Use of Real-World Evidence for Drug Regulatory Decisions in China: Current Status and Future Directions

Real-world data (RWD) and real-world evidence (RWE) have garnered great interest for supporting drug research and development (R&D) by medical researchers and regulators in recent years. The application and development of RWD/E in drug regulatory decision-making have been vigorously promoted in China. This study seeks to provide a broad overview of how RWE has been contributing to drug regulatory decisions in China. In this paper, we review the development of RWD and RWE, summarize key elements that promote application of RWE, introduce relevant methods and guidelines, elaborate on the opportunities and challenges of RWE in regulatory decision-making in China, and put forward suggestions to promote the application of RWE in China's regulatory decision-making and to further facilitate innovative drug evaluation and regulation.

Analyzing factors affecting risk aversion: Case of life insurance data in Korea

This research employs machine learning analysis on extensive data from a prominent Korean life insurance company to substantiate the insurance demand theory, which posits that insurance demand increases with risk aversion. We quantitatively delineate the traits of risk-averse individuals. Our study focuses on a cohort of 94,306 individuals who have filed insurance claims due to illness. To forecast prospective insurance consumers inclined toward additional purchases, we construct a predictive model using a machine learning algorithm. This model incorporates 19 demographic and socioeconomic factors as independent variables, with additional insurance acquisition as the dependent variable. Consequently, we uncover the distinctive characteristics of consumers predicted to acquire supplementary insurance products. Our findings reveal a significant association between the independent variables and the likelihood of purchasing additional insurance. Notably, 10 out of the 19 independent variables exert a substantial influence on additional insurance acquisitions. These characteristics encompass residence in rural areas, a higher likelihood of being female, advanced age, increased assets, a higher likelihood of being blue-collar workers, lower education levels, a greater likelihood of being married or divorced/separated, a history of cancer, and a predisposition for existing policyholders with prior subscriptions to actual loss insurance or substantial insurance contract amounts. Our study holds academic significance by addressing limitations observed in prior research, which predominantly relied on questionnaires to qualitatively assess risk aversion. Instead, we offer specific insights into individual characteristics associated with risk aversion. Moreover, we anticipate that Korean insurance companies can leverage these insights to attract new clientele while retaining existing members through predictive risk aversion analysis. These findings also offer valuable insights across a spectrum of disciplines, including business administration, psychology, education, sociology, and sales/marketing, related to individuals' risk preferences and behaviors.

Innovative thinking of clinical investigation for rare disease drug development

For the development of a test treatment or drug product, it is necessary to conduct composite hypothesis testing to test for effectiveness and safety simultaneously, since some approved drug products have been recalled due to safety concerns. One of the major issues in conducting a composite hypothesis testing for effectiveness and safety is the requirement of a huge sample size to achieve the desired power for detecting clinically meaningful differences in both safety and effectiveness. Situation can be much difficult in orphan drug development. In this article, a generalized two-stage innovative approach to test for effectiveness and safety simultaneously is proposed. Additionally, to alleviate the requirement of a large randomized clinical trial (RCT) and revealing effectiveness, real-world data is suggested to use in conjunction with RCT data for orphan drug development. The proposed approach can help investigators test for effectiveness and safety at the same time without worrying about the sample size. It also helps reduce the probability of approving a drug product with safety concerns.

[Real world data in radiotherapy: A data farming project by Unitrad]

The aim of the data farming project by the Unitrad group is to produce and use large quantities of structured real-life data throughout radiotherapy treatment. Starting in 2016, target real world data were selected at expert consensus conferences and regularly updated, then captured in MOSAIQ© as the patient was treated. For each partner institution, the data was then stored in a relational database, then extracted and used by researchers to create real world knowledge. This production was carried out in a multicentre, coordinated fashion. When necessary, the raw data was shared according to the research projects, in compliance with regulations. Feedack was provided at each stage, enabling the system to evolve flexibly and rapidly, using the "agile" method. This work, which is constantly evolving, has led to the creation of health data warehouses focused on data of interest in radiotherapy, and the publication of numerous academic studies. It forms part of the wider context of the exploitation of real-life data in cancerology. Unitrad data farming is a collaborative project for creating knowledge from real-life radiotherapy data, based on an active network of clinicians and researchers.

The Role of Real-World Data and Evidence in Oncology Medicines Approved in EU in 2018-2019

Use of Real-World data (RWD) has gained the interest of different stakeholders in cancer care. The aim of this study was to identify and describe the use of RWD/RWE during the pre-authorisation phase of products authorised by the EMA in 2018 and 2019 (n=111), with the focus on oncology medicines (n=24). Information was extracted from the European Public Assessment Report (EPAR) summaries and recorded for 5 stages (11 categories) of the drug development lifecycle (discovery, early development, clinical development, registration/market launch, lifecycle management). Specific chapters of full EPAR were reviewed to substantiate the findings on RWD/RWE use in clinical trial design, efficacy, safety, and effectiveness evaluation. RWD/RWE is present in all stages of the oncology drug development; 100.0% in discovery, 37.5% early development, 58.3% in clinical development, 62.5% in registration decision and 100.0% in post-authorization lifecycle management. Examples showed that trial design supported by RWD/RWE included use of open label/single arm studies; efficacy was about using either comparison of results to historical controls, supplying survey data obtained outside the clinical trial or utilizing expert panel advice; safety about including literature findings in evidence; and effectiveness on comparison of trial results of the given product to historical data or existing standard of care. The findings of this study provide specific insights into how RWD/RWE is used in development of cancer therapeutics, how it contributes to regulatory decision making and can guide further policy developments in this field.

Use of algorithms for identifying patients in a German claims database: learnings from a lung cancer case

Background

The analysis of statutory health insurance (SHI) data is a little-used approach for understanding treatment and care as well as resource use of lung cancer (LC) patients in Germany. The aims of this observational, retrospective, longitudinal analysis of structured data were to analyze the healthcare situation of LC patients in Germany based on routine data from SHI funds, to develop an algorithm that sheds light on LC types (non-small cell / NSCLC vs. small cell / SCLC), and to gain new knowledge to improve needs-based care.

Methods

Anonymized billing data of approximately four million people with SHI were analyzed regarding ICD-10 (German modification), documented medical interventions based on the outpatient SHI Uniform Assessment Standard Tariff (EBM) or the inpatient Operations and Procedure Code (OPS), and the dispensing of prescription drugs to outpatients (ATC classification). The study included patients who were members of 64 SHI funds between Jan-1st, 2015 and Dec-31st, 2016 and who received the initial diagnosis of LC in 2015 and 2016.

Results

The analysis shows that neither the cancer type nor the cancer stage can be unambiguously described by the ICD-10 coding. Furthermore, an assignment based on the prescribed medication provides only limited information: many of the drugs are either approved for both LC types or are used off-label, making it difficult to assign them to a specific LC type. Overall, 25% of the LC patients were unambiguously identifiable as NSCLC vs SCLC based on the ICD-10 code, the drug therapy, and the billing data.

Conclusions

The current coding system appears to be of limited suitability for drawing conclusions about LC and therefore the SHI patient population. This makes it difficult to analyze the healthcare data with the aim of gathering new knowledge to improve needs-based care. The approach chosen for this study did not allow for development of a LC differentiation algorithm based on the available healthcare data. However, a better overview of patient specific needs could make it possible to modify the range of services provided by the SHI funds. From this perspective, it makes sense, in a first step, to refine the ICD-10 system to facilitate NSCLC vs. SCLC classification.

Supplementary Information

The online version contains supplementary material available at 10.1186/s12913-022-07982-8.

Developing a real-world evidence base for prescribed cannabis in the United Kingdom: preliminary findings from Project Twenty21

The therapeutic potential of medical cannabis to treat a variety of conditions is becoming increasingly recognised. Globally, a large number of countries have now legalised cannabis for medical uses and a substantial number of patients are able to access their medications. Yet in the UK, where medical cannabis was legalised in November 2018, only a handful of NHS prescriptions have been written, meaning that most patients are unable to access the medicine. Reasons for this are manyfold and include the perceived lack of clinical evidence due to the challenges of studying medical cannabis through randomised controlled trials. In order to develop the current evidence base, the importance of incorporating real-world data (RWD) to assess the effectiveness and efficacy of medical cannabis has gradually become recognised. The current paper provides a detailed outline of Project Twenty21 (T21), the UK's first medical cannabis registry, launched in August 2020. We provide the rationale for T21 and describe the methodology before reporting the characteristics of the 'first patients' enrolled in the registry. We describe the health status of all patients enrolled into the project during its first 7 months of operation and the sociodemographic characteristics and primary presenting conditions for these patients, as well as details of the medical cannabis prescribed to these individuals. By 12th March 2021, 678 people had been enrolled into T21; the majority (64%) were male and their average age was 38.7 years (range = 18-80). The most commonly reported primary conditions were chronic pain (55.6%) and anxiety disorders (32.0%) and they reported high levels of multi-morbidity, including high rates of insomnia and depression. We also present preliminary evidence from 75 patients followed up after 3 months indicating that receipt of legal, prescribed cannabis was associated with a significant increase in self-reported health, assessed using the visual analogue scale of the EQ-5D-5L (Cohen's d = .77, 95% CI = .51-1.03). Our initial findings complement reports from other large-scale databases globally, indicating that the current RWD is building up a pattern of evidence. With many clinicians demanding better and faster evidence to inform their decisions around prescribing medical cannabis, the current and future results of T21 will expand the existing evidence base on the effectiveness of cannabis-based medical products (CBMPs).

PCSK9 Inhibitors in Real-world Practice: Analysis of Data from 314 Patients and 2 Years of Experience in a Center of Preventive Cardiology

PURPOSE OF REVIEW

PCSK9 inhibitors have been shown to be the most effective class of drugs modifying the levels of LDL-cholesterol as the main risk factor for atherosclerotic cardiovascular disease. The aim of this paper is to assess the effect of monoclonal antibodies on lipid and lipoprotein metabolism in real-world practice.

RECENT FINDINGS

The outcome trials showed effective reduction of LDL-C by 56-62%. Landmark studies enrolling over a total of 46,000 patients with CHD in their medical history demonstrated the beneficial effect of both agents on cardiovascular morbidity and mortality. The data from real everyday clinical practice are very limited or missing. Even in real-world practice, PCSK9 inhibitors have been shown to be an effective, safe, and well-tolerated class of drugs with effects comparable with those reported from large randomized controlled trials.

Machine learning for classification of postoperative patient status using standardized medical data

BACKGROUND AND OBJECTIVE

Real-world evidence is defined as clinical evidence regarding the use and potential benefits or risks of a medical product derived from real-world data analyses. Standardization and structuring of data are necessary to analyze medical real-world data collected from different medical institutions. An electronic message and repository have been developed to link electronic medical records in this research project, which has simplified the data integration. Therefore, this paper proposes an analysis method and learning health systems to determine the priority of clinical intervention by clustering and visualizing time-series and prioritizing patient outcomes and status during hospitalization.

METHODS

Common data items for reimbursement (Diagnosis Procedure Combination [DPC]) and clinical pathway data were examined in this project at each participating institution that runs the verification test. Long-term hospitalization data were analyzed using the data stored in the cloud platform of the institutions' repositories using multiple machine learning methods for classification, visualization, and interpretation.

RESULTS

The ePath platform contributed to integrate the standardized data from multiple institutions. The distribution of DPC items or variances could be confirmed by clustering, temporal tendency through the directed graph, and extracting variables that contributed to the prediction and evaluation of SHapley Additive Explanation effects. Constipation was determined to be the risk factor most strongly related to long-term hospitalization. Drainage management was identified as a factor that can improve long-term hospitalization. These analyses effectively extracted patient status to provide feedback to the learning health system.

CONCLUSIONS

We successfully generated evidence of medical processes by gathering patient status, medical purposes, and patient outcomes with high data quality from multiple institutions, which were difficult with conventional electronic medical records. Regarding the significant analysis results, the learning health system will be used on this project to provide feedback to each institution, operate it for a certain period, and analyze and re-evaluate it.

The emerging role of real-world data in advanced breast cancer therapy: Recommendations for collaborative decision-making

Among stakeholders and decision-makers in advanced breast cancer, the demand for insights from real-world data (RWD) is increasing. Although RWD can be used to support decisions throughout different stages of a breast cancer drug's life cycle, barriers exist to its use and acceptance. We propose a collaborative approach to generating and using RWD that is meaningful to multiple stakeholders, and encourage frameworks toward international guidelines to help standardize RWD methodologies to achieve more efficient use of RWD insights.

2020 Food and Drug Law Institute (FDLI) Annual Conference (October 6-8, 2020 - Virtual Meeting)

This year's annual conference of the Food and Drug Law Institute (FDLI) drew more than 700 attendees, including over 200 from the U.S. Food and Drug Administration (FDA), and featured 93 speakers. Despite being held virtually for the first time, the event offered a full agenda comprising breakout sessions, award presentations, and opportunities for networking that included postsession roundtable discussions and a sponsor virtual exhibit hall. Not surprisingly, the reality of the COVID-19 public health emergency was a recurrent and emphasized theme throughout the 3 days of the conference. This report summarizes several of the 29 breakout sessions from the event.

Evaluating the Feasibility of Electronic Health Records and Claims Data Sources for Specific Research Purposes

Data collected in real-world clinical settings are increasingly being used to evaluate therapeutic options. While in its infancy for research assessing effectiveness, especially comparative effectiveness in the regulatory environment, electronic health records (EHR) and administrative insurance claims data are used extensively by both manufacturers and regulators to evaluate post-marketing safety of products in the real world. The feasibility of using these data for analysis in a research study depends on the specific research question and the availability, quality and relevance of the collected data to address the scientific question. It is unlikely that any specific database could be 'qualified' for use across all research questions, even within a specific therapeutic area, due to dependence of feasibility on the elements of the specific research question. This paper describes considerations for determining whether EHR or claims data can be used for specific research purposes. A new structured approach for assessing the feasibility of these data in research is proposed. The framework builds on and considers whether each element of the PICOTS framework for well-structured research questions is adequately captured to allow for viable reliance on EHR and claims data for that specific scientific question. Practical examples and discussion of the limitations of RWD for research are given along with approaches for interpretation of analyses using RWD.

A scoping review of core outcome sets and their 'mapping' onto real-world data using prostate cancer as a case study

Background

A Core Outcomes Set (COS) is an agreed minimum set of outcomes that should be reported in all clinical studies related to a specific condition. Using prostate cancer as a case study, we identified, summarized, and critically appraised published COS development studies and assessed the degree of overlap between them and selected real-world data (RWD) sources.

Methods

We conducted a scoping review of the Core Outcome Measures in Effectiveness Trials (COMET) Initiative database to identify all COS studies developed for prostate cancer. Several characteristics (i.e., study type, methods for consensus, type of participants, outcomes included in COS and corresponding measurement instruments, timing, and sources) were extracted from the studies; outcomes were classified according to a predefined 38-item taxonomy. The study methodology was assessed based on the recent COS-STAndards for Development (COS-STAD) recommendations. A ‘mapping’ exercise was conducted between the COS identified and RWD routinely collected in selected European countries.

Results

Eleven COS development studies published between 1995 and 2017 were retrieved, of which 8 were classified as ‘COS for clinical trials and clinical research’, 2 as ‘COS for practice’ and 1 as ‘COS patient reported outcomes’. Recommended outcomes were mainly categorized into ‘mortality and survival’ (17%), ‘outcomes related to neoplasm’ (18%), and ‘renal and urinary outcomes’ (13%) with no relevant differences among COS study types. The studies generally fulfilled the criteria for the COS-STAD ‘scope specification’ domain but not the ‘stakeholders involved’ and ‘consensus process’ domains. About 72% overlap existed between COS and linked administrative data sources, with important gaps. Linking with patient registries improved coverage (85%), but was sometimes limited to smaller follow-up patient groups.

Conclusions

This scoping review identified few COS development studies in prostate cancer, some quite dated and with a growing level of methodological quality over time. This study revealed promising overlap between COS and RWD sources, though with important limitations; linking established, national patient registries to administrative data provide the best means to additionally capture patient-reported and some clinical outcomes over time. Thus, increasing the combination of different data sources and the interoperability of systems to follow larger patient groups in RWD is required.