STaRT-RWE: structured template for planning and reporting on the implementation of real world evidence studies

The maze of real-world evidence frameworks: from a desert to a jungle! An environmental scan and comparison across regulatory and health technology assessment agencies

Aim: Regulatory and health technology assessment (HTA) agencies have increasingly published frameworks, guidelines, and recommendations for the use of real-world evidence (RWE) in healthcare decision-making. Variations in the scope and content of these documents, with updates running in parallel, may create challenges for their implementation especially during the market authorization and reimbursement phases of a medicine's life cycle. This environmental scan aimed to comprehensively identify and summarize the guidance documents for RWE developed by most well-established regulatory and reimbursement agencies, as well as other organizations focused on healthcare decision-making, and present their similarities and differences. Methods: RWE guidance documents, including white papers from regulatory and HTA agencies, were reviewed in March 2024. Data on scope and recommendations from each body were extracted by two reviewers and similarities and differences were summarized across four topics: study planning, choosing fit-for-purpose data, study conduct, and reporting. Post-authorization or non-pharmacological guidance was excluded. Results: Forty-six documents were identified across multiple agencies; US FDA produced the most RWE-related guidance. All agencies addressed specific and often similar methodological issues related to study design, data fitness-for-purpose, reliability, and reproducibility, although inconsistency in terminologies on these topics was noted. Two HTA bodies (National Institute for Health and Care Excellence [NICE] and Canada's Drug Agency) each centralized all related RWE guidance under a unified framework. RWE quality tools and checklists were not consistently named and some differences in preferences were noted. European Medicines Agency, NICE, Haute Autorité de Santé, and the Institute for Quality and Efficiency in Health Care included specific recommendations on the use of analytical approaches to address RWE complexities and increase trust in its findings. Conclusion: Similarities in agencies' expectations on RWE studies design, quality elements, and reporting will facilitate evidence generation strategy and activities for manufacturers facing multiple, including global, regulatory and reimbursement submissions and re-submissions. A strong preference by decision-making bodies for local real-world data generation may hinder opportunities for data sharing and outputs from international federated data networks. Closer collaboration between decision-making agencies towards a harmonized RWE roadmap, which can be centrally preserved in a living mode, will provide manufacturers and researchers clarity on minimum acceptance requirements and expectations, especially as novel methodologies for RWE generation are rapidly emerging.

Time-dependent risk of atopic dermatitis following nontyphoidal Salmonella infection

BACKGROUND

The pathogenesis of atopic dermatitis (AD) remains unclear. Nontyphoidal Salmonella (NTS) infection might trigger immune-mediated reactions. We aimed to examine NTS and the risk of subsequent AD.

METHODS

From 2002 to 2015, eligible patients (aged 0-100 years) with NTS were identified. NTS and non-NTS groups were matched at a 1:10 ratio on age and sex. We utilized conditional multivariable Cox proportional hazard models to estimate the adjusted hazard ratio (aHR) and 95% confidence interval (CI) for AD development. Subgroup analyses were conducted based on age, sex, and severity of NTS infection. We utilized landmark analysis to explore the time-dependent hazard of AD following NTS.

RESULTS

In the NTS group (N = 6624), 403 developed AD. After full adjustment of demographics and comorbidities, the NTS group had a higher risk of AD than the reference group (aHR = 1.217, 95% CI = 1.096-1.352). Age-stratified analysis revealed that NTS group exhibited an elevated risk compared to the reference group, particularly among those aged 13-30 years (aHR = 1.25, 95% CI = 1.017-1.559), individuals aged 31-50 years (aHR = 1.388, 95% CI = 1.112-1.733), those aged 51-70 years (aHR = 1.301, 95% CI = 1.008-1.679), and individuals aged 71 years and over (aHR = 1.791, 95% CI = 1.260-2.545). Severe NTS was associated with a higher risk of AD than the reference group (aHR = 2.411, 95% CI = 1.577-3.685). Landmark analysis showed generally consistent findings.

CONCLUSIONS

Minimizing exposure to NTS infection may represent a prospective strategy for averting the onset and progression of atopic dermatitis.

Comparative Effectiveness of Glucagon-Like Peptide-1 Receptor Agonists, Sodium-Glucose Cotransporter 2 Inhibitors, Dipeptidyl Peptidase-4 Inhibitors, and Sulfonylureas for Sight-Threatening Diabetic Retinopathy

OBJECTIVE

To investigate whether the choice of glucose-lowering agent for type 2 diabetes (T2D) impacts a patient's risk of developing sight-threatening diabetic retinopathy complications.

DESIGN

Retrospective observational database study emulating an idealized target trial.

SUBJECTS

Adult (≥21 years) enrollees in United States commercial, Medicare Advantage, and Medicare fee-for-service plans from January 1, 2014, to December 31, 2021, with T2D and moderate cardiovascular disease (CVD) risk who had no baseline history of advanced diabetic retinal complications, initiating treatment with glucagon-like peptide-1 receptor agonists (GLP-1 RA), sodium-glucose cotransporter 2 inhibitors (SGLT2i), dipeptidyl peptidase-4 inhibitors (DPP-4i), and sulfonylureas.

METHODS

We used inverse propensity scoring weights in time-to-event Cox proportional hazards models.

MAIN OUTCOME MEASURES

Treatment for either diabetic macular edema or proliferative diabetic retinopathy.

RESULTS

The final study population included 371 698 patients, of whom 42 265 initiated GLP-1 RA, 53 476 initiated SGLT2i, 78 444 initiated DPP-4i, and 197 513 initiated sulfonylurea agents. The probability of treatment for sight-threatening retinopathy within 2 and 5 years was 0.3% and 0.7% for patients initiating SGLT2i (median follow-up 830 [interquartile range (IQR), 343-1401] days), 0.4% and 1.0% for GLP-1 RA (669 [IQR, 256-1167] days), 0.4% and 0.9% for DPP-4i (1263 [IQR, 688-1938] days), and 0.5% and 1.2% for sulfonylurea (1223 [IQR, 662-1879] days). Sodium-glucose cotransporter 2 inhibitors use was associated with a lower risk of treatment for sight-threatening retinopathy compared with all other medication classes, including GLP-1 RA (hazard ratio [HR], 0.73; 95% confidence interval [CI], 0.55-0.97), DPP-4i (HR, 0.79; 95% CI, 0.64-0.97), and sulfonylurea (HR, 0.61; 95% CI, 0.50-0.74). Glucagon-like peptide-1 receptor agonists use was associated with a similar risk of sight-threatening retinopathy as DPP-4i (HR, 1.07; 95% CI, 0.85-1.35) and sulfonylurea (HR, 0.83; 95% CI, 0.67-1.03).

CONCLUSIONS

Sodium-glucose cotransporter 2 inhibitors use was associated with a lower risk of sight-threatening diabetic retinopathy among adults with T2D and moderate CVD risk compared with other glucose-lowering therapies. Glucagon-like peptide-1 receptor agonists do not confer increased retinal risk, relative to DPP-4i and sulfonylurea medications.

FINANCIAL DISCLOSURE(S)

Proprietary or commercial disclosure may be found in the Footnotes and Disclosures at the end of this article.

Scoping review of the recommendations and guidance for improving the quality of rare disease registries

BACKGROUND

Rare disease registries (RDRs) are valuable tools for improving clinical care and advancing research. However, they often vary qualitatively, structurally, and operationally in ways that can determine their potential utility as a source of evidence to support decision-making regarding the approval and funding of new treatments for rare diseases.

OBJECTIVES

The goal of this research project was to review the literature on rare disease registries and identify best practices to improve the quality of RDRs.

METHODS

In this scoping review, we searched MEDLINE and EMBASE as well as the websites of regulatory bodies and health technology assessment agencies from 2010 to April 2023 for literature offering guidance or recommendations to ensure, improve, or maintain quality RDRs.

RESULTS

The search yielded 1,175 unique references, of which 64 met the inclusion criteria. The characteristics of RDRs deemed to be relevant to their quality align with three main domains and several sub-domains considered to be best practices for quality RDRs: (1) governance (registry purpose and description; governance structure; stakeholder engagement; sustainability; ethics/legal/privacy; data governance; documentation; and training and support); (2) data (standardized disease classification; common data elements; data dictionary; data collection; data quality and assurance; and data analysis and reporting); and (3) information technology (IT) infrastructure (physical and virtual infrastructure; and software infrastructure guided by FAIR principles (Findability; Accessibility; Interoperability; and Reusability).

CONCLUSIONS

Although RDRs face numerous challenges due to their small and dispersed populations, RDRs can generate quality data to support healthcare decision-making through the use of standards and principles on strong governance, quality data practices, and IT infrastructure.

R WE ready for reimbursement? A round up of developments in real-world evidence relating to health technology assessment: part 15

In this latest update we discuss real-world evidence (RWE) guidance from the leading oncology professional societies, the American Society of Clinical Oncology and the European Society for Medical Oncology, and the PRINCIPLED practical guide on the design and analysis of causal RWE studies.

Describing diversity of real world data sources in pharmacoepidemiologic studies: The DIVERSE scoping review

PURPOSE

Real-world evidence (RWE) is increasingly used for medical regulatory decisions, yet concerns persist regarding its reproducibility and hence validity. This study addresses reproducibility challenges associated with diversity across real-world data sources (RWDS) repurposed for secondary use in pharmacoepidemiologic studies. Our aims were to identify, describe and characterize practices, recommendations and tools for collecting and reporting diversity across RWDSs, and explore how leveraging diversity could improve the quality of evidence.

METHODS

In a preliminary phase, keywords for a literature search and selection tool were designed using a set of documents considered to be key by the coauthors. Next, a systematic search was conducted up to December 2021. The resulting documents were screened based on titles and abstracts, then based on full texts using the selection tool. Selected documents were reviewed to extract information on topics related to collecting and reporting RWDS diversity. A content analysis of the topics identified explicit and latent themes.

RESULTS

Across the 91 selected documents, 12 topics were identified: 9 dimensions used to describe RWDS (organization accessing the data source, data originator, prompt, inclusion of population, content, data dictionary, time span, healthcare system and culture, and data quality), tools to summarize such dimensions, challenges, and opportunities arising from diversity. Thirty-six themes were identified within the dimensions. Opportunities arising from data diversity included multiple imputation and standardization.

CONCLUSIONS

The dimensions identified across a large number of publications lay the foundation for formal guidance on reporting diversity of data sources to facilitate interpretation and enhance replicability and validity of RWE.

Effectiveness of glucose-lowering medications on cardiovascular outcomes in patients with type 2 diabetes at moderate cardiovascular risk

Cardiovascular disease (CVD) is the leading cause of death among people with type 2 diabetes1-5, most of whom are at moderate CVD risk6, yet there is limited evidence on the preferred choice of glucose-lowering medication for CVD risk reduction in this population. Here, we report the results of a retrospective cohort study where data for US adults with type 2 diabetes and moderate risk for CVD are used to compare the risks of experiencing a major adverse cardiovascular event with initiation of glucagon-like peptide-1 receptor agonists (GLP-1RA; n = 44,188), sodium-glucose cotransporter 2 inhibitors (SGLT2i; n = 47,094), dipeptidyl peptidase-4 inhibitors (DPP4i; n = 84,315) and sulfonylureas (n = 210,679). Compared to DPP4i, GLP-1RA (hazard ratio (HR) 0.87; 95% confidence interval (CI) 0.82-0.93) and SGLT2i (HR 0.85; 95% CI 0.81-0.90) were associated with a lower risk of a major adverse cardiovascular event, whereas sulfonylureas were associated with a higher risk (HR 1.19; 95% CI 1.16-1.22). Thus, GLP-1RA and SGLT2i may be the preferred glucose-lowering agents for cardiovascular risk reduction in patients at moderate baseline risk for CVD. ClinicalTrials.gov registration: NCT05214573.

Generating and using real-world data: A worthwhile uphill battle

The precision oncology paradigm challenges the feasibility and data generalizability of traditional clinical trials. Consequently, an unmet need exists for practical approaches to test many subgroups, evaluate real-world drug value, and gather comprehensive, accessible datasets to validate novel biomarkers. Real-world data (RWD) are increasingly recognized to have the potential to fill this gap in research methodology. Established applications of RWD include informing disease epidemiology, pharmacovigilance, and healthcare quality assessment. Currently, concerns regarding RWD quality and comprehensiveness, privacy, and biases hamper their broader application. Nonetheless, RWD may play a pivotal role in supplementing clinical trials, enabling conditional reimbursement and accelerated drug access, and innovating trial conduct. Moreover, purpose-built RWD repositories may support the extension or refinement of drug indications and facilitate the discovery and validation of new biomarkers. This perspective explores the potential of leveraging RWD to advance oncology, highlights its benefits and challenges, and suggests a path forward in this evolving field.

Staging and clean room: Constructs designed to facilitate transparency and reduce bias in comparative analyses of real-world data

PURPOSE

We describe constructs designed to protect the integrity of the results from comparative analyses using real-world data (RWD): staging and clean room.

METHODS

Staging involves performing sequential preliminary analyses and evaluating the population size available and potential bias before conducting comparative analyses. A clean room involves restricted access to data and preliminary results, policies governing exploratory analyses and protocol deviations, and audit trail. These constructs are intended to allow decisions about protocol deviations, such as changes to design or model specification, to be made without knowledge of how they might affect subsequent analyses. We describe an example for implementing staging with a clean room.

RESULTS

Stage 1 may involve selecting a data source, developing and registering a protocol, establishing a clean room, and applying inclusion/exclusion criteria. Stage 2 may involve attempting to achieve covariate balance, often through propensity score models. Stage 3 may involve evaluating the presence of residual confounding using negative control outcomes. After each stage, check points may be implemented when a team of statisticians, epidemiologists and clinicians masked to how their decisions may affect study outcomes, reviews the results. This review team may be tasked with making recommendations for protocol deviations to address study precision or bias. They may recommend proceeding to the next stage, conducting additional analyses to address bias, or terminating the study. Stage 4 may involve conducting the comparative analyses.

CONCLUSIONS

The staging and clean room constructs are intended to protect the integrity and enhance confidence in the results of analyses of RWD.

Risk of Major Bleeding Associated with Concomitant Direct-Acting Oral Anticoagulant and Clopidogrel Use: A Retrospective Cohort Study

BACKGROUND AND AIM

Combined anticoagulant-antiplatelet therapy is often indicated in adults with cardiovascular disease and atrial fibrillation or venous thromboembolism. The study aim was to assess the comparative risk of bleeding between rivaroxaban and apixaban when combined with clopidogrel.

METHODS

We conducted a retrospective cohort study of commercially insured US adults newly treated with a combination of rivaroxaban+clopidogrel or apixaban+clopidogrel (2015-2018) using Merative™ Marketscan Research Databases. We used propensity score-based inverse probability of treatment weighting (IPTW) to balance the treatment groups. Weighted Cox proportional hazards regression was used to estimate the risk of major bleeding.

RESULTS

The study cohort included 2895 rivaroxaban+clopidogrel users and 3628 apixaban+clopidogrel users. The median (range) duration of follow up was 61 (73) days. Rivaroxaban+clopidogrel users had a similar risk of major bleeding compared with apixaban+clopidogrel users (IPTW incidence rate per 100 person-years 7.96 vs 7.38; IPTW hazard ratio [HR] 1.13 [95% CI 0.78-1.63]). In the subcohort of adults who were treated with DOAC or clopidogrel monotherapy prior to the combined therapy, the risk of major bleeding did not differ by the drug of monotherapy (IPTW HR for rivaroxaban+clopidogrel group: 0.66 [95% CI 0.33-1.32]; IPTW HR for apixaban+clopidogrel group: 1.10 [95% CI 0.55-2.23]) CONCLUSIONS: In our study of commercially insured US adults, the concomitant use of rivaroxaban+clopidogrel and apixaban+clopidogrel conferred a similar risk of major bleeding. DOAC versus clopidogrel monotherapy prior to the concomitant therapy did not influence the risk of major bleeding.

Antipsychotics and Risks of Cardiovascular and Cerebrovascular Diseases and Mortality in Dwelling Community Older Adults

This study aims to investigate the effect of antipsychotic agents on cardiovascular and cerebrovascular diseases (CVD/CEV) and mortality risks in the older population living in a community. A cohort of 42,650 new users of antipsychotic agents was built using Quebec healthcare databases (1998-2011). The outcomes were CVD/CEV and mortality incidence in 5 years of follow-up in the total cohort, sub-cohort of patients with no schizophrenia/dementia, sub-cohort with schizophrenia, and sub-cohort with dementia. Comparisons were made between the new users who continued the treatment (adherent level ≥ 60%) vs. those ceasing treatment (adherence level < 60%) using inverse probability of treatment weighting and Cox models. Comparing high adherence vs. low levels, CVD/CEV risk was increased by 36% in the sub-cohort with schizophrenia for atypical antipsychotic users and by 25% in the sub-cohort with dementia for typical antipsychotic users. An increasing mortality risk of 2- to 3-fold was linked with the typical antipsychotic use in all cohorts except the sub-cohort with schizophrenia; in addition, mortality risk is linked with the use of high vs. low doses. Antipsychotics were not linked with CVD/CEV risk, except for atypical antipsychotics in patients with schizophrenia and typical antipsychotics in patients with dementia. The mortality risk was linked with the use of typical antipsychotics and the dose used.

The FAIRification of research in real-world evidence: A practical introduction to reproducible analytic workflows using Git and R

Transparency and reproducibility are major prerequisites for conducting meaningful real-world evidence (RWE) studies that are fit for decision-making. Many advances have been made in the documentation and reporting of study protocols and results, but the principles for version control and sharing of analytic code in RWE are not yet as established as in other quantitative disciplines like computational biology and health informatics. In this practical tutorial, we aim to give an introduction to distributed version control systems (VCS) tailored toward the FAIR (Findable, Accessible, Interoperable, and Reproducible) implementation of RWE studies. To ease adoption, we provide detailed step-by-step instructions with practical examples on how the Git VCS and R programming language can be implemented into RWE study workflows to facilitate reproducible analyzes. We further discuss and showcase how these tools can be used to track changes, collaborate, disseminate, and archive RWE studies through dedicated project repositories that maintain a complete audit trail of all relevant study documents.

Building trust in real world evidence (RWE): moving transparency in RWE towards the randomized controlled trial standard

The increasing use of RWE in regulatory and reimbursement decision-making indicates the significant progress that has been made in building trust in RWE through greater transparency. This review of the published literature and key online sources was conducted to provide an update on progress towards greater transparency in RWE, based on four key barriers to trust identified in a 2016 paper and applying learnings from transparency initiatives established for RCTs, such as the US FDA Amendments Act (FDAAA) 2007 Final Rule. Multiple initiatives from the US FDA, EMA and organizations such as the ISPOR-ISPE Joint Task Force have yielded new guidance documents and tools that enable greater transparency in RWE study methodology (STaRT-RWE and HARPER templates), data source selection and quality (SPIFD2 framework, REQueST tool), strategy (the Center for Open Science RWE Study Registry), and will therefore improve transparency in RWE study reporting. Programs such as the REPEAT Initiative and RWE DUPLICATE are investigating reproducibility of RWE studies and improving understanding of the circumstances when valid inference on treatment effects can be obtained from RWE studies. Further work is needed to embed and to implement the new tools and guidance that are available, and thus raise standards for RWE transparency towards the levels expected for RCTs.

Real-World Evidence Studies in Oncology Therapeutics: Hope or Hype?

Randomized controlled trial (RCT) remains a gold standard in evidence-based medicine for assessing the efficacy and safety of cancer therapies. However, due to some inherent methodological limitations of RCT, such as stringent inclusion criteria, highly specific treatment, ethical and scientific compromise in rare cancer, and inability to adequately assess safety, real-world evidence (RWE) has been adjudged as a suitable option to complement data obtained from RCT. Moreover, in the context of cancer therapeutics, few notable merits pertain to developing a novel product for rare cancer subtypes, establishing new indications for already approved drugs, optimization of treatment regimen and sequence, a better description of long-term safety, and supporting the reimbursement-related decision. However, the implementation of RWE for the aforementioned purposes will be limited by various challenges, especially in the context of developing economies such as India. Special attention should be given to the availability of data, maintaining the quality standard, and establishing stringent regulations for privacy and security along with active regulatory engagement with relevant stakeholders. Such activities will be key to facilitating the use of RWE in cancer therapeutics.

Real-world data analysis on effectiveness of integrative therapies: A practical guide to study design and data analysis using healthcare databases

Real world data (RWD) is increasingly used to investigate health outcomes and treatment efficacy in the field of integrative medicine. Due to the fact that the majority of RWDs are not intended for research, their secondary use in research necessitates complex study designs to account for bias and confounding. To conduct a robust analysis of RWD in integrative medicine, a comprehensive study design process that reflects the characteristics of integrative therapies is necessary. In this paper, we present a guide for designing comparative effectiveness RWE research in integrative medicine. We discuss key factors to consider when selecting RWDs for research on integrative medicine. We provide practical steps for developing a research question, formulating the PICOT objectives (population, intervention, comparator, outcome, and time horizon), and selecting and defining covariates with a summary table. Specific study designs are depicted with corresponding diagrams. Finally, data analysis procedures are introduced. We hope this article clarifies the importance of RWE research design and related processes in order to improve the rigor of RWD studies in the field of integrative medicine research.

Principles for Good Practice in the Conduct of Non-interventional Studies: The View of Industry Researchers

This reflection paper presents a consolidated view of EFPIA on the need for principles for good practice in the generation and use of non-interventional studies (NIS), including overarching principles such as the registration of hypothesis evaluating treatment effect (HETE) studies. We first define NIS and the important adjacencies to clinical trials and relationship with real-world evidence (RWE). We then outline the principles for good practice with respect to appropriate research design, study protocol, fit-for-purpose variables and data quality, analytical methods, bias reduction, transparency in conduct and use, privacy management and ethics review. We conclude with recommendations for action for the research community to promote trust and credibility in the use of NIS.

SURF: A Screening Tool (for Sponsors) to Evaluate Whether Using Real-World Data to Support an Effectiveness Claim in an FDA Application Has Regulatory Feasibility

Based on recent guidance and publicly available approvals, the US Food and Drug Administration (FDA) has demonstrated its openness to considering evidence of effectiveness from real-world data (RWD) and nonrandomized studies (or "real-world evidence (RWE)") in its decision making. Through analysis of the FDA approvals, several authors have identified methodologic issues commonly discussed by FDA reviewers. However, in our analysis of FDA guidance and use cases, acceptability of RWE also critically depends on whether the characteristics of the clinical development program align with circumstances in which the FDA may have flexibility in considering evidence from real-world study designs relative to more robust designs. Successful use of RWD requires advance planning to allocate the necessary resources and time to undertake principled epidemiology approaches to study design, data selection, and implementation of analyses as well as address regulatory feedback. Thus, sponsors benefit from early identification of programs in which successful RWD use is more likely, to ensure efficient use of resources required for the next steps of scientific feasibility assessment. We developed SURF, a screening tool intended to help a sponsor decide whether to prioritize resources for further exploring the feasibility of using an RWD approach to meet the FDA's effectiveness evidentiary standards for a particular clinical development program. Here, we provide an analysis of FDA guidance, highlighting the circumstances in which RWD approaches may be most acceptable, and demonstrate the use of this screening tool, including the rationale for its construction, types of evidence needed, and application to publicly available approvals as support of concept.

Real-world outcomes for first line next-generation hormonal agents in metastatic prostate cancer: a systematic review

Aim: This review aims to summarize published evidence on the real-world (RW) outcomes of abiraterone or enzalutamide in first-line metastatic castration-resistant prostate cancer. Materials & methods: Studies reporting on RW effectiveness, safety, economic and/or health-related quality of life outcomes were identified by systematic literature review (2011-2021, incl. Embase®, MEDLINE®) and presented in a qualitative synthesis. Risk of bias was assessed using ROBINS-I or the Molinier checklist. Results: 88 studies (n = 83,427 patients) were included. Median progression-free (40 studies) and overall survival (38 studies) ranged from 3.7 to 20.9 months and 9.8 to 45 months, respectively. Survival, safety and economic outcomes were similar across individual treatments, while limited health-related quality of life evidence suggested improvements with abiraterone. Risk of bias was moderate to high. Conclusion: RW outcomes in first-line metastatic castration-resistant prostate cancer remain poor despite treatment, highlighting an unmet need for new regimens. This review was supported by AstraZeneca and Merck Sharp & Dohme.

The Value and Deliverables of Medical Affairs: Affiliate Perspectives and Future Expectations

The Medical Affairs (MA) function in pharmaceutical companies creates a unique opportunity to ensure the internal linkage between Research & Development (R&D) and Commercial/Marketing functions, in addition to managing external scientific engagements with multiple stakeholders across life-science ecosystems. In recognition of the strategic value of MA, the objective of this paper is to share a comprehensive set of practical examples of the main deliverables within the MA function in the affiliate and align these with the two distinct phases; pre- and post-launch, respectively. We believe that an information gap currently exists in the available literature on these matters addressing practical aspects and examples beyond visionary, strategic thinking. Based on this contribution, further opportunities within MA can be discussed. In addition, we share our thoughts and considerations on future advancements in the role.

Rapid Access Addiction Medicine Clinics for People With Problematic Opioid Use

Importance

New approaches are needed to provide care for individuals with problematic opioid use (POU). Rapid access addiction medicine (RAAM) clinics offer a flexible, low-barrier, rapid access care model for this population.

Objective

To assess the associations of RAAM clinics with emergency department (ED) visits, hospitalizations, and mortality for people with POU.

Design, Setting, and Participants

A retrospective cohort study involving a matched control group was performed using health administrative data from Ontario, Canada. Anonymized data from 4 Ontario RAAM clinics (cities of Ottawa, Toronto, Oshawa, and Sudbury) were linked with health administrative data. Analyses were performed on a cohort of individuals who received care at participating RAAM clinics and geographically matched controls who did not receive care at a RAAM clinic. All visits occurred between October 2, 2017, and October 30, 2019, and data analyses were completed in spring 2023. A propensity score-matching approach was used to balance confounding factors between groups, with adjustment for covariates that remained imbalanced after matching.

Exposures

Individuals who initiated care through the RAAM model (including assessment, pharmacotherapy, brief counseling, harm reduction, triage to appropriate level of care, navigation to community services and primary care, and related care) were compared with individuals who did not receive care through the RAAM model.

Main Outcomes and Measures

The primary outcome was a composite measure of ED visits for any reason, hospitalization for any reason, and all-cause mortality (all measured up to 30 days after index date). Outcomes up to 90 days after index date, as well as outcomes looking at opioid-related ED visits and hospitalizations, were also assessed.

Results

In analyses of the sample of 876 patients formed using propensity score matching, 440 in the RAAM group (mean [SD] age, 36.5 [12.6] years; 276 [62.7%] male) and 436 in the control group (mean [SD] age, 36.8 [13.8] years; 258 [59.2%] male), the pooled odds ratio (OR) for the primary, 30-day composite outcome of all-cause ED visit, hospitalization, or mortality favored the RAAM model (OR, 0.68; 95% CI, 0.50-0.92). Analysis of the same outcome for opioid-related reasons only also favored the RAAM intervention (OR, 0.47; 95% CI, 0.29-0.76). Findings for the individual events of hospitalization, ED visit, and mortality at both 30-day and 90-day follow-up also favored the RAAM model, with comparisons reaching statistical significance in most cases.

Conclusions and Relevance

In this cohort study of individuals with POU, RAAM clinics were associated with reductions in ED visits, hospitalizations, and mortality. These findings provide valuable evidence toward a broadened adoption of the RAAM model in other regions of North America and beyond.

Evaluation of US oncology electronic health record real-world data to reduce uncertainty in health technology appraisals: a retrospective cohort study

OBJECTIVES

Examine whether data from early access to medicines in the USA can be used to inform National Institute for Health and Care Excellence (NICE) health technology assessments (HTA) in oncology.

DESIGN

Retrospective cohort study.

SETTING

Oncology-based community and academic treatment centres in the USA.

PARTICIPANTS

Patients present in a nationwide electronic health record (EHR)-derived deidentified database.

INTERVENTIONS

Cancer drugs that underwent NICE technology appraisal (TA) between 2014 and 2019.

PRIMARY AND SECONDARY OUTCOME MEASURES

The count and follow-up time of US patients, available in the EHR, who were exposed to cancer drugs of interest in the period between Food and Drug Administration (FDA) approval and dates relevant to the NICE appraisal process.

RESULTS

In 59 of 60 TAs analysed, the cancer therapy was approved in the USA before the final appraisal by NICE. The median time from FDA approval to the publication of NICE recommendations was 18.5 months, at which time the US EHR-derived database had, on average, 269 patients (SD=356) exposed to the new therapy, with a median of 75.3 person-years (IQR: 13.1-173) in time-at-risk. A case study generated evidence on real-world overall survival and treatment duration.

CONCLUSIONS

Across different cancer therapies, there was substantial variability in US real-world data accumulated between FDA approval and NICE decision milestones. The applicability of these data to generate evidence for HTA decision-making should be assessed on a case-by-case basis depending on the intended HTA use case.

A causal roadmap for generating high-quality real-world evidence

Increasing emphasis on the use of real-world evidence (RWE) to support clinical policy and regulatory decision-making has led to a proliferation of guidance, advice, and frameworks from regulatory agencies, academia, professional societies, and industry. A broad spectrum of studies use real-world data (RWD) to produce RWE, ranging from randomized trials with outcomes assessed using RWD to fully observational studies. Yet, many proposals for generating RWE lack sufficient detail, and many analyses of RWD suffer from implausible assumptions, other methodological flaws, or inappropriate interpretations. The Causal Roadmap is an explicit, itemized, iterative process that guides investigators to prespecify study design and analysis plans; it addresses a wide range of guidance within a single framework. By supporting the transparent evaluation of causal assumptions and facilitating objective comparisons of design and analysis choices based on prespecified criteria, the Roadmap can help investigators to evaluate the quality of evidence that a given study is likely to produce, specify a study to generate high-quality RWE, and communicate effectively with regulatory agencies and other stakeholders. This paper aims to disseminate and extend the Causal Roadmap framework for use by clinical and translational researchers; three companion papers demonstrate applications of the Causal Roadmap for specific use cases.

Association between cardiovascular diseases and dementia among various age groups: a population-based cohort study in older adults

The link between cardiovascular (CV) risk factors or diseases and dementia is documented. There is conflicting evidence whether age moderates the association. We need to study this gap so that research and clinical initiatives target appropriate age groups. A cohort of 320,630 adult patients without dementia was built using Quebec healthcare databases (1998-2010). The CV risk factors were hypertension, diabetes and dyslipidemia, while diseases included stroke, myocardial infarction (MI), chronic heart failure (HF), and atrial fibrillation (AF). Dementia risk and CV risk factors or diseases were assessed using incidence rate ratios and Cox regression across age groups. The cohort presented by mainly female sex (67.7%) and mean age of 74.1 years. Incident rate of dementia increased with age, ranging from 4.1 to 93.5 per 1000 person-years. Diabetes, stroke, HF and AF were significantly associated with dementia risk, hazard ratios ranged from 1.08 to 3.54. The strength of association decreased in advanced age for diabetes, stroke and HF. The results suggest that prevention of diabetes, stroke, HF and AF are crucial to mitigate dementia risk. The pathophysiology of dementia in younger and older populations seems to differ, with less impact of CV risk factors in advanced age.

Prognostic Factors of COVID-19: An Umbrella Review Endorsed by the International Society for Pharmacoepidemiology

During the coronavirus disease 2019 (COVID-19) pandemic, the urgency for updated evidence to inform public health and clinical care placed systematic literature reviews (SLRs) at the cornerstone of research. We aimed to summarize evidence on prognostic factors for COVID-19 outcomes through published SLRs and to critically assess quality elements in the findings' interpretation. An umbrella review was conducted via electronic databases from January 2020 to April 2022. All SLRs (and meta-analyses) in English were considered. Data screening and extraction were conducted by two independent reviewers. AMSTAR 2 tool was used to assess SLR quality. The study was registered with PROSPERO (CRD4202232576). Out of 4,564 publications, 171 SLRs were included of which 3 were umbrella reviews. Our primary analysis included 35 SLRs published in 2022, which incorporated studies since the beginning of the pandemic. Consistent findings showed that, for adults, older age, obesity, heart disease, diabetes, and cancer were more strongly predictive of risk of hospitalization, intensive care unit admission, and mortality due to COVID-19. Male sex was associated with higher risk of short-term adverse outcomes, but female sex was associated with higher risk of long COVID. For children, socioeconomic determinants that may unravel COVID-19 disparities were rarely reported. This review highlights key prognostic factors of COVID-19, which can help clinicians and health officers identify high-risk groups for optimal care. Findings can also help optimize confounding adjustment and patient phenotyping in comparative effectiveness research. A living SLR approach may facilitate dissemination of new findings. This paper is endorsed by the International Society for Pharmacoepidemiology.

Factors influencing prescribing the first add-on disease-modifying antirheumatic drugs in patients initiating methotrexate for rheumatoid arthritis

Background

Advances in Disease-Modifying Antirheumatic Drugs (DMARDs) have expanded the treatment landscape for Rheumatoid Arthritis (RA). Guidelines recommend adding either conventional synthetic (cs), biologic (b), or targeted synthetic (ts) DMARDs to methotrexate (MTX) for managing RA. Limited evidence exists regarding the factors that contribute to adding a DMARD agent to the MTX regimen. This study examined the factors associated with adding the first DMARD in RA patients initiating MTX.

Methods

This retrospective cohort study utilized the MarketScan data (2012-2014) involving adults (aged ≥18) with RA initiating an MTX (index date) between Jul 1, 2012 and Dec 30, 2013, and with continuous enrollment for the 6-month pre-index period. The combination therapy users received the first treatment addition of DMARD starting from day 30 after the index MTX over one year period. The study focused on the addition of csDMARDs, Tumor Necrosis Factor Inhibitors (TNFi) bDMARDs, non-TNFi bDMARDs, or tsDMARDs. Baseline covariates were measured in the 6-month pre-index and grouped into predisposing, enabling, and need factors, as per the Andersen Behavior Model. Multivariable logistic regression examined the factors associated with the addition of TNFi compared to adding a csDMARD. An additional regression model evaluated the factors associated with adding any biologic (combining TNFi and non-TNFi biologics).

Results

Among 8350 RA patients starting MTX, 31.92% (n = 2665) initiated any DMARD within the 1-year post-index period. Among RA patients initiating a DMARD prescription after starting MTX, 945 (11.32%) received combination therapy with treatment addition of a DMARD to MTX regimen; majority added TNFi (550, 58%), followed by csDMARD (352, 37%); non-TNF biologic (40, 4%), or tsDMARD (3, 0.3%). The tsDMARD group was limited and was not included for further analysis. The multivariable model found Preferred Provider Organization insurance coverage (odds ratio [OR], 1.43; 95% confidence interval (CI), 1.06-1.93), chronic pulmonary disease (OR, 1.98; 95% CI, 1.14-3.44), liver disease (OR, 5.24; 95% CI, 1.77-15.49), and Elixhauser score (OR, 0.91; 95% CI, 0.86-0.97) were significantly associated with the addition of TNF-α inhibitors. The separate multivariable model additionally found that patients from metropolitan areas (OR, 1.50; 95% CI, 1.04-2.16) were positively associated with adding any biological agent.

Conclusions

TNFi are often added to MTX for managing RA. Enabling and need factors contribute to the prescribing of a TNFi add-on therapy in RA. Future research should examine the impact of these combination therapies on RA management.

A Road Map for Peer Review of Real-World Evidence Studies on Safety and Effectiveness of Treatments

The growing acceptance of real-world evidence (RWE) in clinical and regulatory decision-making, coupled with increasing availability of health care data and advances in automated analytic approaches, has contributed to a marked expansion of RWE studies of diabetes and other diseases. However, a recent spate of high-profile retractions highlights the need for improvements in the conduct of RWE research as well as in the associated peer review and editorial processes. We review best pharmacoepidemiologic practices and common pitfalls regarding design, measurement, analysis, data validity, appropriateness, and generalizability of RWE studies. To enhance RWE study assessments, we propose that journal editors require 1) study authors to complete RECORD-PE, a reporting guideline for pharmacoepidemiological studies on routinely collected data, 2) availability of predetermined study protocols and analysis plans, 3) inclusion of pharmacoepidemiologists on the peer review team, and 4) provision of detail on data provenance, characterization, and custodianship to facilitate assessment of the data source. We recognize that none of these steps guarantees a high-quality research study. Collectively, however, they permit an informed assessment of whether the study was adequately designed and conducted and whether the data source used was fit for purpose.

Tell me what you want, what you really really want: Estimands in observational pharmacoepidemiologic comparative effectiveness and safety studies

PURPOSE

Ideally, the objectives of a pharmacoepidemiologic comparative effectiveness or safety study should dictate its design and data analysis. This paper discusses how defining an estimand is instrumental to this process.

METHODS

We applied the ICH-E9 (Statistical Principles for Clinical Trials) R1 addendum on estimands - which originally focused on randomized trials - to three examples of observational pharmacoepidemiologic comparative effectiveness and safety studies. Five key elements specify the estimand: the population, contrasted treatments, endpoint, intercurrent events, and population-level summary measure.

RESULTS

Different estimands were defined for case studies representing three types of pharmacological treatments: (1) single-dose treatments using a case study about the effect of influenza vaccination versus no vaccination on mortality risk in an adult population of ≥60 years of age; (2) sustained-treatments using a case study about the effect of dipeptidyl peptidase 4 inhibitor versus glucagon-like peptide-1 agonist on hypoglycemia risk in treatment of uncontrolled diabetes; and (3) as needed treatments using a case study on the effect of nitroglycerin spray as-needed versus no nitroglycerin on syncope risk in treatment of stabile angina pectoris.

CONCLUSIONS

The case studies illustrated that a seemingly clear research question can still be open to multiple interpretations. Defining an estimand ensures that the study targets a treatment effect that aligns with the treatment decision the study aims to inform. Estimand definitions further help to inform choices regarding study design and data-analysis and clarify how to interpret study findings.

Validation of diagnosis codes in healthcare databases in Taiwan, a literature review

PURPOSE

To compile validation findings of diagnosis codes and related algorithms for health outcomes of interest from National Health Insurance (NHI) or electronic medical records in Taiwan.

METHODS

We carried out a literature review of English articles in PubMed® and Embase from 2000 through July 2022 with appropriate search terms. Potentially relevant articles were identified through review of article titles and abstracts, full text search of methodology terms "validation", "positive predictive value", and "algorithm" in Subjects & Methods (or Methods) and Results sections of articles, followed by full text review of potentially eligible articles.

RESULTS

We identified 50 published reports with validation findings of diagnosis codes and related algorithms for a wide range of health outcomes of interest in Taiwan, including cardiovascular diseases, stroke, renal impairment, malignancy, diabetes, mental health diseases, respiratory diseases, viral (B and C) hepatitis, and tuberculosis. Most of the reported PPVs were in the 80% ~ 99% range. Assessment of algorithms based on ICD-10 systems were reported in 8 articles, all published in 2020 or later.

CONCLUSIONS

Investigators have published validation reports that may serve as empirical evidence to evaluate the utility of secondary health data environment in Taiwan for research and regulatory purpose.

Use of Real-World Data in Population Science to Improve the Prevention and Care of Diabetes-Related Outcomes

The past decade of population research for diabetes has seen a dramatic proliferation of the use of real-world data (RWD) and real-world evidence (RWE) generation from non-research settings, including both health and non-health sources, to influence decisions related to optimal diabetes care. A common attribute of these new data is that they were not collected for research purposes yet have the potential to enrich the information around the characteristics of individuals, risk factors, interventions, and health effects. This has expanded the role of subdisciplines like comparative effectiveness research and precision medicine, new quasi-experimental study designs, new research platforms like distributed data networks, and new analytic approaches for clinical prediction of prognosis or treatment response. The result of these developments is a greater potential to progress diabetes treatment and prevention through the increasing range of populations, interventions, outcomes, and settings that can be efficiently examined. However, this proliferation also carries an increased threat of bias and misleading findings. The level of evidence that may be derived from RWD is ultimately a function of the data quality and the rigorous application of study design and analysis. This report reviews the current landscape and applications of RWD in clinical effectiveness and population health research for diabetes and summarizes opportunities and best practices in the conduct, reporting, and dissemination of RWD to optimize its value and limit its drawbacks.

Real-World Evidence in Cost-Effectiveness Analysis of Enhanced Influenza Vaccines in Adults ≥ 65 Years of Age: Literature Review and Expert Opinion

Influenza vaccination can benefit most populations, including adults ≥ 65 years of age, who are at greater risk of influenza-related complications. In many countries, enhanced vaccines, such as adjuvanted, high-dose, and recombinant trivalent/quadrivalent influenza vaccines (aTIV/aQIV, HD-TIV/HD-QIV, and QIVr, respectively), are recommended in older populations to provide higher immunogenicity and increased relative vaccine efficacy/effectiveness (rVE) than standard-dose vaccines. This review explores how efficacy and effectiveness data from randomized controlled trials and real-world evidence (RWE) are used in economic evaluations. Findings from published cost-effectiveness analyses (CEA) on enhanced influenza vaccines for older adults are summarized, and the assumptions and approaches used in these CEA are assessed alongside discussion of the importance of RWE in CEA. Results from many CEA showed that adjuvanted and high-dose enhanced vaccines were cost-effective compared with standard vaccines, and that differences in rVE estimates and acquisition price may drive differences in cost-effectiveness estimates between enhanced vaccines. Overall, RWE and CEA provide clinical and economic rationale for enhanced vaccine use in people ≥ 65 years of age, an at-risk population with substantial burden of disease. Countries that consider RWE when making vaccine recommendations have preferentially recommended aTIV/aQIV, as well as HD-TIV/HD-QIV and QIVr, to protect older individuals.

Strategies to Address Current Challenges in Real-World Evidence Generation in Japan

The generation of real-world evidence (RWE), which describes patient characteristics or treatment patterns using real-world data (RWD), is rapidly growing more popular as a tool for decision-making in Japan. The aim of this review was to summarize challenges to RWE generation in Japan related to pharmacoepidemiology, and to propose strategies to address some of these challenges. We first focused on data-related issues, including the lack of transparency of RWD sources, linkage across different care settings, definitions of clinical outcomes, and the overall assessment framework of RWD when used for research purposes. Next the study reviewed methodology-related challenges. As lack of design transparency impairs study reproducibility, transparent reporting of study design is critical for stakeholders. For this review, we considered different sources of biases and time-varying confounding, along with potential study design and methodological solutions. Additionally, the implementation of robust assessment of definition uncertainty, misclassification, and unmeasured confounders would enhance RWE credibility in light of RWD source-related limitations, and is being strongly considered by task forces in Japan. Overall, the development of guidance for best practices on data source selection, design transparency, and analytical methods to address different sources of biases and robustness in the process of RWE generation will enhance credibility for stakeholders and local decision-makers.

R WE ready for reimbursement? A round up of developments in real-world evidence relating to health technology assessment: part 11

In this latest update we highlight a study from the REPEAT initiative that evaluates the reproducibility of real-world data studies, the publication of the HARPER Protocol Template developed by a joint ISPE/ISPOR taskforce, and discuss recent US FDA guidance on external control arms.

Reproducible variability: assessing investigator discordance across 9 research teams attempting to reproduce the same observational study

OBJECTIVE

Observational studies can impact patient care but must be robust and reproducible. Nonreproducibility is primarily caused by unclear reporting of design choices and analytic procedures. This study aimed to: (1) assess how the study logic described in an observational study could be interpreted by independent researchers and (2) quantify the impact of interpretations' variability on patient characteristics.

MATERIALS AND METHODS

Nine teams of highly qualified researchers reproduced a cohort from a study by Albogami et al. The teams were provided the clinical codes and access to the tools to create cohort definitions such that the only variable part was their logic choices. We executed teams' cohort definitions against the database and compared the number of subjects, patient overlap, and patient characteristics.

RESULTS

On average, the teams' interpretations fully aligned with the master implementation in 4 out of 10 inclusion criteria with at least 4 deviations per team. Cohorts' size varied from one-third of the master cohort size to 10 times the cohort size (2159-63 619 subjects compared to 6196 subjects). Median agreement was 9.4% (interquartile range 15.3-16.2%). The teams' cohorts significantly differed from the master implementation by at least 2 baseline characteristics, and most of the teams differed by at least 5.

CONCLUSIONS

Independent research teams attempting to reproduce the study based on its free-text description alone produce different implementations that vary in the population size and composition. Sharing analytical code supported by a common data model and open-source tools allows reproducing a study unambiguously thereby preserving initial design choices.

Data Governance for Real-World Data Management: A Proposal for a Checklist to Support Decision Making

OBJECTIVES

Real-world data (RWD) and real-world evidence (RWE) can provide extensive information on healthcare for use in health technology assessment and decision making. Nevertheless, there is a lack of consensus surrounding the appropriate data governance (DG) practices for RWD/RWE. Data sharing is also a large concern, especially considering evolving data protection regulations. Our objective is to propose recommendations for international standards of evaluating the acceptability of RWD governance practices.

METHODS

After reviewing the literature, we created a checklist targeting DG practices for RWD/RWE. We then carried out a 3-round Delphi panel, including European policy makers, health technology assessment experts, and hospital managers. The consensus for each statement was measured and the checklist adjusted accordingly.

RESULTS

The literature review identified the main topics regarding RWD/RWE DG practices: data privacy and security, data management and linkage, data access management, and the generation and use of RWE. Members of the Delphi panel (21 experts/25 invited) were presented a total of 24 statements related to each of the topics. Experts demonstrated a progressive level of consensus and importance ratings in all topics and to most statements. We suggest a refined checklist in which the statements rated less important or with less consensus have been removed.

CONCLUSIONS

This study suggests how the DG of RWD/RWE could be qualitatively evaluated. We propose checklists that could be used by all RWD/RWE users to help ensure the quality and integrity of RWD/RWE governance and complement data protection law.

Assessing Heterogeneity of Treatment Effect in Real-World Data

Increasing availability of real-world data (RWD) generated from patient care enables the generation of evidence to inform clinical decisions for subpopulations of patients and perhaps even individuals. There is growing opportunity to identify important heterogeneity of treatment effects (HTE) in these subgroups. Thus, HTE is relevant to all with interest in patients' responses to interventions, including regulators who must make decisions about products when signals of harms arise postapproval and payers who make coverage decisions based on expected net benefit to their beneficiaries. Prior work discussed HTE in randomized studies. Here, we address methodological considerations when investigating HTE in observational studies. We propose 4 primary goals of HTE analyses and the corresponding approaches in the context of RWD: to confirm subgroup effects, to describe the magnitude of HTE, to discover clinically important subgroups, and to predict individual effects. We discuss other possible goals including exploring prognostic score- and propensity score-based treatment effects, and testing the transportability of trial results to populations different from trial participants. Finally, we outline methodological needs for enhancing real-world HTE analysis.

A Structured Process to Identify Fit-for-Purpose Study Design and Data to Generate Valid and Transparent Real-World Evidence for Regulatory Uses

Generating evidence from real-world data requires fit-for-purpose study design and data. In addition to validity, decision makers require transparency in the reasoning that underlies study design and data source decisions. The 2019 Structured Preapproval and Postapproval Comparative Study Design Framework to Generate Valid and Transparent Real-World Evidence (SPACE) and the 2021 Structured Process to Identify Fit-For-Purpose Data (SPIFD)-intended to be used together-provide a step-by-step guide to identify decision grade, fit-for-purpose study design and data. In this update (referred to as "SPIFD2" to encompass both the design and data aspects) we provide an update to these frameworks that combines the templates into one, more explicitly calls for articulation of the hypothetical target trial and sources of bias that may arise in the real-world emulation, and provides explicit references to the Structured Template and Reporting Tool for Real-World Evidence (STaRT-RWE) tables that we suggest using immediately after invoking the SPIFD2 framework. Following the steps recommended in the SPIFD2 process requires due diligence on the part of the researcher to ensure that every aspect of study design and data selection is rationalized and supported by evidence. The resulting stepwise documentation enables reproducibility and clear communication with decision makers, and it increases the likelihood that the evidence generated is valid, fit-for-purpose, and sufficient to support healthcare and regulatory decisions.

Use of Structured Template and Reporting Tool for Real-World Evidence for Critical Appraisal of the Quality of Reporting of Real-World Evidence Studies: A Systematic Review

OBJECTIVES

Real-world evidence (RWE) studies are increasingly being used to support healthcare decisions. Various frameworks, tools, and checklists exist for ensuring quality of real-world data, designing robust studies, and assessing potential for bias. In January 2021, Structured Template and Reporting Tool for RWE (STaRT-RWE) was released to further reduce ambiguity, assumptions, and misinterpretation while planning, implementing, and reporting RWE studies of the safety and effectiveness of treatments. The objective of this study was to identify gaps in the reporting quality of published RWE studies by using this template for critical appraisal.

METHODS

Two reviewers conducted a keyword search on PubMed for free-full-text research articles using real-world data, RWE design, and safety with or without effectiveness outcomes of a medicinal product or intervention in humans of any age or gender, published in English between January 13, 2021, and January 13, 2022. Assessment of risk of bias was done using Assessment of Real-World Observational Studies critical appraisal tool. Deficiencies in methods and findings as per STaRT-RWE template were reported as frequencies.

RESULTS

A total of 54 of 2374 retrieved studies were included in the review. Based on the STaRT-RWE template, the studies inadequately reported empirically defined covariates, power and sample size calculation, attrition, sensitivity analyses, index date (day 0) defining criterion, predefined covariates, outcome, metadata about data source and software, objective, inclusion and exclusion criteria, analysis specifications, and follow-up.

CONCLUSIONS

The use of STaRT-RWE template along with its tables, design diagram, and library of published studies has a potential of improving robustness of RWE studies.

Worldwide Trends in Registering Real-World Studies at ClinicalTrials.gov: A Cross-Sectional Analysis

Objective

The purpose of this study was to characterize real-world studies (RWSs) registered at ClinicalTrials.gov to help investigators better conduct relevant research in clinical practice.

Methods

A retrospective analysis of 944 studies was performed on February 28, 2023.

Results

A total of 944 studies were included. The included studies involved a total of 48 countries. China was the leading country in terms of the total number of registered studies (37.9%, 358), followed by the United States (19.7%, 186). Regarding intervention type, 42.4% (400) of the studies involved drugs, and only 9.1% (86) of the studies involved devices. Only 8.5% (80) of the studies mentioned both the detailed study design type and data source in the "Brief Summary". A total of 49.4% (466) of studies had a sample size of 500 participants and above. Overall, 63% (595) of the studies were single-center studies. A total of 213 conditions were covered in the included studies. One-third of the studies (32.7%, 309) involved neoplasms (or tumors). China and the United States were very different regarding the study of different conditions.

Conclusion

Although the pandemic has provided new opportunities for RWSs, the rigor of scientific research still needs to be emphasized. Special attention needs to be given to the correct and comprehensive description of the study design in the Brief Summary of registered studies, thereby promoting communication and understanding. In addition, deficiencies in ClinicalTrials.gov registration data remain prominent.

Harnessing Real-World Evidence to Advance Cancer Research

Randomized controlled trials (RCTs) form a cornerstone of oncology research by generating evidence about the efficacy of therapies in selected patient populations. However, their implementation is often resource- and cost-intensive, and their generalisability to patients treated in routine practice may be limited. Real-world evidence leverages data collected about patients receiving clinical care in routine practice outside of clinical trial settings and provides opportunities to identify and address gaps in clinical trial evidence. This review outlines the strengths and limitations of real-world and RCT evidence and proposes a framework for the complementary use of the two bodies of evidence to advance cancer research. There are challenges to the implementation of real-world research in oncology, including heterogeneity of data sources, timely access to high-quality data, and concerns about the quality of methods leveraging real-world data, particularly causal inference. Improved understanding of the strengths and limitations of real-world data and ongoing efforts to optimise the conduct of real-world evidence research will improve its reliability, understanding and acceptance, and enable the full potential of real-world evidence to be realised in oncology practice.

High-dimensional propensity scores for empirical covariate selection in secondary database studies: Planning, implementation, and reporting

Real-world evidence used for regulatory, payer, and clinical decision-making requires principled epidemiology in design and analysis, applying methods to minimize confounding given the lack of randomization. One technique to deal with potential confounding is propensity score (PS) analysis, which allows for the adjustment for measured preexposure covariates. Since its first publication in 2009, the high-dimensional propensity score (hdPS) method has emerged as an approach that extends traditional PS covariate selection to include large numbers of covariates that may reduce confounding bias in the analysis of healthcare databases. hdPS is an automated, data-driven analytic approach for covariate selection that empirically identifies preexposure variables and proxies to include in the PS model. This article provides an overview of the hdPS approach and recommendations on the planning, implementation, and reporting of hdPS used for causal treatment-effect estimations in longitudinal healthcare databases. We supply a checklist with key considerations as a supportive decision tool to aid investigators in the implementation and transparent reporting of hdPS techniques, and to aid decision-makers unfamiliar with hdPS in the understanding and interpretation of studies employing this approach. This article is endorsed by the International Society for Pharmacoepidemiology.