scholarly journals Setting the IMPACT (IMProve Access to Clinical Trial data) Observatory baseline

2018 ◽  
Vol 28 (1) ◽  
Author(s):  
Mersiha Mahmić-Kaknjo ◽  
Josip Šimić ◽  
Karmela Krleža-Jerić
Keyword(s):  
Clinical Trial ◽  
Clinical Trial Data ◽  
Trial Data ◽  
The Impact ◽  
Improve Access

scholarly journals The impact of patient-reported outcome (PRO) data from clinical trials: a systematic review and critical analysis

2019 ◽  
Vol 17 (1) ◽  
Author(s):  
Samantha Cruz Rivera ◽  
Derek G. Kyte ◽  
Olalekan Lee Aiyegbusi ◽  
Anita L. Slade ◽  
Christel McMullan ◽  
...  
Keyword(s):  
Systematic Review ◽  
Clinical Trial ◽  
Clinical Trials ◽  
Case Studies ◽  
Real World ◽  
Clinical Trial Data ◽  
Research Impact ◽  
Trial Data ◽  
Patient Reported ◽  
The Impact

Abstract Background Patient-reported outcomes (PROs) are commonly collected in clinical trials and should provide impactful evidence on the effect of interventions on patient symptoms and quality of life. However, it is unclear how PRO impact is currently realised in practice. In addition, the different types of impact associated with PRO trial results, their barriers and facilitators, and appropriate impact metrics are not well defined. Therefore, our objectives were: i) to determine the range of potential impacts from PRO clinical trial data, ii) identify potential PRO impact metrics and iii) identify barriers/facilitators to maximising PRO impact; and iv) to examine real-world evidence of PRO trial data impact based on Research Excellence Framework (REF) impact case studies. Methods Two independent investigators searched MEDLINE, EMBASE, CINAHL+, HMIC databases from inception until December 2018. Articles were eligible if they discussed research impact in the context of PRO clinical trial data. In addition, the REF 2014 database was systematically searched. REF impact case studies were included if they incorporated PRO data in a clinical trial. Results Thirty-nine publications of eleven thousand four hundred eighty screened met the inclusion criteria. Nine types of PRO trial impact were identified; the most frequent of which centred around PRO data informing clinical decision-making. The included publications identified several barriers and facilitators around PRO trial design, conduct, analysis and report that can hinder or promote the impact of PRO trial data. Sixty-nine out of two hundred nine screened REF 2014 case studies were included. 12 (17%) REF case studies led to demonstrable impact including changes to international guidelines; national guidelines; influencing cost-effectiveness analysis; and influencing drug approvals. Conclusions PRO trial data may potentially lead to a range of benefits for patients and society, which can be measured through appropriate impact metrics. However, in practice there is relatively limited evidence demonstrating directly attributable and indirect real world PRO-related research impact. In part, this is due to the wider challenges of measuring the impact of research and PRO-specific issues around design, conduct, analysis and reporting. Adherence to guidelines and multi-stakeholder collaboration is essential to maximise the use of PRO trial data, facilitate impact and minimise research waste. Trial registration Systematic Review registration PROSPERO CRD42017067799.

The impact of immunotherapy education on GI cancers.

2021 ◽  
Vol 39 (3_suppl) ◽  
pp. 479-479
Author(s):  
Kinjal Parikh ◽  
Katie Lucero ◽  
Charlotte Warren ◽  
Emily Sherene Van Laar ◽  
Patrick Kugel ◽  
...  
Keyword(s):  
Clinical Trial ◽  
Checkpoint Inhibitors ◽  
Statistical Significance ◽  
Clinical Trial Data ◽  
Trial Data ◽  
Educational Activities ◽  
Cancer Subtypes ◽  
Gi Cancers ◽  
The Impact

479 Background: Gastrointestinal (GI) cancers are a heterogeneous group of cancers with varying underlying pathophysiology and distinct treatment paradigms. Immunotherapy (IO) is unique in each of the subtypes and biomarkers utility varies. With the expansion of IO in each cancer subtypes, education remains essential to optimize patient outcomes through integration of the latest evidence-based data at point of care. Through the partnership between Medscape Oncology and the Society for Immunotherapy of Cancer, 2 educational activities were designed to increase the knowledge and competence of oncologists surrounding the role of IO in patients with advanced GI cancers. Methods: The 2 educational activities included a text based online activity with 3 chapters focused on gastroesophageal cancers, colorectal cancers, and hepatocellular carcinoma (HCC) and a 30-minute online, video discussion with 3 faculty and synchronized slides on HCC. Educational effectiveness was assessed with repeated paired pre/post assessment where learners served as their own controls. A chi-square test was used to identify statistical significance in proportion of correct responses. The first activity launched 11/27/2019 and the second activity launched on 5/8/20. Data were collected and reported through 8/25/2020. Results: A total of 8433 learners, including 1543 oncologists, participated from 11/2019 through 8/2020. Participation in education resulted in significant relative improvements among oncologist learners on IO in GI cancers in (n = 641): 110%: role or eligibility of immune checkpoint inhibitors (ICIs) (p < .001) 38%: clinical trial data of ICIs (p < .001) Subsequent education on unresectable HCC demonstrated a significant relative improvement in both knowledge and competence for oncologist learners (n = 902): 19%: regarding clinical trial data in unresectable HCC (p = .073) 19%; competence identifying role of ICIs in unresectable HCC (p < .05) 59%: competence managing irAEs in unresectable HCC (p < .001). Conclusions: These 2 online CME-certified educational activities resulted in statistically significant gains in oncologist knowledge surrounding the use of IO in advanced GI cancers Follow-up education on HCC demonstrated the value and benefit of multi-modal and sequential activities on improving competence among oncologists caring for patients with unresectable HCC There remains a need for continuous education as more oncologists utilize IO in their practice while the understanding and availability of clinical data continues to expand and evolve in the varying GI cancer subtypes. More than 50% of learners continued to demonstrate a need in understanding the clinical trial data or role of IO in metastatic GI cancers with more than 40% of the learners demonstrating continued need on clinical trial data or role of IO in HCC specifically.

scholarly journals 488 The impact of education on novel concepts in metastatic melanoma: triplet therapy

2020 ◽  
Vol 8 (Suppl 3) ◽  
pp. A524-A524
Author(s):  
Kinjal Parikh ◽  
Sara Fagerlie ◽  
Patrick Kugel ◽  
Richard Caracio ◽  
Ryan Sullivan
Keyword(s):  
Clinical Trial ◽  
Checkpoint Inhibitors ◽  
Panel Discussion ◽  
Clinical Trial Data ◽  
Advanced Melanoma ◽  
Trial Data ◽  
Educational Activity ◽  
List Type ◽  
The Impact ◽  
Post Assessment

BackgroundAdvanced melanoma treatment selection is guided by BRAF-mutation status and patient and disease-specific factors. Historically, oncologists decided between targeted therapy or immune checkpoint inhibitors (ICI). However, given the differences in onset of activity, response durability, and adverse events combination BRAF/MEK inhibitors and ICI (triplet therapy) are being evaluated to optimize outcomes. With several trials due to report, oncologists need education to stay up-to-date on the available data and contextualize this potential treatment option.MethodsAn online continuing education (CME) activity consisting of a multi-media 30-minute video panel discussion explored the rationale, available clinical trial data, and future directions of triplet therapy for the treatment of advanced BRAF-mutated melanoma. Educational effect was assessed using a repeated pairs pre-assessment/post-assessment study design and compared the pre- and post-assessment responses. A chi-square test was used to identify differences between pre- and post-assessment responses. Effect size was calculated using Cramer’s V test by determining the strength of the association between the activity and the outcomes (V = 0.16–0.26 is considerable and V > 0.26 is extensive). P values were calculated and those < 0.05 were considered statistically significant. Data from oncologist participants were collected between 12/23/2019 through 2/26/20.ResultsParticipation in education resulted in statistically significant improvements and noticeable educational effect for oncologists (n=49; p < 0.05, V =0.136). • 39% of pre-assessment questions were correctly answered increasing to 52% post-assessment • 15% of oncologists had a measurable improvement in confidence regarding the rationale for use of triplet therapy in advanced melanoma• Significant improvement in knowledge regarding clinical trial data in triplet therapy was observed (35% vs. 55%; p < 0.05, V = 0.205)ConclusionsThis online, interactive, expert-led, CME-certified educational activity resulted in significant gains in oncologist knowledge and confidence regarding triplet therapy in the management of melanoma. These results demonstrate the effectiveness of on-demand education but also highlight an ongoing need for education on this topic as further data becomes available.AcknowledgementsThis educational initiative was supported through educational grants from Novartis Pharmaceuticals Corporation and GenentechReferenceSullivan RJ, Salama AKS. Managing Melanoma: Emerging Concepts of Triplet Therapy. https://www.medscape.org/viewarticle/923003

scholarly journals Clinical Trial Data Sharing: A Cross-Sectional Study of Outcomes Associated with Two NIH Models

2021 ◽  
Author(s):  
Anisa Rowhani-Farid ◽  
Alexander C. Egilman ◽  
Audrey D. Zhang ◽  
Cary P. Gross ◽  
Harlan M. Krumholz ◽  
...  
Keyword(s):  
Clinical Trial ◽  
Data Sharing ◽  
Clinical Trial Data ◽  
Cross Sectional Study ◽  
Trial Data ◽  
Cross Sectional ◽  
Shared Data ◽  
Using Data ◽  
Meta Analyses ◽  
The Impact

AbstractBackgroundThe impact and value of clinical trial data sharing, including the number and quality of publications that result from shared data – “shared data publications” – may differ depending on the data sharing model used.MethodsWe characterized the outcomes associated with two data sharing models previously used by Institutes of the U.S. National Institutes of Health (NIH): NHLBI’s centralized model, which uses a repository to manage data sharing requests, and NCI’s decentralized model, which entrusted research groups to independently manage data sharing requests. We identified trials completed in 2010 that met NIH data sharing criteria and matched studies sponsored by each Institute based on cost or size, determining whether trial data were shared and the frequency of shared data publications.ResultsWe identified 14 NHLBI-funded trials and 48 NCI-funded trials that met NIH data sharing criteria. We matched 14 NCI-funded trials to the 14 NHLBI-funded trials; among these, 4 NHLBI-sponsored trials (29%) and 2 NCI-sponsored trials (14%) shared data. From the 2 NCI-sponsored trials sharing data, we identified 2 shared data publications, one per trial, both of which were meta-analyses. From the 4 NHLBI-sponsored trials sharing data, we identified 7 shared data publications, all using data from 1 trial, 5 of which were pooled analyses and 2 reported secondary outcomes.ConclusionWhen characterizing the outcomes associated with two NIH data sharing models, both the NHLBI and the NCI models resulted in only 21% of trials sharing data and few shared data publications. There are opportunities to optimize clinical trial data sharing efforts both to enhance clinical trial data sharing and increase the number of shared data publications.

THE IMPACT OF ELECTRONIC CLINICAL OUTCOME ASSESSMENTS (ECOA) ON ALZHEIMER’S DISEASE CLINICAL TRIAL DATA QUALITY

2017 ◽  
Vol 13 (7) ◽  
pp. P935
Author(s):  
H. Todd Feaster ◽  
Todd M. Solomon ◽  
Danielle Abi-Saab ◽  
Annamarie Vogt ◽  
Jordan M. Barbone ◽  
...  
Keyword(s):  
Alzheimer’S Disease ◽  
Alzheimer's Disease ◽  
Clinical Trial ◽  
Clinical Outcome ◽  
Data Quality ◽  
Clinical Trial Data ◽  
Trial Data ◽  
Outcome Assessments ◽  
Clinical Outcome Assessments ◽  
The Impact

scholarly journals Optimizing the synthesis of clinical trial data using sequential trees

2020 ◽  
Author(s):  
Khaled El Emam ◽  
Lucy Mosquera ◽  
Chaoyi Zheng
Keyword(s):  
Clinical Trial ◽  
Synthetic Data ◽  
Particle Swarm ◽  
Clinical Trial Data ◽  
Trial Data ◽  
Variable Order ◽  
Optimization Approach ◽  
Hinge Loss ◽  
High Utility ◽  
The Impact

Abstract Objective With the growing demand for sharing clinical trial data, scalable methods to enable privacy protective access to high-utility data are needed. Data synthesis is one such method. Sequential trees are commonly used to synthesize health data. It is hypothesized that the utility of the generated data is dependent on the variable order. No assessments of the impact of variable order on synthesized clinical trial data have been performed thus far. Through simulation, we aim to evaluate the variability in the utility of synthetic clinical trial data as variable order is randomly shuffled and implement an optimization algorithm to find a good order if variability is too high. Materials and Methods Six oncology clinical trial datasets were evaluated in a simulation. Three utility metrics were computed comparing real and synthetic data: univariate similarity, similarity in multivariate prediction accuracy, and a distinguishability metric. Particle swarm was implemented to optimize variable order, and was compared with a curriculum learning approach to ordering variables. Results As the number of variables in a clinical trial dataset increases, there is a pattern of a marked increase in variability of data utility with order. Particle swarm with a distinguishability hinge loss ensured adequate utility across all 6 datasets. The hinge threshold was selected to avoid overfitting which can create a privacy problem. This was superior to curriculum learning in terms of utility. Conclusions The optimization approach presented in this study gives a reliable way to synthesize high-utility clinical trial datasets.

scholarly journals Response-shift effects in neuromyelitis optica spectrum disorder: estimating response-shift-adjusted scores using equating

2021 ◽  
Author(s):  
Carolyn E. Schwartz ◽  
Roland B. Stark ◽  
Brian D. Stucky ◽  
Yuelin Li ◽  
Bruce D. Rapkin
Keyword(s):  
Clinical Trial ◽  
Neuromyelitis Optica ◽  
Clinical Trial Data ◽  
Response Shift ◽  
Trial Data ◽  
Spectrum Disorder ◽  
Neuromyelitis Optica Spectrum Disorder ◽  
Score Distribution ◽  
Shared Variance ◽  
The Impact

Abstract Background In our companion paper, random intercept models (RIMs) investigated response-shift effects in a clinical trial comparing Eculizumab to Placebo for people with neuromyelitis optica spectrum disorder (NMOSD). RIMs predicted Global Health using the EQ-5D Visual Analogue Scale item (VAS) to encompass broad criteria that people might consider. The SF36™v2 mental and physical component scores (MCS and PCS) helped us detect response shift in VAS. Here, we sought to “back-translate” the VAS into the MCS/PCS scores that would have been observed if response shift had not been present. Methods This secondary analysis utilized NMOSD clinical trial data evaluating the impact of Eculizumab in preventing relapses (n = 143). Analyses began by equating raw scores from the VAS, MCS, and PCS, and computing scores that removed response-shift effects. Correlation analysis and descriptive displays provided a more comprehensive examination of response-shift effects. Results MCS and PCS crosswalks with VAS equated the scores that include and exclude response-shift effects. These two sets of scores had low shared variance for MCS for both groups, suggesting that corresponding mental health constructs were substantially different. The shared variance contrast for physical health was distinct only for the Placebo group. The larger MCS response-shift effects were found at end of study for Placebo only and were more prominent at extremes of the MCS score distribution. Conclusions Our results reveal notable treatment group differences in MCS but not PCS response shifts, which can explain null results detected in previous work. The method introduced herein provides a way to provide further information about response-shift effects in clinical trial data.

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