RCT-DUPLICATE.ResultsDifferences and variation in effect sizes between the results from randomised controlled trials and real world evidence studies were investigated. Most of the heterogeneity in effect estimates between the RCT-RWE study pairs in this sample could be explained by three emulation differences in the meta-regression model: treatment started in hospital (which does not appear in health insurance claims data), discontinuation of some baseline treatments at randomisation (which would have been an unusual care decision in clinical practice), and delayed onset of drug effects (which would be under-reported in real world clinical practice because of the relatively short persistence of the treatment). Adding the three emulation differences to the meta-regression reduced heterogeneity from 1.9 to almost 1 (absence of heterogeneity).ConclusionsThis analysis suggests that a substantial proportion of the observed variation between results from randomised controlled trials and real world evidence studies can be attributed to differences in design emulation.
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Wu, Chi-Shin, Albert C. Yang, Shu-Sen Chang, Chia-Ming Chang, Yi-Hung Liu, Shih-Cheng Liao, and Hui-Ju Tsai. "Validation of Machine Learning-Based Individualized Treatment for Depressive Disorder Using Target Trial Emulation." Journal of Personalized Medicine 11, no. 12 (December 7, 2021): 1316. http://dx.doi.org/10.3390/jpm11121316.
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This study aims to develop and validate the use of machine learning-based prediction models to select individualized pharmacological treatment for patients with depressive disorder. This study used data from Taiwan’s National Health Insurance Research Database. Patients with incident depressive disorders were included in this study. The study outcome was treatment failure, which was defined as psychiatric hospitalization, self-harm hospitalization, emergency visits, or treatment change. Prediction models based on the Super Learner ensemble were trained separately for the initial and the next-step treatments if the previous treatments failed. An individualized treatment strategy was developed for selecting the drug with the lowest probability of treatment failure for each patient as the model-selected regimen. We emulated clinical trials to estimate the effectiveness of individualized treatments. The area under the curve of the prediction model using Super Learner was 0.627 and 0.751 for the initial treatment and the next-step treatment, respectively. Model-selected regimens were associated with reduced treatment failure rates, with a 0.84-fold (95% confidence interval (CI) 0.82–0.86) decrease for the initial treatment and a 0.82-fold (95% CI 0.80–0.83) decrease for the next-step. In emulation of clinical trials, the model-selected regimen was associated with a reduced treatment failure rate.
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Kjellsson, Sara, Kristiina Rajaleid, and Bitte Modin. "Using emulated clinical trials to investigate the risk of being diagnosed with psychiatric ill health following the cancer diagnosis of a sibling." PLOS ONE 19, no. 4 (April 18, 2024): e0298175. http://dx.doi.org/10.1371/journal.pone.0298175.
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Background The sibling bond is often the longest relationship in an individual’s life, spanning both good and bad times. Focusing on the latter, we investigated whether a cancer diagnosis in one adult sibling is predictive of psychiatric illness in the other, and if any such effect differs according the ‘sociodemographic closeness’ between the siblings in terms of sex, age, education, marital status and residence. Methods We used hospital records to identify psychiatric diagnoses (2005–2019) in a Swedish total-population cohort born in 1953, and cancer diagnoses (2005–2017) in their full siblings. By means of emulated clinical trials, the cohort member’s risk of a diagnosis within two years following a first exposure (or non-exposure) to a sibling’s cancer was analyzed through Cox regression. Results Exposed cohort members had a higher risk of psychiatric diagnosis than unexposed (HR = 1.15; CI: 1.08–1.23), with men displaying a higher risk (1.19; CI: 1.09–1.31) than women (HR = 1.11; CI: 1.01–1.22). Sub-analyses of the exposed group showed that women with a cancer-stricken sister had a higher risk of adverse psychiatric outcomes (HR = 1.31; CI: 1.07–1.61) than women with a cancer-stricken brother. Furthermore, unmarried cohort members ran a higher risk, both when the cancer-stricken sibling was married (HR = 2.03; CI: 1.67–2.46) and unmarried (HR = 2.61; CI: 2.16–3.15), than in cases where both siblings were married. No corresponding difference were detected for ‘closeness’ in age, education and residence. Conclusions In line with theories of linked lives, our findings suggest that negative events in one sibling’s life tend to ‘spill over’ on the other sibling’s wellbeing, at least during the 15-year-long period leading up to retirement age.
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Oh, Tae Ryom. "Integrating predictive modeling and causal inference for advancing medical science." Childhood Kidney Diseases 28, no. 3 (October 31, 2024): 93–98. http://dx.doi.org/10.3339/ckd.24.018.
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Artificial intelligence (AI) is revolutionizing healthcare by providing tools for disease prediction, diagnosis, and patient management. This review focuses on two key AI methodologies in healthcare: predictive modeling and causal inference. Predictive models excel in identifying patterns to forecast outcomes but are limited in explaining the underlying causes. In contrast, causal inference focuses on understanding cause-and-effect relationships, which makes effective medical interventions possible. Although randomized controlled trials (RCTs) are the gold standard for causal inference, they face limitations including cost and ethical concerns. As alternatives, emulated RCTs and advanced machine learning techniques have emerged for estimating causal effects, bridging the gap between prediction and causality. Additionally, Shapley values and Local Interpretable Model-Agnostic Explanations improve the interpretability of complex AI models, making them more actionable in clinical settings. Integrating prediction and causal inference holds great promise for advancing personalized medicine, enhancing patient outcomes, and optimizing healthcare delivery. However, careful application of AI tools is crucial to avoid misinterpretation and maximize their potential.
Dissertations / Theses on the topic "Emulated clinical trials"
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Jochum, Floriane. "From studies of disparities in healthcare access to emulated clinical trials : the multifaceted applications of real-world data to women's health in oncology." Electronic Thesis or Diss., Université Paris sciences et lettres, 2024. http://www.theses.fr/2024UPSLS033.
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Les données de vie réelle, recueillies lors des prestations des soins de santé, offrent des éléments de preuves essentielles sur les pratiques et les résultats cliniques en oncologie. Leur analyse permet de répondre à des questions complexes et souvent inaccessibles par les essais cliniques traditionnels, notamment en raison de contraintes pratiques, éthiques ou financières. Cette thèse explore l’utilisation des données de vie réelle pour aborder les défis en oncologie chez la femme, mettant en lumière l’importance de ces données dans la compréhension des disparités d’accès aux soins, l’évaluation des politiques publiques en santé, et la réalisation d’études de comparaison d’efficacité par essai émulé. Grâce à l’analyse de données exhaustives du Système National des Données de Santé (SNDS), couvrant 98,8% de la population française, trois axes principaux sont développés : i) l’examen approfondi des disparités d’accès aux soins et des obstacles structurels, à travers une analyse exhaustive de l’impact du genre et des facteurs socio-environnementaux sur l’accès aux soins oncologiques, ii) l’évaluation de l’efficacité des politiques de santé publique, en prenant comme exemple l’analyse de l’influence des essais cliniques majeurs en oncologie gynécologique sur les pratiques réelles et les barrières à leur adoption, et iii) la mise en œuvre d’études de comparaison d’efficacité via l’inférence causale et l’émulation d’essais cliniques. Ce dernier axe est l’axe pivot de cette thèse et confirme que les données de vie réelle, combinées à des techniques d’inférence causale, permettent d’émuler des essais cliniques de manière fiable, offrant ainsi une alternative robuste et complémentaire aux essais randomisés traditionnels. Cette méthodologie permet de limiter les biais inhérents aux études observationnelles, tels que les biais de confusion, de sélection et d’immortalité. Ce travail souligne le potentiel des données de vie réelle pour améliorer l’accès aux soins et les pratiques cliniques en oncologie, tout en appelant à l’utilisation de méthodologies rigoureuses pour surmonter les défis associés aux données de vie réelleReal-world data, routinely collected during the delivery of healthcare, provide essential evidence on practice and clinical outcomes in oncology. Their analysis enables the answering of complex questions that are often beyond the reach of traditional clinical trials, particularly due to practical, ethical or financial constraints. This paper explores the use of real-world data to address challenges in women’s oncology, highlighting the importance of these data in understanding inequalities in access to care, evaluating public health policies, and conducting comparative effectiveness research through trial emulation. By comprehensively analysing data from the National Health Data System (SNDS), which covers 98.8% of the French population, three main areas are developed: i) an in-depth study of inequalities in access to healthcare and structural barriers, through an exhaustive analysis of the impact of gender and socio-environmental factors on access to oncological care, ii) the evaluation of the effectiveness of public health policies, using as an example the analysis of the impact of major clinical trials in gynaecological oncology on real-world practice and the barriers to their adoption, and iii) the implementation of comparative effectiveness studies through causal inference and clinical trial emulation. This final axis is the central focus of this thesis and confirms that real-world data, combined with causal inference techniques, can reliably emulate clinical trials, thus providing a robust and complementary alternative to traditional randomized trials. This work highlights the potential of real-world data to improve access to care and clinical practice in oncology, while calling for the use of rigorous methodologies to overcome the challenges associated with real-world data
Book chapters on the topic "Emulated clinical trials"
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Cerrito, Patricia, and John Cerrito. "Extracting Data from the National Inpatient Sample." In Advances in Medical Technologies and Clinical Practice, 69–93. IGI Global, 2010. http://dx.doi.org/10.4018/978-1-61520-905-7.ch005.
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In the other type of health care database that we discuss in this chapter, there are multiple columns for each patient observation. It is more difficult to find both the most frequently occurring codes, or to find patients with specific codes for the purpose of extraction. For this reason, many studies focus on the primary diagnosis or procedure. We will provide the programming necessary to find the most frequent codes and to find the patients who have a specific condition. Another aspect of preprocessing we will explore in this chapter using the National Inpatient Sample is that of propensity scoring. When it is not possible to perform a randomized, controlled trial, an attempt is made to emulate such a trial by comparing two observational subgroups. The two groups are matched based upon demographic factors and related patient conditions. It is possible to define a level of patient severity and then to match patients with the severity level as part of the propensity score.
Conference papers on the topic "Emulated clinical trials"
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Durfee, William, Saeed Hashemi, and Andrew Ries. "Hydraulic Ankle Foot Orthosis Emulator for Children With Cerebral Palsy." In BATH/ASME 2020 Symposium on Fluid Power and Motion Control. American Society of Mechanical Engineers, 2020. http://dx.doi.org/10.1115/fpmc2020-2791.
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Abstract Objective: Ankle foot orthoses (AFOs) are used by nearly 50% of children with cerebral palsy (CP) to ameliorate gait impairments. The methods used to prescribe and tune the mechanical properties of an AFO, including its angular stiffness about the ankle, are based on the intuition and experience of the practitioner. The long-term goal of this research is to develop and deploy a technology-based solution to prescribing passive AFOs that uses an AFO emulator to be used in the clinic that can, under computer control, vary its stiffness in real-time to determine the best stiffness for walking. The objective of this project was to design and bench-test a first-generation wearable hydraulic ankle exoskeleton, and to conduct a small clinical trial to determine whether walking in a conventional plastic AFO was the same as walking in the hydraulic exoskeleton whose stiffness was programmed to match that of the conventional AFO. Methods: The hydraulic ankle exoskeleton was comprised of a wearable ankle exoskeleton tethered by small-diameter hydraulic hose to a push-behind cart that contained the hydraulic power supply and control components. The ankle component contained a novel double-ended cylinder with a cable anchored to the piston. The system was controlled to emulate a rotary spring. Bench top tests were performed to validate the performance of the system. In addition, an early feasibility clinical trial was conducted with five children with cerebral palsy who walked in three conventional AFOs (flexible, medium and stiff) and the hydraulic AFO controlled to match each stiffness. Kinematics and dynamics of gait were measured with a 12-camera motion capture system and a force plate. Results: The weight of the wearable exoskeleton plus shoe was 1.5 kg, 60% over the design goal. The system, running at a rail pressure of 141 bar (2,050 psi), could produce 62 Nm of torque and could emulate springs from 1 to 4.6 Nm/deg, the stiffness range of most conventional AFOs. Once calibrated, the torque-displacement properties were similar to the matched conventional AFO. Walking metrics were the same for hydraulic and conventional AFOs. Interpretation: Small-scale hydraulics are effective for a wearable exoskeleton that is designed to mimic a passive AFO and hydraulics can be used to emulate a rotary stiffness. While heavier than the design target, the added weight of the hydraulic system did not seem to impact walking in a significant way. The metrics used to evaluate walking were not sensitive enough to detect any subtle differences between walking with the hydraulic system and walking in a normal AFO.
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Ibsen, Eric M., and Jeffrey L. Kumer. "Abstract 418: A novel animal study software application emulates clinical trials by enabling the conduct of multi-center, asynchronous pre-clinical trials." In Proceedings: AACR 106th Annual Meeting 2015; April 18-22, 2015; Philadelphia, PA. American Association for Cancer Research, 2015. http://dx.doi.org/10.1158/1538-7445.am2015-418.
Full textReports on the topic "Emulated clinical trials"
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Gupta, Tejpal, Riddhijyoti Talukdar, Sadhana Kannan, Archya Dasgupta, Abhishek Chatterjee, and Vijay Patil. Meta-Analysis of Standard Temozolomide versus Extended Adjuvant Temozolomide following concurrent Radiochemotherapy in newly-diagnosed Glioblastoma (MASTER-G). INPLASY - International Platform of Registered Systematic Review and Meta-analysis Protocols, December 2021. http://dx.doi.org/10.37766/inplasy2021.12.0114.
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Review question / Objective: To assess the safety and efficacy of extended adjuvant temozolomide compared to standard adjuvant temozolomide after concurrent radiochemotherapy in patients with newly-diagnosed glioblastoma. Condition being studied: Newly-diagnosed glioblastoma. Eligibility criteria: Prospective clinical trials randomly assigning patients to extended (>6-cycles) adjuvant TMZ (experimental arm) or standard (6-cycles) adjuvant TMZ will be included. Randomization in an individual study may have been done upfront before concurrent phase (RT/TMZ), after completion of concurrent RT/TMZ and before starting adjuvant phase, or after completion of standard adjuvant TMZ (6-cycles). Emulated RCTs, quasi-randomized trials, propensity matched analyses, non-randomized comparative studies, or observational studies will not be considered in this review.