scholarly journals CP2 - USING REAL WORLD DATA TO EXTRAPOLATE EVIDENCE FROM RANDOMIZED CLINICAL TRIALS

2018 ◽  
Vol 21 ◽  
pp. S5
Author(s):  
S.V. Wang ◽  
S. Schneeweiss ◽  
J.J. Gagne ◽  
T. Evers ◽  
C. Gerlinger ◽  
...  
Keyword(s):  
Clinical Trials ◽  
Real World ◽  
Randomized Clinical Trials ◽  
Real World Data ◽  
World Data

scholarly journals Validating Direct Oral Anticoagulants (DOAC) for Use in Children By the Throm-PED Doac Registry of the International Pediatric Thrombosis Network

Blood ◽  
2021 ◽  
Vol 138 (Supplement 1) ◽  
pp. 1063-1063
Author(s):  
Susanne Holzhauer ◽  
Christoph Male ◽  
Paul Monagle ◽  
Mohammadreza Bordbar ◽  
Heleen van Ommen ◽  
...  
Keyword(s):  
Clinical Trials ◽  
Renal Disease ◽  
Real World ◽  
Randomized Clinical Trials ◽  
Chronic Medical Conditions ◽  
Medical Conditions ◽  
Efficacy And Safety ◽  
Real World Data ◽  
World Data ◽  
International Pediatric

Abstract Background: A growing number of randomized clinical trials (RCTs) provide promising data on pharmacokinetics/ pharmacodynamics, efficacy and safety of DOAC in children and the first DOACs have recently been approved for treatment of thromboembolism in children. Based on the risk benefit profiles published so far, we expect DOACs to be widely used for treatment and prophylaxis of thrombosis in children. The strict inclusion criteria for participation in the RCTs limit their generalizability, particularly to those with more serious acute and chronic medical conditions that account for a significant proportion of pediatric VTE patients in clinical practice. Real world data complements evidence from randomized clinical trials, and is urgently needed to improve patient care. Aim: To obtain efficacy and safety data of DOACs in a large and heterogeneous pediatric patient collectives. To expand the knowledge on treatment strategies and outcomes across different risk profiles and comorbidities including cancer and renal disease. Study Design: An international, multicentre, prospective observational cohort study imbedded in the Throm-PED Registry of the International Pediatric Thrombosis Network (IPTN). Parameters included at baseline are age, gender, height, weight, thrombosis type, thrombosis location, risk factors, underlying medical conditions and comedication. Primary outcomes include 1) thrombus progression or recurrence and 2) bleeding (major, clinically relevant non major including menorrhagia) and 3) mortality. Outcomes are assessed every 3 months for a total of 12 months. Additional variables include self-reported adherence, comedication, chemotherapy, DOAC drug levels, measures of renal function and dose adjustments on children with chronic medical conditions including renal disease and cancer. Study population : Patients from 0-21 years with thromboembolic disease treated with DOACs who are enrolled in the IPTN Throm-PED Registry. Results: As of June 30, 2021 82 patients from 10 centers have been enrolled. The majority of patients were at least 12 years old (Figure 1). 66% suffered from venous thrombosis. With about 30% each, pulmonary embolism or thrombosis of the lower extremities were reported most commonly. In this cohort the majority of patients received Rivaroxaban (80%), followed by Apixaban and Edoxaban. Risk factors were manifold, including patients with cancer (13%) and renal disease (4%). Of the 33 patients with follow-up reported at 3 months, bleeding was reported in 3 (9%) of patients during treatment with a DOAC, and thrombosis progression was reported in 2 (6%) (Table 1). Summary: This initial real world data, as expected, demonstrates reduced efficacy and more bleeding than noted in published trials, however numbers are small. Further real world data collection is essential to identify specific patient groups at risk of worse outcome with DOACs and to understand drug interactions and dose adjustments. Data on younger age groups are required. IPTN registry is an important framework to collect real world data that can then be explored in clinical trials. Figure 1 Figure 1. Disclosures Raffini: CSL Behring: Consultancy; Genentech: Consultancy; HEMA Biologics: Consultancy; Bayer: Consultancy; XaTek: Consultancy.

scholarly journals Health Canada & Canadian Society for Pharmaceutical Sciences - Use of Real World Data/Evidence to Inform Regulatory Decision Making

2020 ◽  
Vol 23 ◽  
pp. 1s-47s
Author(s):  
Real World Data Workshop Group CSPS/Health Canada
Keyword(s):  
Health Care ◽  
Decision Making ◽  
Clinical Trials ◽  
Real World ◽  
Randomized Clinical Trials ◽  
Regulatory Agencies ◽  
Real World Data ◽  
World Data ◽  
Regulatory Decision ◽  
Health Canada

Real world data (RWD) and real world evidence (RWE) are playing increasing roles in health-care decisions. Real world data are routinely employed to support reimbursement and coverage decisions for drugs and devices. More recently, clinical trials incorporating pragmatic designs and observational studies are considered to supplement traditional clinical trials (e.g., randomized clinical trials). Regulatory agencies and large co-operative groups including academia and industry are exploring whether leveraging big databases such as electronic medical records and claims databases can be used to garner clinical insights extending beyond those gained from randomized controlled studies. Whether RWE can ultimately replace or improve traditional clinical trials is the big question. The workshop held on December 3, 2019 at Health Canada included presenters from regulatory agencies, industry and academia. Health Canada, US FDA and European Medicine Agency presented current thinking, draft frameworks and guidance available in the public domain. While the three agencies might be at different stages of utilizing RWE for regulatory decision making, the consensus is not whether RWE would be used but when and how it can be incorporated into regulatory decision making while maintaining a high evidentiary bar. The complexity of data sourcing, curating databases, aligning on common data models, illustrated by high-profile work conducted as part of Sentinel, DSEN, OHDSI and Duke-Margolis initiatives, was presented and discussed during the workshop, creating great learning opportunities for the attendees. The design and analysis of RWE studies were compared and contrasted to those of RCTs. While there are gaps, they are closing quickly as novel analytical methods are employed and innovative ways of curating data, including natural language processing and artificial intelligence, are explored.   This proceeding contains summaries of information presented by the speakers, including current highlights about the use of RWE in regulatory decision making. In the world where the uptake of “big data” in everyday life is happening at unprecedented speed, we can expect RWE to be a fast-moving area and with the potential for big impact in health-care decision making in the years to come.

scholarly journals Health Canada & Canadian Society for Pharmaceutical Sciences - Use of Real World Data/Evidence to Inform Regulatory Decision Making

2020 ◽  
Vol 23 ◽  
pp. 1s-47s
Author(s):  
Real World Data Workshop Group CSPS/Health Canada
Keyword(s):  
Health Care ◽  
Decision Making ◽  
Clinical Trials ◽  
Real World ◽  
Randomized Clinical Trials ◽  
Regulatory Agencies ◽  
Real World Data ◽  
World Data ◽  
Regulatory Decision ◽  
Health Canada

Real world data (RWD) and real world evidence (RWE) are playing increasing roles in health-care decisions. Real world data are routinely employed to support reimbursement and coverage decisions for drugs and devices. More recently, clinical trials incorporating pragmatic designs and observational studies are considered to supplement traditional clinical trials (e.g., randomized clinical trials). Regulatory agencies and large co-operative groups including academia and industry are exploring whether leveraging big databases such as electronic medical records and claims databases can be used to garner clinical insights extending beyond those gained from randomized controlled studies. Whether RWE can ultimately replace or improve traditional clinical trials is the big question. The workshop held on December 3, 2019 at Health Canada included presenters from regulatory agencies, industry and academia. Health Canada, US FDA and European Medicine Agency presented current thinking, draft frameworks and guidance available in the public domain. While the three agencies might be at different stages of utilizing RWE for regulatory decision making, the consensus is not whether RWE would be used but when and how it can be incorporated into regulatory decision making while maintaining a high evidentiary bar. The complexity of data sourcing, curating databases, aligning on common data models, illustrated by high-profile work conducted as part of Sentinel, DSEN, OHDSI and Duke-Margolis initiatives, was presented and discussed during the workshop, creating great learning opportunities for the attendees. The design and analysis of RWE studies were compared and contrasted to those of RCTs. While there are gaps, they are closing quickly as novel analytical methods are employed and innovative ways of curating data, including natural language processing and artificial intelligence, are explored.   This proceeding contains summaries of information presented by the speakers, including current highlights about the use of RWE in regulatory decision making. In the world where the uptake of “big data” in everyday life is happening at unprecedented speed, we can expect RWE to be a fast-moving area and with the potential for big impact in health-care decision making in the years to come.

Using real-world cohorts to assess the generalizability and relevance of randomized clinical trials (RCTs).

2019 ◽  
Vol 37 (15_suppl) ◽  
pp. 6540-6540 ◽  
Author(s):  
Caroline Savage Bennette ◽  
Nathan Coleman Nussbaum ◽  
Melissa D. Curtis ◽  
Neal J. Meropol
Keyword(s):  
Clinical Trials ◽  
Real World ◽  
Randomized Clinical Trials ◽  
Treatment Options ◽  
Metastatic Breast ◽  
Standard Of Care ◽  
Real World Data ◽  
Eligibility Criteria ◽  
Relevance Score ◽  
World Data

6540 Background: RCTs are the gold standard for understanding the efficacy of new treatments, however, patients (pts) in RCTs often differ from those treated in the real-world. Further, selecting a standard of care (SOC) arm is challenging as treatment options may evolve during the course of a RCT. Our objective was to assess the generalizability and relevance of RCTs supporting recent FDA approvals of anticancer therapies. Methods: RCTs were identified that supported FDA approvals of anticancer therapies (1/1/2016 - 4/30/2018). Relevant pts were selected from the Flatiron Health longitudinal, EHR-derived database, where available. Two metrics were calculated: 1) a trial’s pt generalizability score (% of real-world pts receiving treatment consistent with the control arm therapy for the relevant indication who actually met the trial's eligibility criteria) and 2) a trial’s SOC relevance score (% of real-world pts with the relevant indication and meeting the trial's eligibility criteria who actually received treatment consistent with the control arm therapy). All analyses excluded real-world pts treated after the relevant trial’s enrollment ended. Results: 14 RCTs across 5 cancer types (metastatic breast, advanced non-small cell lung cancer, metastatic renal cell carcinoma, multiple myeloma, and advanced urothelial) were included. There was wide variation in the SOC relevance and pt generalizability scores. The median pt generalizability score was 63% (range 35% - 88%), indicating that most real-world pts would have met the RCT eligibility criteria. The median SOC relevance score was 37% (range 15% - 74%), indicating that most RCT control arms did not reflect the way trial-eligible real-world pts in the US were actually treated. Conclusions: There is great variability across recent RCTs in terms of pt generalizability and relevance of SOC arms. Real-world data can be used to inform selection of control arms, predict impact of inclusion/exclusion criteria, and also assess the generalizability of the results of completed trials. Incorporating real-world data in planning and interpretation of prospective clinical trials could improve accrual and enhance relevance of RCT outcomes.

scholarly journals Real-World Data for Planning Eligibility Criteria and Enhancing Recruitment: Recommendations from the Clinical Trials Transformation Initiative

2021 ◽  
Author(s):  
Scott R. Evans ◽  
Dianne Paraoan ◽  
Jane Perlmutter ◽  
Sudha R. Raman ◽  
John J. Sheehan ◽  
...  
Keyword(s):  
Clinical Trials ◽  
Real World ◽  
Real World Data ◽  
Eligibility Criteria ◽  
World Data ◽  
Study Planning ◽  
Efficiency And Effectiveness ◽  
Multi Stakeholder ◽  
Functional Teams ◽  
Data Driven Decisions

AbstractThe growing availability of real-world data (RWD) creates opportunities for new evidence generation and improved efficiency across the research enterprise. To varying degrees, sponsors now regularly use RWD to make data-driven decisions about trial feasibility, based on assessment of eligibility criteria for planned clinical trials. Increasingly, RWD are being used to support targeted, timely, and personalized outreach to potential trial participants that may improve the efficiency and effectiveness of the recruitment process. This paper highlights recommendations and resources, including specific case studies, developed by the Clinical Trials Transformation Initiative (CTTI) for applying RWD to planning eligibility criteria and recruiting for clinical trials. Developed through a multi-stakeholder, consensus- and evidence-driven process, these actionable tools support researchers in (1) determining whether RWD are fit for purpose with respect to study planning and recruitment, (2) engaging cross-functional teams in the use of RWD for study planning and recruitment, and (3) understanding patient and site needs to develop successful and patient-centric approaches to RWD-supported recruitment. Future considerations for the use of RWD are explored, including ensuring full patient understanding of data use and developing global datasets.

scholarly journals LDL-cholesterol change and goal attainment following statin intensity titration among Asians in primary care: a retrospective cohort study

2021 ◽  
Vol 20 (1) ◽  
Author(s):  
Hao Sen Andrew Fang ◽  
Qiao Gao ◽  
Mong Li Lee ◽  
Wynne Hsu ◽  
Ngiap Chuan Tan
Keyword(s):  
Primary Care ◽  
Clinical Trials ◽  
Cohort Study ◽  
Real World ◽  
Retrospective Cohort Study ◽  
Retrospective Cohort ◽  
Goal Attainment ◽  
Real World Data ◽  
World Data ◽  
Asian Patients

Abstract Background Clinical trials have demonstrated that either initiating or up-titrating a statin dose substantially reduce Low-Density Lipoprotein-Cholesterol (LDL-C) levels. However, statin adherence in actual practice tends to be suboptimal, leading to diminished effectiveness. This study aims to use real-world data to determine the effect on LDL-C levels and LDL-C goal attainment rates, when selected statins are titrated in Asian patients. Methods A retrospective cohort study over a 5-year period, from April 2014 to March 2019 was conducted on a cohort of multi-ethnic adult Asian patients with clinical diagnosis of Dyslipidaemia in a primary care clinic in Singapore. The statins were classified into low-intensity (LI), moderate-intensity (MI) and high-intensity (HI) groups according to the 2018 American College of Cardiology and American Heart Association (ACC/AHA) Blood Cholesterol Guidelines. Patients were grouped into “No statin”, “Non-titrators” and “Titrators” cohorts based on prescribing patterns. For the “Titrators” cohort, the mean percentage change in LDL-C and absolute change in LDL-C goal attainment rates were computed for each permutation of statin intensity titration. Results Among the cohort of 11,499 patients, with a total of 266,762 visits, there were 1962 pairs of LDL-C values associated with a statin titration. Initiation of LI, MI and HI statin resulted in a lowering of LDL-C by 21.6% (95%CI = 18.9–24.3%), 28.9% (95%CI = 25.0–32.7%) and 25.2% (95%CI = 12.8–37.7%) respectively. These were comparatively lower than results from clinical trials (30 to 63%). The change of LDL-C levels due to up-titration, down-titration, and discontinuation were − 12.4% to − 28.9%, + 13.2% to + 24.6%, and + 18.1% to + 32.1% respectively. The improvement in LDL-C goal attainment ranged from 26.5% to 47.1% when statin intensity was up-titrated. Conclusion In this study based on real-world data of Asian patients in primary care, it was shown that although statin titration substantially affected LDL-C levels and LDL-C goal attainment rates, the magnitude was lower than results reported from clinical trials. These results should be taken into consideration and provide further insight to clinicians when making statin adjustment recommendations in order to achieve LDL-C targets in clinical practice, particularly for Asian populations.

scholarly journals Emulated Clinical Trials from Longitudinal Real-World Data Efficiently Identify Candidates for Neurological Disease Modification: Examples from Parkinson’s Disease

2020 ◽  
Author(s):  
Daphna Laifenfeld ◽  
Chen Yanover ◽  
Michal Ozery-Flato ◽  
Oded Shaham ◽  
Michal Rozen-Zvi ◽  
...  
Keyword(s):  
Parkinson’S Disease ◽  
Parkinson's Disease ◽  
Clinical Trials ◽  
Real World ◽  
Late Onset ◽  
Real World Data ◽  
World Data ◽  
Healthcare Data ◽  
Beneficial Effects ◽  
Disease Modifying

AbstractReal-world healthcare data hold the potential to identify therapeutic solutions for progressive diseases by efficiently pinpointing safe and efficacious repurposing drug candidates. This approach circumvents key early clinical development challenges, particularly relevant for neurological diseases, concordant with the vision of the 21stCentury Cures Act. However, to-date, these data have been utilized mainly for confirmatory purposes rather than as drug discovery engines. Here, we demonstrate the usefulness of real-world data in identifying drug repurposing candidates for disease-modifying effects, specifically candidate marketed drugs that exhibit beneficial effects on Parkinson’s disease (PD) progression. We performed an observational study in cohorts of ascertained PD patients extracted from two large medical databases, Explorys SuperMart (N=88,867) and IBM MarketScan Research Databases (N=106,395); and applied two conceptually different, well-established causal inference methods to estimate the effect of hundreds of drugs on delaying dementia onset as a proxy for slowing PD progression. Using this approach, we identified two drugs that manifested significant beneficial effects on PD progression in both datasets: rasagiline, narrowly indicated for PD motor symptoms; and zolpidem, a psycholeptic. Each confers its effects through distinct mechanisms, which we explored via a comparison of estimated effects within the drug classification ontology. We conclude that analysis of observational healthcare data, emulating otherwise costly, large, and lengthy clinical trials, can highlight promising repurposing candidates, to be validated in prospective registration trials, for common, late-onset progressive diseases for which disease-modifying therapeutic solutions are scarce.

The rate of tumor growth, g, as a biomarker for overall survival (OS) in prostate cancer (PC) in clinical trials as well as in real-world data from the Veterans Administration Medical Centers (VAMCs).

2019 ◽  
Vol 37 (15_suppl) ◽  
pp. 5074-5074
Author(s):  
Harshraj Leuva ◽  
Mengxi Zhou ◽  
Julia Wilkerson ◽  
Keith Sigel ◽  
Ta-Chueh Hsu ◽  
...  
Keyword(s):  
Prostate Cancer ◽  
Clinical Trial ◽  
Overall Survival ◽  
Clinical Trials ◽  
Tumor Growth ◽  
Real World ◽  
Clinical Trial Data ◽  
Trial Data ◽  
Real World Data ◽  
World Data

5074 Background: Novel assessments of efficacy are needed to improve determination of treatment outcomes in clinical trials and in real-world settings. Methods: Cancer treatments usually lead to concurrent regression and growth of the drug-sensitive and drug-resistant fractions of a tumor, respectively. We have exploited novel methods of analysis that assess these two simultaneous processes and have estimated rates of tumor growth ( g) and regression ( d) in over 30,000 patients (pts) with diverse tumors. Results: In prostate cancer (PC) we have analyzed both clinical trial and real-world data from Veterans. Using clinical trial data from 6819 pts enrolled in 15 treatment arms we have established separately and by combining all the data that g correlates highly (p<0.0001) with overall survival (OS) – slower g associated with better OS. In PC, abiraterone (ABI) and docetaxel (DOC) are superior to placebo, prednisone and mitoxantrone. ABI (median g =0.0017) is superior to DOC ( g=0.0021) in first line (p=0.0013); and ABI in 2nd line ( g=0.0034) is inferior to ABI in 1st line ( g=0.0017; p<0.0001). Finally, using combined clinical trial data as a benchmark we could assess the efficacy of novel therapies in as few as 30-40 patients. Amongst 7457 Veterans, the median g on a taxane ( g=0.0022) was similar to that from clinical trials ( g=0.0012). Although only 258 Veterans received cabazitaxel (CAB), g values for CAB ( g=0.0018) and DOC ( g=0.0023) were indistinguishable (p=0.3) consistent with their identical mechanism of action. Finally, outcomes with DOC in African American (AA) ( g=0.00212) and Caucasian ( g=0.00205) Veterans were indistinguishable (p=0.9) and comparable across all VAMCs. Conclusions: The rate of tumor growth, g, is an excellent biomarker for OS both in clinical trials and in real-world settings. g allows comparisons between trials and for large trial data sets to be used as benchmarks of efficacy. Real-world outcomes in the VAMCs are similar to those in clinical trials. In the egalitarian VAMCs DOC efficacy in PC is comparable in AA and Caucasian Veterans -- indicating inferior outcomes reported in AAs are likely due to differential health care access, not differences in biology.

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