Real-World Evidence Utilization in Clinical Development Reflected by US Product Labeling: Statistical Review

Seifu, Yodit; Gamalo, Margaret; Barthel, Friederike Maria-Sophie; Lin, Junjing; Qiu, Junshan; Cooner, Freda; Ruan, Shiling; Walley, Rosalind

doi:10.1007/s43441-020-00170-y

Cited by 18 publications

(12 citation statements)

References 18 publications

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“…1 Such evidence can address issues such as generalizability to diverse populations, or investigation of rare populations, becoming a component of clinical development programs and/or regulatory decision making. 2,3 These types of data analyses require metrics and end points distinct from clinical trials to accommodate particularities such as source heterogeneity or missing information. 4 In clinical trials of solid tumors in oncology, treatment response or disease progression end points are typically measured via RECIST as standard research practice.…”

Section: Introductionmentioning

confidence: 99%

“…Data collection on patient response to treatment outside of the clinical trial setting and the evidence it generates are valuable areas of research to complement and supplement clinical trials . Such evidence can address issues such as generalizability to diverse populations, or investigation of rare populations, becoming a component of clinical development programs and/or regulatory decision making …”

Section: Introductionmentioning

confidence: 99%

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Concordance of Clinician-Documented and Imaging Response in Patients With Stage IV Non–Small Cell Lung Cancer Treated With First-Line Therapy

Bellomo

Hooley

et al. 2022

JAMA Netw Open

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IMPORTANCEIn observational oncology studies of solid tumors, response to treatment can be evaluated based on electronic health record (EHR) documentation (clinician-assessed response [CAR]), an approach different from standardized radiologist-measured response (Response Evaluation Criteria in Solid Tumours [RECIST] 1.1). OBJECTIVE To evaluate the feasibility of an imaging response based on RECIST (IRb-RECIST) and the concordance between CAR and imaging response based on RECIST assessments, and investigate discordance causes. DESIGN, SETTING, AND PARTICIPANTS This cohort study used an EHR-derived, deidentified database that included patients with stage IV non-small cell lung cancer (NSCLC) diagnosed between January 1, 2011, to June 30, 2019, selected from 3 study sites. Data analysis was conducted in August, 2020.EXPOSURES Undergoing first-line therapy and imaging assessments of response to treatment. MAIN OUTCOMES AND MEASURESIn this study, CAR assessments (referred to in prior publications as "real-world response" [rwR]) were defined as clinician-documented changes in disease burden at radiologic evaluation time points; they were abstracted manually and assigned to response categories. The RECIST-based assessments accommodated routine practice patterns by using a modified version of RECIST 1.1 (IRb-RECIST), with independent radiology reads. Concordance was calculated as the percent agreement across all response categories and across a dichotomous stratification (response [complete or partial] vs no response), unconfirmed or confirmed. RESULTSThis study found that, in 100 patients evaluated for concordance, agreement between CAR and IRb-RECIST was 71% (95% CI, 61%-80%), and 74% (95% CI, 64%-82%) for confirmed and unconfirmed response, respectively. There were more responders using CAR than IRb-RECIST (40 vs 29 with confirmation; 64 vs 43 without confirmation). The main sources of discordance were the different use of thresholds for tumor size changes by RECIST vs routine care, and unavailable baseline or follow-up scans resulting in inconsistent anatomic coverage over time. CONCLUSIONS AND RELEVANCEIn this cohort study of patients with stage IV NSCLC, we collected routine-care imaging, showing the feasibility of response evaluation using IRb-RECIST criteria with independent centralized review. Concordance between CAR and centralized IRb-RECIST was moderate. Future work is needed to evaluate the generalizability of these results to broader populations, and investigate concordance in other clinical settings.

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Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Concordance of Clinician-Documented and Imaging Response in Patients With Stage IV Non–Small Cell Lung Cancer Treated With First-Line Therapy

Bellomo

Hooley

et al. 2022

JAMA Netw Open

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“…They are listed in reverse chronological order by approval year, starting with the most recent approval. Two of the examples, Brineura and Defitelio, provide details on the basis for the statistical analysis that was used in the submissions and the labels 95 . The other examples consist of shorter excerpts highlighting the type of natural history or RWD data that was used.…”

Section: Considerations and Examples In Using Natural History And Rwd...mentioning

confidence: 99%

Natural History and Real‐World Data in Rare Diseases: Applications, Limitations, and Future Perspectives

Liu

Barrett

Leonardi

et al. 2022

The Journal of Clinical Pharma

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Rare diseases represent a highly heterogeneous group of disorders with high phenotypic and genotypic diversity within individual conditions. Due to the small numbers of people affected, there are unique challenges in understanding rare diseases and drug development for these conditions, including patient identification and recruitment, trial design, and costs. Natural history data and real-world data (RWD) play significant roles in defining and characterizing disease progression, final patient populations, novel biomarkers, genetic relationships, and treatment effects. This review provides an introduction to rare diseases, natural history data, RWD, and real-world evidence, the respective sources and applications of these data in several rare diseases. Considerations for data quality and limitations when using natural history and RWD are also elaborated. Opportunities are highlighted for cross-sector collaboration, standardized and high-quality data collection using new technologies, and more comprehensive evidence generation using quantitative approaches such as disease progression modeling, artificial intelligence, and machine learning. Advanced statistical approaches to integrate natural history data and RWD to further disease understanding and guide more efficient clinical study design and data analysis in drug development in rare diseases are also discussed.

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“…11,12 Recent literature has focused on the criteria for choosing historical controls and appropriate statistical methods for data analysis. 13–20…”

Section: Introductionmentioning

confidence: 99%

“…11,12 Recent literature has focused on the criteria for choosing historical controls and appropriate statistical methods for data analysis. [13][14][15][16][17][18][19][20] A major concern about using non-randomized historical controls is the potential for bias in estimating treatment effects. Studies may differ according to eligibility criteria and other patient characteristics, leading to covariate imbalance between studies.…”

Section: Introductionmentioning

confidence: 99%

Dynamic use of historical controls in clinical trials for rare disease research: A re-evaluation of the MILES trial

et al. 2023

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Background: Randomized controlled trials offer the best design for eliminating bias in estimating treatment effects but can be slow and costly in rare disease research. Additionally, an equal randomization approach may not be optimal in studies in which prior evidence of superiority of one or more treatments exist. Supplementing prospectively enrolled, concurrent controls with historical controls can reduce recruitment requirements and provide patients a higher likelihood of enrolling in a new and possibly superior treatment arm. Appropriate methods need to be employed to ensure comparability of concurrent and historical controls to minimize bias and variability in the treatment effect estimates and reduce the chances of drawing incorrect conclusions regarding treatment benefit. Methods: MILES was a phase III placebo-controlled trial employing 1:1 randomization that led to US Food and Drug Administration approval of sirolimus for treating patients with lymphangioleiomyomatosis. We re-analyzed the MILES trial data to learn whether substituting concurrent controls with controls from a historical registry could have accelerated subject enrollment while leading to similar study conclusions. We used propensity score matching to identify exchangeable historical controls from a registry balancing the baseline characteristics of the two control groups. This allowed more new patients to be assigned to the sirolimus arm. We used trial data and simulations to estimate key outcomes under an array of alternative designs. Results: Borrowing information from historical controls would have allowed the trial to enroll fewer concurrent controls while leading to the same conclusion reached in the trial. Simulations showed similar statistical performance for borrowing as for the actual trial design without producing type I error inflation and preserving power for the same study size when concurrent and historical controls are comparable. Conclusion: Substituting concurrent controls with propensity score–matched historical controls can allow more prospectively enrolled patients to be assigned to the active treatment and enable the trial to be conducted with smaller overall sample size, while maintaining covariate balance and study power and minimizing bias in response estimation. This approach does not fully eliminate the concern that introducing non-randomized historical controls in a trial may lead to bias in estimating treatment effects, and should be carefully considered on a case-by-case basis. Borrowing historical controls is best suited when conducting randomized controlled trials with conventional designs is challenging, as in rare disease research. High-quality data on covariates and outcomes must be available for candidate historical controls to ensure the validity of these designs. Additional precautions are needed to maintain blinding of the treatment assignment and to ensure comparability in the assessment of treatment safety. MILES ClinicalTrials.gov Number: NCT00414648.

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Real-World Evidence Utilization in Clinical Development Reflected by US Product Labeling: Statistical Review

Cited by 18 publications

References 18 publications

Concordance of Clinician-Documented and Imaging Response in Patients With Stage IV Non–Small Cell Lung Cancer Treated With First-Line Therapy

Concordance of Clinician-Documented and Imaging Response in Patients With Stage IV Non–Small Cell Lung Cancer Treated With First-Line Therapy

Natural History and Real‐World Data in Rare Diseases: Applications, Limitations, and Future Perspectives

Dynamic use of historical controls in clinical trials for rare disease research: A re-evaluation of the MILES trial

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