The Role of Patient Level Data In Assessing Health Economic Value: A Case Study Using Edge and The Core Diabetes Model

e20521 Background: The randomized clinical trial (RCT) is the gold standard in drug development. However, for indications where patients have a strong preference for the investigational product, such as many oncology and rare diseases, the use of a SCA may improve drug development and reduce patient burden. SCA is an external control constructed from patient-level data from previous clinical trials to match the baseline characteristics of the patients in an investigational group and can augment a single-arm trial or a RCT compromised by control arm early withdrawal or noncompliance in order to estimate treatment effects. This research explores whether the treatment effect (difference between arms) based on an SCA can mimic the treatment effect from a RCT. Tipping point analyses were explored to assess the impact of unobserved confounders on the SCA-based demonstration of efficacy. Methods: This case study is based on patient-level data from previous clinical trials in R/R MM. The SCA patients satisfied key eligibility criteria of the target RCT and were further selected using propensity score methods to balance the baseline characteristics in the SCA with the target randomized treatment group (TRT) from the original RCT. Sensitivity analyses utilizing methods proposed by Lin (1998) illustrate the robustness of the treatment effect to unobserved covariate(s). Results: Comparable balance was achieved in observed baseline characteristics between SCA and the matched patients from TRT. The treatment effect utilizing SCA is similar to the original RCT. The Kaplan Meier curve of overall survival for the SCA overlaps with that of the randomized control and the quantified differences between SCA and the matched patients from TRT are very similar to the original RCT. Tipping point analyses show changes in HRs under representative sets of assumptions regarding the unobserved confounder (results not shown). Conclusions: This case study demonstrates an SCA built from previous clinical trials, can be well-balanced at baseline with TRT and can provide similar treatment effect estimates as a RCT. Tipping point analyses can elucidate whether treatment effects are reliable despite a reasonable degree of confounding expected in a clinical setting. This suggests, in some settings, SCA can be used to augment or replace a randomized control in future trials without loss of understanding of the treatment effect. [Table: see text]

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Correlating Cost Effectiveness Output with Patient Level Data Input Via the IMS Core Diabetes Model (CDM)

Value in Health ◽

10.1016/j.jval.2013.08.1622 ◽

2013 ◽

Vol 16 (7) ◽

pp. A587

Author(s):

P. McEwan ◽

V. Foos ◽

J.L. Palmer ◽

M. Lamotte ◽

D. Grant

Keyword(s):

Cost Effectiveness ◽

Data Input ◽

Patient Level Data ◽

Patient Level ◽

Level Data ◽

Diabetes Model

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Long-term Disability Outcomes in Patients Treated With Teriflunomide for up to 14 Years: Group- and Patient-Level Data from the Phase 2 Extension Study

10.26226/morressier.59a3edacd462b8028d89544e ◽

2017 ◽

Author(s):

Mark S. Freedman

Keyword(s):

Extension Study ◽

Phase 2 ◽

Patient Level Data ◽

Patient Level ◽

Level Data

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Faculty Opinions recommendation of Survival benefit of primary prevention implantable cardioverter-defibrillator therapy after myocardial infarction: does time to implant matter? A meta-analysis using patient-level data from 4 clinical trials.

Faculty Opinions – Post-Publication Peer Review of the Biomedical Literature ◽

10.3410/f.717980033.793479806 ◽

2013 ◽

Author(s):

Robert Sheldon ◽

Tarek Hatoum

Keyword(s):

Myocardial Infarction ◽

Clinical Trials ◽

Primary Prevention ◽

Survival Benefit ◽

Meta Analysis ◽

Implantable Cardioverter Defibrillator Therapy ◽

Patient Level Data ◽

Cardioverter Defibrillator ◽

Patient Level ◽

Level Data

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Amalgamation of cloud-based colonoscopy videos with patient-level metadata to facilitate large-scale machine learning

Endoscopy International Open ◽

10.1055/a-1326-1289 ◽

2021 ◽

Vol 09 (02) ◽

pp. E233-E238

Author(s):

Rajesh N. Keswani ◽

Daniel Byrd ◽

Florencia Garcia Vicente ◽

J. Alex Heller ◽

Matthew Klug ◽

...

Keyword(s):

Machine Learning ◽

Large Scale ◽

Medical Center ◽

Academic Medical Center ◽

Procedure Time ◽

Image Capture ◽

Common Procedure ◽

Patient Level Data ◽

Patient Level ◽

Level Data

Abstract Background and study aims Storage of full-length endoscopic procedures is becoming increasingly popular. To facilitate large-scale machine learning (ML) focused on clinical outcomes, these videos must be merged with the patient-level data in the electronic health record (EHR). Our aim was to present a method of accurately linking patient-level EHR data with cloud stored colonoscopy videos. Methods This study was conducted at a single academic medical center. Most procedure videos are automatically uploaded to the cloud server but are identified only by procedure time and procedure room. We developed and then tested an algorithm to match recorded videos with corresponding exams in the EHR based upon procedure time and room and subsequently extract frames of interest. Results Among 28,611 total colonoscopies performed over the study period, 21,170 colonoscopy videos in 20,420 unique patients (54.2 % male, median age 58) were matched to EHR data. Of 100 randomly sampled videos, appropriate matching was manually confirmed in all. In total, these videos represented 489,721 minutes of colonoscopy performed by 50 endoscopists (median 214 colonoscopies per endoscopist). The most common procedure indications were polyp screening (47.3 %), surveillance (28.9 %) and inflammatory bowel disease (9.4 %). From these videos, we extracted procedure highlights (identified by image capture; mean 8.5 per colonoscopy) and surrounding frames. Conclusions We report the successful merging of a large database of endoscopy videos stored with limited identifiers to rich patient-level data in a highly accurate manner. This technique facilitates the development of ML algorithms based upon relevant patient outcomes.

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