Quantifying the value of older adult-specific clinical trials: Post-lumpectomy irradiation among older adults with early-stage breast cancer.

INTRODUCTION: Although there is increasing interest in conducting cancer clinical trials in older adults, the benefit of such trials is unclear. We aimed to quantify the real-world clinical and economic effects of two phase 3 trials (CALGB 9343 and PRIME II) which showed that post-lumpectomy radiation therapy (RT) improves loco-regional recurrence but makes no improvement in overall survival among older women with early-stage breast cancer (ESBC). MATERIALS AND METHODS: We developed a health-transition model to quantify the incremental clinical and economic outcomes between scenarios with vs. without older adult-specific trial results from a societal perspective between 2004 and 2018. The transition probabilities in the model were mainly derived from the 10-year results of CALGB 9343. The total number of the affected patient population in the US and the change in the probability of omitting post-lumpectomy RT due to the CALGB 9343 and PRIME II results were derived from a retrospective analysis of the SEER registry data for patients with ESBC. Sensitivity analyses were conducted to calculate the 95% credible interval (CR) of the incremental clinical and economic outcomes between the two scenarios. RESULTS: Between 2004 and 2018, 32,936 (95% CR: 31,512, 34,357) fewer patients received post-lumpectomy RT among those aged 70 years or older diagnosed with ESBC in the US and there was a decrease cost of $419 M USD (95% CR: -$238 M, -$689 M) in scenarios with vs. without older adult-specific trial results. The difference in projected life years (1083 years, 95% CI: -2542, 7985) and QALYs (866 years, 95% CI: -2561, 7780) were not significant. At a willingness-to-pay threshold of $100 k/QALY, the probability of older adult-specific trial results generating a positive net monetary benefit was 98%. DISCUSSION: The CALGB 9343 and PRIME II trial results were associated with a substantial cost-saving in the US society. Our results suggest that older adult-specific clinical trials that demonstrate no survival benefit of an intervention in older adults could be correlated with a significant monetary benefit. Further case studies are needed for different types of older adult-specific trials to understand the value of older adult-specific trials comprehensively.

A Scoping Review of the Use of Machine Learning in Health Economics and Outcomes Research: Part 1-Data From Wearable Devices.

OBJECTIVES: With the emerging use of machine learning (ML) techniques, there has been particular interest in using wearable data for health economics and outcomes research (HEOR). We aimed to understand the emerging patterns of how ML has been applied to wearable data in HEOR. METHODS: We identified studies published in PubMed between January 2016 and March 2021. Studies that included at least 1 HEOR-related Medical Subject Headings term, applied an ML, and used wearable data were eligible for inclusion. Two reviewers abstracted information including ML application types and data on which ML was applied and analyzed them using descriptive analyses. RESULTS: A total of 148 studies were identified from PubMed, among which 32 studies met the inclusion criteria. There has been an increase over time in the number of ML studies using wearable data. ML has been more frequently used for monitoring events in real time (78%) than to predict future events (22%). There has been a wide range of outcomes examined, ranging from general physical or mental health (24%) to more disease-specific outcomes (eg, disease incidence [19%] and progression [13%]) and treatment-related outcomes (eg, treatment adherence [9%] and outcomes [9%]). Data for ML models were more often derived from wearable devices with specific medical purposes (60%) than those without (40%). CONCLUSION: There has been a wide range of applications of ML to wearable data. Both medical and nonmedical wearable devices have been used as a data source, showing the potential for providing rich data for ML studies in HEOR.

A Scoping Review of the Use of Machine Learning in Health Economics and Outcomes Research: Part 2-Data From Nonwearables.

OBJECTIVES: Despite the increasing interest in applying machine learning (ML) methods in health economics and outcomes research (HEOR), stakeholders face uncertainties in when and how ML can be used. We reviewed the recent applications of ML in HEOR. METHODS: We searched PubMed for studies published between January 2020 and March 2021 and randomly chose 20% of the identified studies for the sake of manageability. Studies that were in HEOR and applied an ML technique were included. Studies related to wearable devices were excluded. We abstracted information on the ML applications, data types, and ML methods and analyzed it using descriptive statistics. RESULTS: We retrieved 805 articles, of which 161 (20%) were randomly chosen. Ninety-two of the random sample met the eligibility criteria. We found that ML was primarily used for predicting future events (86%) rather than current events (14%). The most common response variables were clinical events or disease incidence (42%) and treatment outcomes (22%). ML was less used to predict economic outcomes such as health resource utilization (16%) or costs (3%). Although electronic medical records (35%) were frequently used for model development, claims data were used less frequently (9%). Tree-based methods (eg, random forests and boosting) were the most commonly used ML methods (31%). CONCLUSIONS: The use of ML techniques in HEOR is growing rapidly, but there remain opportunities to apply them to predict economic outcomes, especially using claims databases, which could inform the development of cost-effectiveness models.

Use of geriatric assessment in cancer clinical trials: A systematic review.

BACKGROUND: Older adults are underrepresented in cancer clinical trials despite accounting for most of the disease burden. Geriatric assessment (GA) could be used in clinical trials of cancer drugs for older adults to improve the clinical evidence for cancer drug use among older adults. OBJECTIVE: To examine patterns of use of GA in cancer clinical trials. METHODS: We undertook a systematic review of the studies reporting use of GA in a clinical trial setting for all cancer types and published between January 2010 and January 2020. Characteristics of GA use were extracted for each study, along with study phase, cancer type, and participant age (PROSPERO: CRD42020170584). RESULTS: We identified 320 studies and 63 studies met the final inclusion criteria. Among 74 purposes of GA use, the most common was to examine the association between impairments in GA domains and clinical outcomes (28/74, 38%). Among 258 GA domains assessed across 63 studies, physical status (59/258, 23%) and comorbidities (50/258, 19%) were most often evaluated. There was significant heterogeneity in the instruments used to assess physical function (n = 16) and mood disorders (n = 7). Most studies were phase 2 (32/63, 51%). CONCLUSIONS: GA is most often used in clinical trial settings to examine associations between GA-identified deficits and clinical outcomes. Significant heterogeneity exists in the GA instruments used across trials. Comprehensive and consistent incorporation of GA into future cancer clinical trial designs could help collect more older adult-specific clinical information and adjust trial eligibility criteria to increase representation by older adults.

Modeling the Ex Post Real Option Value in Metastatic Melanoma Using Real-World Data.

OBJECTIVES: Real option value (ROV) is created when a drug enables a patient to live long enough to benefit from a future innovation. Few studies have quantified ROV in the real world. We aimed to estimate the ex post ROV for ipilimumab in metastatic melanoma using real-world data (RWD). METHODS: We developed a framework for calculating ROV using RWD, accounting for the health gain in the standard therapy arm and the uptake of future innovations. A Markov model was developed to estimate the quality-adjusted life-years (QALYs) gained with ipilimumab compared with chemotherapy for patients with or without subsequent cancer immunotherapy (CIT). A nationwide electronic health record-derived, deidentified database was used to estimate survival and uptake of CIT. RESULTS: The incremental QALYs gained for ipilimumab compared with chemotherapy without subsequent CIT were 1.74. With subsequent CIT, the incremental QALYs compared with chemotherapy increased by 0.92, 0.60, 0.33, 0.18, 0.10, and 0.02 when CIT became available 0, 3, 6, 9, 12, and 24 months after the initiation of first-line treatment, respectively. The results were most sensitive to the survival benefit of ipilimumab, the survival benefit of subsequent CIT, and the uptake of CIT. CONCLUSIONS: This is the first study to estimate ex post ROV using RWD. The ex post ROV was between 1% and 54% of conventional value for patients who received a diagnosis within 2 years before CIT availability. Further studies are needed to understand ROV in other disease areas, particularly those with longer survival times.

Use of real-world evidence in economic assessments of pharmaceuticals in the United States.

BACKGROUND: Despite the increasing interest in expanding the use of real-world evidence (RWE) in economic assessments of pharmaceuticals, decision makers face uncertainty about how RWE should be used. OBJECTIVE: To assess the use of RWE in economic assessments of drugs by the Institute for Clinical and Economic Review (ICER). METHODS: We reviewed cost-effectiveness and budget impact analyses in final evidence reports of pharmaceuticals published by ICER. We calculated the total number of RWE uses and the proportion of model inputs informed by RWE per report. We classified model inputs into 15 categories based on their attributes and then examined what category each RWE informed to classify the reason for RWE use. Finally, we characterized RWE by study design, data source, and sponsor type. RESULTS: We identified 33 reports, all of which used RWE; the mean RWE use per report was 12 (range = 4-26). The average proportion of model inputs informed by RWE per report was 32.7%, but this proportion had a wide range (range = 4.1%-76.9%). RWE was most commonly used for disease progression inputs (28.7%) and health care resource utilization and costs (21.1%), but was rarely used for drug-specific clinical outcomes such as effectiveness (1.5%), adverse drug event rates (0.5%), and discontinuation rates (1.2%). The most frequently used study design was a retrospective cohort (56.6%), and the most frequently used data source was registry data (41.4%). About a third (30.2%) of RWE was industry-sponsored. CONCLUSIONS: RWE has been commonly used to inform pharmaceutical value assessments conducted by ICER. However, there has been relatively limited use of RWE to inform drug-specific effectiveness, despite calls for greater inclusion of RWE in value assessments for real-world drug effectiveness. DISCLOSURES: This study was funded by the University of Washington School of Pharmacy Corporate Advisory Board Health Tech Fund (HTF). The funder had no role in management, analysis, and interpretation of the data; preparation, review, and approval of the manuscript; and the decision to submit the manuscript for publication. All authors were employed by the CHOICE Institute, University of Washington School of Pharmacy, at the time of the study. Carlson reports grants from the Institute for Clinical and Economic Review during the conduct of the study and personal fees from Bayer, Adaptive Biotechnologies, Allergan, Galderma, and ViFor Pharma, unrelated to this study. Veenstra reports personal fees from several manufacturers unrelated to this study. The other authors have nothing to disclose.

The use of real-world evidence in ICER's scoping process and clinical evidence assessments.

BACKGROUND: There has been growing interest in using real-world evidence (RWE) for health technology assessment (HTA) in the United States. The Institute for Clinical and Economic Review (ICER) is an independent U.S.-based HTA organization that focuses primarily on pharmaceuticals. RWE is used to inform ICER's scoping and comparative clinical effectiveness (CCE) assessments, but the extent to which it is used has not been quantified. OBJECTIVE: To systematically evaluate use of RWE in the scoping and CCE assessment sections of the ICER HTA reports on pharmaceuticals. METHODS: We reviewed all ICER reports of pharmaceuticals published between January 2014 and June 2019. We examined the average number of instances and the proportion of RWE use in the scoping documents to inform the population, intervention, comparator, outcome, setting, or timing (PICOTS) elements of the appraisal. We also examined the average number of instances and the proportion of RWE use in the CCE assessments to inform effectiveness, safety, or treatment patterns. Finally, we evaluated use of RWE in clinical guidelines that were cited in the CCE assessments. RESULTS: In ICER scoping documents, the mean (SD) number of instances of RWE use was 3.8 (3.7) per document (55% for outcomes, 20% for population, 14% for comparator, 11% for intervention, and 0% for timing and setting). In ICER CCE assessments, the mean (SD) number of instances per assessment was 0.7 (0.5) per drug (53% for effectiveness, 44% for safety, and 3% for treatment patterns). In clinical guidelines used in ICER reports, the mean (SD) number of instances of RWE use was 1.6 (2.3) per drug per guideline (41% for effectiveness, 30% for safety, and 29% for treatment patterns). CONCLUSIONS: RWE was frequently used in the ICER scoping process, particularly to inform selection of outcomes. RWE was used infrequently in ICER CCE assessments, while more often used to inform effectiveness, safety, and treatment patterns in relevant clinical guidelines. There are opportunities to increase the use of RWE in U.S. HTA processes. DISCLOSURES: This study was supported by the Health Tech Fund, University of Washington School of Pharmacy, which was created through unrestricted support from several health care industry companies. Veenstra and Carlson report grant support from the Institute for Clinical and Economic Review outside the submitted work. Carlson reports personal fees from Bayer, Allergan, and Galderma outside the submitted work. Jiao, Lee, and Devine report no support outside the submitted work.

Assessment of chromosomal losses and gains in hepatocellular carcinoma.

We examined the chromosomal changes of 22 hepatocellular carcinomas (HCCs) by comparative genomic hybridization (CGH) analysis and compared the results with that of allelotype by polymerase chain reaction based loss of heterozygosity (PCR-LOH) analysis. By CGH analysis, frequent chromosomal losses were noted in the chromosomal region of 4q (59%), 8p (77%), and 16q (50%), whereas gains were noted in 1q (86%) and 8q (77%). All of these chromosomal arms were revealed to have frequent allelic imbalances by PCR-LOH analysis, however, 9% of chromosomal aberrations were detected only by CGH analysis and 2% were detected only by PCR-LOH analysis. Our results suggest that CGH analysis gives more precise results for the screening of chromosomal aberrations in HCCs than that of PCR-LOH analysis with randomly selected microsatellite markers.