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string(1656) "Background: Randomized controlled trials (RCTs) are the gold standard for evidence generation but are slow, expensive, and in certain circumstances ethically challenging. Real-world data (RWD) from electronic health records (EHR) could improve efficiency of RCTs for multiple use cases, including external control arms (ECAs) when randomization is not ethical/feasible, hybrid controls that collect RWD for patients in the trial, and/or RWD-based prognostic models that increase statistical power through covariate adjustment. However, FDA is rejecting RWD for primary evidence due to lack of demonstrated reliability - accuracy, completeness, and traceability – as outlined in FDA guidance.
Objective: This project will develop methods to utilize RWD in RCTs with quantified reliability (accuracy, completeness, and traceability) that is used to account for biases resulting from RWD data loss and transformation errors in comparative outcome analyses.
Study Design: Methodological research on integrated analyses of RCT and RWD.
Participants: RCTs will be selected from the YODA project and matched cohorts from RWD will be selected for each RCT from Droice Labs’ US hospital partners.
Primary and Secondary Outcome Measure(s): The same outcome measures from each selected RCT will be used in the RCT/RWD analysis.
Statistical Analysis: For each selected RCT, analysis will assess the replicability of the RCT inferences with RWD comparator arms and quantify the stability of inferences under measured misclassification in RWD using probabilistic quantitative bias analysis."
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string(3252) "RCTs are the gold standard for evidence generation but are highly resource intensive. If RWD from EHR could be used for evidence generation at scale, it could accelerate therapy development and improve care in wide-ranging therapeutic areas. For example, RWD external control arms (ECAs) can be used when randomization is not ethical/feasible [1–8]. For common conditions for which FDA requires RCTs, hybrid controls can capture major portions of data from RWD to reduce the cost and time burden of manual data collection [9–11], and prognostic disease progression models developed from RWD can be applied to increase statistical power through covariate adjustment [12].
However, there are major challenges in using EHR data for generating reliable inferences in clinical trials, because EHR data is highly messy and noisy compared to RCT data and typically requires extensive transformation to prepare it for analysis [1,2,4,13–16]. Transformation errors and data loss in RWD processing induce misclassifications and subsequent biases in the analysis data that can render it unreliable for critical regulatory decisions because the potential impact of such biases on study inferences cannot be quantitatively assessed [17]. Accordingly, FDA has been rejecting submissions using RWD because data reliability - accuracy, completeness, and traceability – has not been sufficiently demonstrated according to FDA guidance [6,13,17,18]. For RWD to be reliable, traceability to raw data across all transformations is required to validate the study data and quantify accuracy.
Droice SuperLineage is an element-level lineage solution that enables traceability and validation of RWD transformations. SuperLineage captures EHR in a lossless standardized format, which is critical for measuring information loss and error. SuperLineage is used to find and fix data loss and transformation errors and to measure transformation performance (e.g. sensitivity and sensitivity of computable phenotype algorithms [17]) so that error bounds can be quantitatively accounted for in study analyses. Droice Labs’ discussed SuperLineage with the FDA, where both FDA and Droice agreed that accuracy, completeness, and traceability are necessary for regulatory use of RWD [19]. Furthermore, Droice and CDISC, the body responsible for developing data submission standards for FDA, are building the RWD lineage metadata standard based on SuperLineage for FDA submissions [20,21].
In this project, patient-level data from cardiometabolic RCTs will be integrated with matched RWD cohorts from multiple US hospital partners of Droice Labs. SuperLineage will be used to quantify accuracy and completeness of RWD variables while maintaining required regulatory-grade traceability. For each selected RCT, the primary analysis will be replicated across the RCT and RWD study arms to evaluate whether the base inferences of the RCT are reproducible in the RWD with and without considering the error bound quantified through SuperLineage. Use of a large and diverse RCT dataset will permit evaluation of the generalizability and scalability of the approach to use validation data to quantify the reliability of inferences using RWD."
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Aim 2: Evaluate reproducibility of efficacy and safety endpoints across RCT and RWD arms for each selected RCT with and without considering the data reliability from Aim 1.
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string(1209) "Clinical trial data: Phase 2-4 RCT from the YODA project are selected for the following cardiometabolic conditions/indications: heart failure, myocardial infarction, heart valve disease, atrial fibrillation, stroke, chronic kidney disease, diabetes (type 1 and 2), obesity, hypertension (essential and pulmonary), NASH, and hyperlipidemia. All patients from each trial will be included (no exclusion criteria).
EHR data: Deidentified EHR data from hospitals and clinics relevant to the specific RCT study populations (i.e. that have populations with the condition under study/similar care environments to the RCT) from the Droice data warehouse will be used to select matched cohorts based on the inclusion/exclusion specific to each RCT. Droice will upload deidentified patient-level analysis data (i.e. CSVs with treatment status, baseline covariate values, and outcome status/values as columns) derived from EHR data to the YODA platform. Research use of the deidentified EHR data has been verified IRB exempt as non-human subjects research not requiring informed consent according to 45CFR46.104(d) (Solutions IRB).
Pooled IPD analysis will be performed on the YODA platform."
["project_main_outcome_measure"]=>
string(249) "For each selected RCT, the analysis will compare the same primary/secondary outcomes as the RCT for EHR-derived study cohorts, subject to the availability of the corresponding outcome data in the RWD source for a given EHR-derived comparator cohort."
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string(273) "For each selected RCT, the analysis will compare the same intervention/standard of care as the RCT for the EHR-derived study cohorts, subject to the availability of the corresponding intervention/standard of care in the RWD source for a given EHR-derived comparator cohort."
["project_other_variables_interest"]=>
string(235) "For each selected RCT, the analysis will utilize the same covariates as the RCT for the EHR-derived study cohorts, subject to the availability of the corresponding covariates in the RWD source for a given EHR-derived comparator cohort."
["project_stat_analysis_plan"]=>
string(4014) "For each selected RCT, analysis will include arms from the RCT and matched arms from RWD:
1) The treatment arm(s) from the RCT
2) The control arm from the RCT
3) Matched treatment arm(s) from RWD with patients exposed to the RCT intervention(s)
4) Matched control arm from RWD treated with the RCT SoC
Outcomes for treatments and standard of care will be compared across all arms:
1) RCT treatment(s) vs. RWD SoC to investigate clinical trial treatment efficacy vs. real-world SoC (representing an external control arm study)
2) RCT treatment(s) vs. RWD treatment(s) to investigate differential efficacy in RCT vs. real-world treatment
3) RWD treatment(s) vs. RWD SoC to investigate real-world treatment effectiveness
4) RCT SoC vs. RWD SoC to investigate differential efficacy of SoC in RCT vs. real-world SoC
RWD analysis data preparation and validation:
Variable definition: Multimodal EHR variable definitions incorporating NLP and cross-domain phenotyping algorithms will be developed to match the variables of the clinical trial arms, including inclusion/exclusion criteria, the exposure, covariates, and the primary outcome.
Variable implementation and analysis data generation: Study variables will be implemented on a base pool of potentially eligible patients that meet broad ‘screening criteria’ for each RCT to generate the analysis dataset.
EHR variable validation studies using SuperLineage: SuperLineage will be used to evaluate true and false positives and negatives to quantitatively measure misclassification of all study variables against source data.
All data preparation and validation for the EHR-derived study data will occur on the Droice platform, and the resulting deidentified analysis data and validation data (i.e. confusion matrices/sensitivity and specificity data for each study variable) will be uploaded to the YODA project platform for combined analysis with the RCT data.
Creation of RCT matched cohorts in RWD:
Inclusion/exclusion criteria for each RCT will be applied to RWD to generate an eligible patient pool for matching. Propensity score and Mahalanobis distance matching methods will be used to create matched cohorts between RCT and RWD cohorts [22–24].
Quantitative bias analysis (QBA) to evaluate inference robustness to EHR variable misclassification errors:
First, base inferences will be calculated to determine if RCT results are reproduced in RWD without considering misclassification in RWD. Then, with misclassification assumed negligible in the clinical trial data (due to rigorous data collection and quality assurance protocols), effects of EHR-derived variable misclassification will be systematically investigated through probabilistic quantitative bias analysis (QBA) [16,25–28] to determine the stability of the base inferences considering the measured misclassification (sensitivity and specificity) in EHR variables.
QBA will account for misclassification across all EHR-derived study variables, including:
Inclusion/exclusion criteria: misclassified inclusion/exclusion criteria can alter which patients are included in the study cohort, potentially imparting selection bias
Exposures: misclassifications in exposures change which arm (exposed or unexposed) an individual is assigned to, biasing the exposure-outcome relationship
Covariates: misclassifications in prognostic covariates distort the real profiles of confounding factors, which can bias the control for confounding, making covariate adjustment ineffective or inaccurate
Outcomes: misclassifications in outcomes can change the efficacy and safety profile across study groups, biasing the exposure-outcome relationship
Details of the probabilistic QBA methodology that will be used in this study are described in references [25] and [27].
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["project_timeline"]=>
string(146) "Estimated start date: October 2024
Estimated analysis completion: October 2025
Estimated manuscript submission: January 2026
"
["project_dissemination_plan"]=>
string(319) "This project is expected to result in conference presentations and at least one major publication in a journal targeting a broad audience interested in advancements in the use of RWD for enhancing clinical evidence generation, such as Nature Scientific Reports, JAMA Network Open, BMC Medical Research Methodology, etc."
["project_bibliography"]=>
string(6556) "
- Kim TE, Park SI, Shin KH. Incorporation of real-world data to a clinical trial: use of external controls. Transl Clin Pharmacol. 2022;30(3):121. doi:10.12793/TCP.2022.30.E14
- Yap TA, Jacobs I, Baumfeld Andre E, Lee LJ, Beaupre D, Azoulay L. Application of Real-World Data to External Control Groups in Oncology Clinical Trial Drug Development. Front Oncol. 2021;11:695936. doi:10.3389/fonc.2021.695936
- Zou KH, Vigna C, Talwai A, et al. The Next Horizon of Drug Development: External Control Arms and Innovative Tools to Enrich Clinical Trial Data. Ther Innov Regul Sci. 2024;58(3):443-455. doi:10.1007/s43441-024-00627-4
- Carrigan G, Bradbury BD, Brookhart · M Alan, et al. External Comparator Groups Derived from Real-world Data Used in Support of Regulatory Decision Making: Use Cases and Challenges. Current Epidemiology Reports 2022 9:4. 2022;9(4):326-337. doi:10.1007/S40471-022-00305-9
- Yin X, Mishra-Kalyan PS, Sridhara R, Stewart MD, Stuart EA, Davi RC. Exploring the Potential of External Control Arms created from Patient Level Data: A case study in non-small cell lung cancer. J Biopharm Stat. 2022;32(1):204-218. doi:10.1080/10543406.2021.2011901
- FDA. Designing Sound Clinical Trials That Incorporate Real-World Data | FDA. Accessed May 29, 2024. https://www.fda.gov/drugs/regulatory-science-action/designing-sound-clinical-trials-incorporate-real-world-data
- Jahanshahi M, Gregg K, Davis G, et al. The use of external controls in FDA regulatory decision making. Ther Innov Regul Sci. 2021;55(5):1019-1035. doi:10.1007/s43441-021-00302-y
- Jiao F, Chen YF, Min M, Jimenez S. Challenges and potential strategies utilizing external data for efficacy evaluation in small-sized clinical trials. J Biopharm Stat. 2022;32(1):21-33. doi:10.1080/10543406.2021.2011906
- Hampson L V., Izem R. Innovative Hybrid Designs and Analytical Approaches Leveraging Real-World Data and Clinical Trial Data. Real-World Evidence in Medical Product Development. Published online January 1, 2023:211-232. doi:10.1007/978-3-031-26328-6_12
- Zhu M, Sridhar S, Hollingsworth R, et al. Hybrid clinical trials to generate real-world evidence: design considerations from a sponsor’s perspective. Contemp Clin Trials. 2020;94. doi:10.1016/J.CCT.2019.105856
- Ventz S, Khozin S, Louv B, et al. The design and evaluation of hybrid controlled trials that leverage external data and randomization. Nature Communications 2022 13:1. 2022;13(1):1-11. doi:10.1038/s41467-022-33192-1
- Adjusting for Covariates in Randomized Clinical Trials for Drugs and Biological Products | FDA. Accessed June 9, 2024. https://www.fda.gov/regulatory-information/search-fda-guidance-documents/adjusting-covariates-randomized-clinical-trials-drugs-and-biological-products
- FDA. Considerations for the Use of Real-World Data and Real-World Evidence To Support Regulatory Decision-Making for Drug and Biological Products | FDA. Accessed May 29, 2024. https://www.fda.gov/regulatory-information/search-fda-guidance-documents/considerations-use-real-world-data-and-real-world-evidence-support-regulatory-decision-making-drug
- Liu F, Demosthenes P. Real-world data: a brief review of the methods, applications, challenges and opportunities. BMC Medical Research Methodology 2022 22:1. 2022;22(1):1-10. doi:10.1186/S12874-022-01768-6
- Gray CM, Grimson F, Layton D, Pocock S, Kim J. A Framework for Methodological Choice and Evidence Assessment for Studies Using External Comparators from Real-World Data. Drug Saf. 2020;43(7):623-633. doi:10.1007/S40264-020-00944-1
- Gray C, Ralphs E, Fox MP, et al. Use of quantitative bias analysis to evaluate single-arm trials with real-world data external controls. Pharmacoepidemiol Drug Saf. 2024;33(5). doi:10.1002/pds.5796
- FDA. Real-World Data: Assessing Electronic Health Records and Medical Claims Data To Support Regulatory Decision-Making for Drug and Biological Products | FDA. Accessed May 30, 2024. https://www.fda.gov/regulatory-information/search-fda-guidance-documents/real-world-data-assessing-electronic-health-records-and-medical-claims-data-support-regulatory
- FDA. Data Standards for Drug and Biological Product Submissions Containing Real-World Data | FDA. Accessed May 30, 2024. https://www.fda.gov/regulatory-information/search-fda-guidance-documents/data-standards-drug-and-biological-product-submissions-containing-real-world-data
- Droice Labs Discusses SuperLineage for Real-World Data Traceability at FDA Listening Session. Accessed May 29, 2024. https://finance.yahoo.com/news/droice-labs-discusses-superlineage-real-150000597.html?guccounter=1
- Nagamine T, Hume S. Transforming RWD for Regulatory Submissions: How to Use SDTM for RWD. In: PHUSE US Connect. ; 2023.
- RWD Lineage | CDISC. Accessed May 30, 2024. https://www.cdisc.org/rwd-lineage
- King G, Nielsen R. Why Propensity Scores Should Not Be Used for Matching. Political Analysis. 2019;27(4):435-454. doi:10.1017/PAN.2019.11
- Ripollone JE, Huybrechts KF, Rothman KJ, Ferguson RE, Franklin JM. Implications of the Propensity Score Matching Paradox in Pharmacoepidemiology. Am J Epidemiol. 2018;187(9):1951-1961. doi:10.1093/AJE/KWY078
- Ali MS, Prieto-Alhambra D, Lopes LC, et al. Propensity score methods in health technology assessment: principles, extended applications, and recent advances. Front Pharmacol. 2019;10:973. doi:10.3389/fphar.2019.00973
- Fox MP, MacLehose RF, Lash TL. Applying Quantitative Bias Analysis to Epidemiologic Data. Published online 2021. doi:10.1007/978-3-030-82673-4
- Petersen JM, Ranker LR, Barnard-Mayers R, Maclehose RF, Fox MP. A systematic review of quantitative bias analysis applied to epidemiological research. Int J Epidemiol. 2021;50(5):1708-1730. doi:10.1093/ije/dyab061
- Fox MP, Maclehose RF, Lash TL. SAS and R code for probabilistic quantitative bias analysis for misclassified binary variables and binary unmeasured confounders. Int J Epidemiol. 2023;52(5):1624-1633. doi:10.1093/ije/dyad053
- Thorlund K, Duffield S, Popat S, et al. Quantitative bias analysis for external control arms using real-world data in clinical trials: a primer for clinical researchers. J Comp Eff Res. 2024;13(3). doi:10.57264/cer-2023-0147
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General Information
How did you learn about the YODA Project?:
Colleague
Conflict of Interest
Request Clinical Trials
Associated Trial(s):
- NCT02065791 - A Randomized, Double-blind, Event-driven, Placebo-controlled, Multicenter Study of the Effects of Canagliflozin on Renal and Cardiovascular Outcomes in Subjects With Type 2 Diabetes Mellitus and Diabetic Nephropathy
- NCT01032629 - A Randomized, Multicenter, Double-Blind, Parallel, Placebo-Controlled Study of the Effects of JNJ-28431754 on Cardiovascular Outcomes in Adult Subjects With Type 2 Diabetes Mellitus
- NCT02243202 - A Randomized, Double-Blind, Placebo-Controlled, Parallel-Group Study to Investigate the Safety and Efficacy of the Co-administration of Canagliflozin 300 mg and Phentermine 15 mg Compared With Placebo for the Treatment of Non-diabetic Overweight and Obese Subjects
- NCT00236613 - A Randomized, Double-Blind, Placebo-Controlled, Multicenter, Parallel Group, Dose-Response Study to Assess the Efficacy and Safety of Topiramate in the Treatment of Patients With Obesity
- NCT00231608 - The Safety and Efficacy of Topiramate in Male Patients With Abdominal Obesity: A 6-Month Double-Blind, Randomized, Placebo-Controlled Study With a 6-Month Open-Label Extension
- NCT00231634 - A Multicenter, Randomized, Double-Blind, Placebo-Controlled, Parallel Group Study of the Efficacy and Safety of Topiramate in the Treatment of Obese, Type 2 Diabetic Patients Inadequately Controlled on Sulfonylurea Therapy
- NCT00231660 - A Multicenter, Randomized, Double-Blind, Placebo-Controlled, Parallel-Group Study of the Efficacy and Safety of Topiramate in the Treatment of Obese, Type 2 Diabetic Patients Treated With Metformin
- NCT00236626 - A 9 Month, Double-Blind, Placebo-Controlled Study With a Blinded Crossover Transition to Open-Label Extension, Evaluating the Safety and Effectiveness of Topiramate on Insulin Sensitivity in Overweight or Obese Type 2 Diabetes Patients
- NCT00231621 - A Multicenter, Randomized, Double-Blind, Placebo-Controlled, Parallel Group, One-year Study of the Efficacy and Safety of Topiramate in the Treatment of Obese Subjects With Dyslipidemia
- NCT00231647 - A Randomized, Double-Blind, Placebo-Controlled, Multicenter, Parallel-Group Study to Assess the Efficacy and Safety of Topiramate OROS Controlled-Release in the Treatment of Obese, Type 2 Diabetic Subjects Managed With Diet or Metformin
- NCT01989754 - A Randomized, Multicenter, Double-Blind, Parallel, Placebo-Controlled Study of the Effects of Canagliflozin on Renal Endpoints in Adult Subjects With Type 2 Diabetes Mellitus
- NCT00231530 - A Multicenter, Randomized, Double-Blind, Placebo-Controlled, Parallel-Group Study to Assess the Safety and Efficacy of Topiramate in the Treatment of Obese, Type 2 Diabetic Patients on a Controlled Diet
- NCT00231673 - A Double-Blind, Placebo-Controlled, Parallel Group Study to Evaluate the Effect of Topiramate on Electrophysiological Parameters in Subjects With Diabetic Peripheral Polyneuropathy
- NCT02025907 - A Randomized, Double-blind, Placebo Controlled, 2-arm, Parallel-group, 26-week, Multicenter Study to Evaluate the Efficacy, Safety, and Tolerability of Canagliflozin in the Treatment of Subjects With Type 2 Diabetes Mellitus With Inadequate Glycemic Control on Metformin and Sitagliptin Therapy
- NCT01340664 - A Randomized, Double-Blind, Placebo-Controlled, 3-Arm, Parallel-Group, Multicenter Study to Evaluate the Efficacy, Safety, and Tolerability of Canagliflozin in the Treatment of Subjects With Type 2 Diabetes Mellitus With Inadequate Glycemic Control on Metformin
- NCT01381900 - A Randomized, Double-Blind, Placebo-Controlled, Parallel Group, 18-Week Study to Evaluate the Efficacy, Safety, and Tolerability of Canagliflozin in the Treatment of Subjects With Type 2 Diabetes Mellitus With Inadequate Glycemic Control on Metformin Alone or in Combination With a Sulphonylurea
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What type of data are you looking for?:
Individual Participant-Level Data, which includes Full CSR and all supporting documentation
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Data Request Status
Status:
Ongoing
Research Proposal
Project Title:
Developing approaches to accelerate clinical trials using real world data (RWD) from electronic health records (EHR)
Scientific Abstract:
Background: Randomized controlled trials (RCTs) are the gold standard for evidence generation but are slow, expensive, and in certain circumstances ethically challenging. Real-world data (RWD) from electronic health records (EHR) could improve efficiency of RCTs for multiple use cases, including external control arms (ECAs) when randomization is not ethical/feasible, hybrid controls that collect RWD for patients in the trial, and/or RWD-based prognostic models that increase statistical power through covariate adjustment. However, FDA is rejecting RWD for primary evidence due to lack of demonstrated reliability - accuracy, completeness, and traceability -- as outlined in FDA guidance.
Objective: This project will develop methods to utilize RWD in RCTs with quantified reliability (accuracy, completeness, and traceability) that is used to account for biases resulting from RWD data loss and transformation errors in comparative outcome analyses.
Study Design: Methodological research on integrated analyses of RCT and RWD.
Participants: RCTs will be selected from the YODA project and matched cohorts from RWD will be selected for each RCT from Droice Labs' US hospital partners.
Primary and Secondary Outcome Measure(s): The same outcome measures from each selected RCT will be used in the RCT/RWD analysis.
Statistical Analysis: For each selected RCT, analysis will assess the replicability of the RCT inferences with RWD comparator arms and quantify the stability of inferences under measured misclassification in RWD using probabilistic quantitative bias analysis.
Brief Project Background and Statement of Project Significance:
RCTs are the gold standard for evidence generation but are highly resource intensive. If RWD from EHR could be used for evidence generation at scale, it could accelerate therapy development and improve care in wide-ranging therapeutic areas. For example, RWD external control arms (ECAs) can be used when randomization is not ethical/feasible [1--8]. For common conditions for which FDA requires RCTs, hybrid controls can capture major portions of data from RWD to reduce the cost and time burden of manual data collection [9--11], and prognostic disease progression models developed from RWD can be applied to increase statistical power through covariate adjustment [12].
However, there are major challenges in using EHR data for generating reliable inferences in clinical trials, because EHR data is highly messy and noisy compared to RCT data and typically requires extensive transformation to prepare it for analysis [1,2,4,13--16]. Transformation errors and data loss in RWD processing induce misclassifications and subsequent biases in the analysis data that can render it unreliable for critical regulatory decisions because the potential impact of such biases on study inferences cannot be quantitatively assessed [17]. Accordingly, FDA has been rejecting submissions using RWD because data reliability - accuracy, completeness, and traceability -- has not been sufficiently demonstrated according to FDA guidance [6,13,17,18]. For RWD to be reliable, traceability to raw data across all transformations is required to validate the study data and quantify accuracy.
Droice SuperLineage is an element-level lineage solution that enables traceability and validation of RWD transformations. SuperLineage captures EHR in a lossless standardized format, which is critical for measuring information loss and error. SuperLineage is used to find and fix data loss and transformation errors and to measure transformation performance (e.g. sensitivity and sensitivity of computable phenotype algorithms [17]) so that error bounds can be quantitatively accounted for in study analyses. Droice Labs' discussed SuperLineage with the FDA, where both FDA and Droice agreed that accuracy, completeness, and traceability are necessary for regulatory use of RWD [19]. Furthermore, Droice and CDISC, the body responsible for developing data submission standards for FDA, are building the RWD lineage metadata standard based on SuperLineage for FDA submissions [20,21].
In this project, patient-level data from cardiometabolic RCTs will be integrated with matched RWD cohorts from multiple US hospital partners of Droice Labs. SuperLineage will be used to quantify accuracy and completeness of RWD variables while maintaining required regulatory-grade traceability. For each selected RCT, the primary analysis will be replicated across the RCT and RWD study arms to evaluate whether the base inferences of the RCT are reproducible in the RWD with and without considering the error bound quantified through SuperLineage. Use of a large and diverse RCT dataset will permit evaluation of the generalizability and scalability of the approach to use validation data to quantify the reliability of inferences using RWD.
Specific Aims of the Project:
Aim 1: Develop high-quality EHR-derived RWD cohorts matched to RCT treatment and control arms for a variety of cardiometabolic indications. Data reliability (accuracy, completeness, and traceability) of all EHR-derived study variables (inclusion/exclusion criteria, exposures, covariates, and outcomes) will be quantitatively evaluated using Droice SuperLineage.
Aim 2: Evaluate reproducibility of efficacy and safety endpoints across RCT and RWD arms for each selected RCT with and without considering the data reliability from Aim 1.
Study Design:
Methodological research
What is the purpose of the analysis being proposed? Please select all that apply.:
New research question to examine treatment effectiveness on secondary endpoints and/or within subgroup populations
Participant-level data meta-analysis
Meta-analysis using data from the YODA Project and other data sources
Develop or refine statistical methods
Research on clinical trial methods
Software Used:
Python
Data Source and Inclusion/Exclusion Criteria to be used to define the patient sample for your study:
Clinical trial data: Phase 2-4 RCT from the YODA project are selected for the following cardiometabolic conditions/indications: heart failure, myocardial infarction, heart valve disease, atrial fibrillation, stroke, chronic kidney disease, diabetes (type 1 and 2), obesity, hypertension (essential and pulmonary), NASH, and hyperlipidemia. All patients from each trial will be included (no exclusion criteria).
EHR data: Deidentified EHR data from hospitals and clinics relevant to the specific RCT study populations (i.e. that have populations with the condition under study/similar care environments to the RCT) from the Droice data warehouse will be used to select matched cohorts based on the inclusion/exclusion specific to each RCT. Droice will upload deidentified patient-level analysis data (i.e. CSVs with treatment status, baseline covariate values, and outcome status/values as columns) derived from EHR data to the YODA platform. Research use of the deidentified EHR data has been verified IRB exempt as non-human subjects research not requiring informed consent according to 45CFR46.104(d) (Solutions IRB).
Pooled IPD analysis will be performed on the YODA platform.
Primary and Secondary Outcome Measure(s) and how they will be categorized/defined for your study:
For each selected RCT, the analysis will compare the same primary/secondary outcomes as the RCT for EHR-derived study cohorts, subject to the availability of the corresponding outcome data in the RWD source for a given EHR-derived comparator cohort.
Main Predictor/Independent Variable and how it will be categorized/defined for your study:
For each selected RCT, the analysis will compare the same intervention/standard of care as the RCT for the EHR-derived study cohorts, subject to the availability of the corresponding intervention/standard of care in the RWD source for a given EHR-derived comparator cohort.
Other Variables of Interest that will be used in your analysis and how they will be categorized/defined for your study:
For each selected RCT, the analysis will utilize the same covariates as the RCT for the EHR-derived study cohorts, subject to the availability of the corresponding covariates in the RWD source for a given EHR-derived comparator cohort.
Statistical Analysis Plan:
For each selected RCT, analysis will include arms from the RCT and matched arms from RWD:
1) The treatment arm(s) from the RCT
2) The control arm from the RCT
3) Matched treatment arm(s) from RWD with patients exposed to the RCT intervention(s)
4) Matched control arm from RWD treated with the RCT SoC
Outcomes for treatments and standard of care will be compared across all arms:
1) RCT treatment(s) vs. RWD SoC to investigate clinical trial treatment efficacy vs. real-world SoC (representing an external control arm study)
2) RCT treatment(s) vs. RWD treatment(s) to investigate differential efficacy in RCT vs. real-world treatment
3) RWD treatment(s) vs. RWD SoC to investigate real-world treatment effectiveness
4) RCT SoC vs. RWD SoC to investigate differential efficacy of SoC in RCT vs. real-world SoC
RWD analysis data preparation and validation:
Variable definition: Multimodal EHR variable definitions incorporating NLP and cross-domain phenotyping algorithms will be developed to match the variables of the clinical trial arms, including inclusion/exclusion criteria, the exposure, covariates, and the primary outcome.
Variable implementation and analysis data generation: Study variables will be implemented on a base pool of potentially eligible patients that meet broad 'screening criteria' for each RCT to generate the analysis dataset.
EHR variable validation studies using SuperLineage: SuperLineage will be used to evaluate true and false positives and negatives to quantitatively measure misclassification of all study variables against source data.
All data preparation and validation for the EHR-derived study data will occur on the Droice platform, and the resulting deidentified analysis data and validation data (i.e. confusion matrices/sensitivity and specificity data for each study variable) will be uploaded to the YODA project platform for combined analysis with the RCT data.
Creation of RCT matched cohorts in RWD:
Inclusion/exclusion criteria for each RCT will be applied to RWD to generate an eligible patient pool for matching. Propensity score and Mahalanobis distance matching methods will be used to create matched cohorts between RCT and RWD cohorts [22--24].
Quantitative bias analysis (QBA) to evaluate inference robustness to EHR variable misclassification errors:
First, base inferences will be calculated to determine if RCT results are reproduced in RWD without considering misclassification in RWD. Then, with misclassification assumed negligible in the clinical trial data (due to rigorous data collection and quality assurance protocols), effects of EHR-derived variable misclassification will be systematically investigated through probabilistic quantitative bias analysis (QBA) [16,25--28] to determine the stability of the base inferences considering the measured misclassification (sensitivity and specificity) in EHR variables.
QBA will account for misclassification across all EHR-derived study variables, including:
Inclusion/exclusion criteria: misclassified inclusion/exclusion criteria can alter which patients are included in the study cohort, potentially imparting selection bias
Exposures: misclassifications in exposures change which arm (exposed or unexposed) an individual is assigned to, biasing the exposure-outcome relationship
Covariates: misclassifications in prognostic covariates distort the real profiles of confounding factors, which can bias the control for confounding, making covariate adjustment ineffective or inaccurate
Outcomes: misclassifications in outcomes can change the efficacy and safety profile across study groups, biasing the exposure-outcome relationship
Details of the probabilistic QBA methodology that will be used in this study are described in references [25] and [27].
Narrative Summary:
The use of real-world data (RWD) from electronic health records (EHR) in clinical trials stands to significantly improve the speed and efficiency of developing new therapies. However, data quality/reliability issues in EHR-derived study data can lead to biases that confound the exposure-outcome relationship and limit comparability with clinical trial data. This project will develop methods to leverage RWD in randomized controlled trials (RCT) while accounting for RWD data quality/reliability issues by using a novel data traceability solution to measure error in EHR-derived data quantitatively evaluate impact of biases in comparative outcome analyses.
Project Timeline:
Estimated start date: October 2024
Estimated analysis completion: October 2025
Estimated manuscript submission: January 2026
Dissemination Plan:
This project is expected to result in conference presentations and at least one major publication in a journal targeting a broad audience interested in advancements in the use of RWD for enhancing clinical evidence generation, such as Nature Scientific Reports, JAMA Network Open, BMC Medical Research Methodology, etc.
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