Trial designs using real-world data: The changing landscape of the regulatory approval process

Lifestyle in adulthood can modify the causal relationship between BMI and islet function: using Mendelian randomization analysis

BACKGROUND

Body mass index was intimately associated with islet function, which was affected by various confounding factors. Among all methods of statistical analysis, Mendelian randomization best ruled out bias to find the causal relationship. In the present study, we explored the relationship between 13 East Asian body mass index-related genes reported previously and islet function using the Mendelian randomization method.

METHODS

A total of 2892 participants residing in northern China were enrolled. Anthropological information, such as sex, age, drinking status, smoking status, weight, height and blood pressure, was recorded for all participants. Fasting glucose and insulin were detected, and the insulin sensitivity index was calculated. 13 single nucleotide polymorphismss in East Asian body mass index -related genes were analysed with the ABI7900HT system.

RESULTS

Five genetic locus mutations, CDKAL1, MAP2K5, BDNF, FTO and SEC16B, were found to be associated with body mass index and were used to estimate the genetic risk score. We found that the genetic risk score was negatively associated with the insulin sensitivity index. Even after adjusted of confounding factors, the relationship showed statistical significance. A subsequent interaction effect analysis suggested that the negative relationship between the genetic risk score and insulin sensitivity index no longer existed in the nondrinking population, and smokers had a stronger negative relationship than nonsmokers.

CONCLUSION

We found a negative causal relationship between body mass index-related genetic locus mutations and insulin resistance, which might be increased by acquired lifestyle factors, such as drinking and smoking status.

Real world data and data science in medical research: present and future

Real world data (RWD) are generating greater interest in recent times despite being not new. There are various purposes of the RWD analytics in medical research as follows: effectiveness and safety of medical treatment, epidemiology such as incidence and prevalence of disease, burden of disease, quality of life and activity of daily living, medical costs, etc. The RWD research in medicine is a mixture of digital transformation, statistics or data science, public health, and regulatory science. Most of the articles describing the RWD or real-world evidence (RWE) in medical research cover only a portion of these specializations, which might lead to an incomplete understanding of the RWD. This article summarizes the overview and challenges of the RWD analysis in medical fields from methodological perspectives. As the first step for the RWD analysis, data source of the RWD should be comprehended. The progress of the RWD is closely related to the digitization, especially of medical administrative data and medical records. Second, the selection of appropriate statistical and epidemiological methods is highly critical for an RWD analysis than those for randomized clinical trials. This is because it contains greater varieties of bias, which should be controlled by balancing the underlying risk between treatment groups. Last, the future of the RWD is discussed in terms of overcoming limited data by proxy confounders, using unstructured text data, linking of multiple databases, using the RWD or RWE for a regulatory purpose, and evaluating values and new aspects in medical research brought by the RWD.

Electronic Health Record-Triggered Research Infrastructure Combining Real-world Electronic Health Record Data and Patient-Reported Outcomes to Detect Benefits, Risks, and Impact of Medication: Development Study

Background

Real-world data from electronic health records (EHRs) represent a wealth of information for studying the benefits and risks of medical treatment. However, they are limited in scope and should be complemented by information from the patient perspective.

Objective

The aim of this study is to develop an innovative research infrastructure that combines information from EHRs with patient experiences reported in questionnaires to monitor the risks and benefits of medical treatment.

Methods

We focused on the treatment of overactive bladder (OAB) in general practice as a use case. To develop the Benefit, Risk, and Impact of Medication Monitor (BRIMM) infrastructure, we first performed a requirement analysis. BRIMM’s starting point is routinely recorded general practice EHR data that are sent to the Dutch Nivel Primary Care Database weekly. Patients with OAB were flagged weekly on the basis of diagnoses and prescriptions. They were invited subsequently for participation by their general practitioner (GP), via a trusted third party. Patients received a series of questionnaires on disease status, pharmacological and nonpharmacological treatments, adverse drug reactions, drug adherence, and quality of life. The questionnaires and a dedicated feedback portal were developed in collaboration with a patient association for pelvic-related diseases, Bekkenbodem4All. Participating patients and GPs received feedback. An expert meeting was organized to assess the strengths, weaknesses, opportunities, and threats of the new research infrastructure.

Results

The BRIMM infrastructure was developed and implemented. In the Nivel Primary Care Database, 2933 patients with OAB from 27 general practices were flagged. GPs selected 1636 (55.78%) patients who were eligible for the study, of whom 295 (18.0% of eligible patients) completed the first questionnaire. A total of 288 (97.6%) patients consented to the linkage of their questionnaire data with their EHR data. According to experts, the strengths of the infrastructure were the linkage of patient-reported outcomes with EHR data, comparison of pharmacological and nonpharmacological treatments, flexibility of the infrastructure, and low registration burden for GPs. Methodological weaknesses, such as susceptibility to bias, patient selection, and low participation rates among GPs and patients, were seen as weaknesses and threats. Opportunities represent usefulness for policy makers and health professionals, conditional approval of medication, data linkage to other data sources, and feedback to patients.

Conclusions

The BRIMM research infrastructure has the potential to assess the benefits and safety of (medical) treatment in real-life situations using a unique combination of EHRs and patient-reported outcomes. As patient involvement is an important aspect of the treatment process, generating knowledge from clinical and patient perspectives is valuable for health care providers, patients, and policy makers. The developed methodology can easily be applied to other treatments and health problems.

Inhaled Dry Powder Mannitol Treatment in Pediatric Patients with Cystic Fibrosis: Evaluation of Clinical Data in a Real-World Setting

Background: Cystic fibrosis (CF) is a genetic disorder, in which defective clearance of airway secretions leads to progressive lung function loss. Inhaled mannitol is used to increase sputum and mucociliary clearance. There are little data from real-world studies on the effectiveness of mannitol in children. Our objective was to evaluate the spirometry and clinical results of mannitol in pediatric patients. Methods: We retrospectively reviewed the records of 30 children and adolescents with CF receiving inhaled mannitol who were already on recombinant human deoxyribonuclease (rhDNase) treatment. The change in forced expiratory volume in 1 second (FEV₁) from baseline at 2-4 months was the primary outcome. Secondary measures were other spirometry results, body mass index (BMI), hospital admissions, sputum characteristics, and positive bacterial colonization. Results: Compared to baseline, we found significant improvement in percent predicted FEV₁ at 2-4 months of treatment; 84.50 (58.00-99.00) vs. 96.00 (66.00-106.00) (P = 0.0007). The absolute change in FEV₁ was +11.5% at 2-4 months, +6.5% at 5-7 months, and +4% at 8-12 months. Also, significant improvements in other spirometry results were observed. Adolescents had significantly lower FEV₁ results, but the improvement in their lung function was sustained for a more extended period than children. Mannitol provided easier sputum removal, increased sputum volume, significant decline in hospitalizations, and significantly fewer patients with positive sputum cultures. A significant increase in BMI at 8-12 months was observed. Cough was the most frequent adverse effect. Conclusion: In a real-world setting, our results demonstrated that adding mannitol to rhDNase therapy is tolerable in pediatric patients with CF and may provide improved spirometry and clinical outcomes. In addition, our results showed that mannitol provided recovery in overall lung function at 2-4 months, which was sustained up to 12 months together with improved BMI, easier sputum removal, and a decline in bacterial colonization and hospital admissions. However, cough was the most frequent side effect.

Real-world data in drug development strategies for orphan drugs: tafasitamab in B cell lymphoma, a case study for approval based on a single-arm combination trial

Tafasitamab (TAF) plus lenalidomide (LEN) is a novel treatment option for patients with relapsed/refractory diffuse large B cell lymphoma (rrDLBCL) who are not eligible for autologous stem cell transplantation. The initial US/EU approvals for TAF represent precedents because this is the first time that approval of a novel combination therapy was granted based on a pivotal single-arm trial (SAT). Matching real world-data (RWD) helped to disentangle the contribution of individual agents. In this review, we present the TAF development strategy, the prospective incorporation of RWD within the clinical development plan, the corresponding regulatory hurdles of this strategy, and the prior regulatory actions for other cancer drugs that previously incorporated RWD and propensity score matching in EU and US regulatory submissions. We also outline how RWD could further advance and impact orphan drug development.

Real-World Evidence for Regulatory Decision-Making: Guidance From Around the World

PURPOSE

Interest in leveraging real-world evidence (RWE) to support regulatory decision making for product effectiveness has been increasing globally as evident by the increasing number of regulatory frameworks and guidance documents. However, acceptance of RWE, especially before marketing for regulatory approval, differs across countries. In addition, guidance on the design and conduct of innovative clinical trials, such as randomized controlled registry studies, pragmatic trials, and other hybrid studies, is lacking.

METHODS

We assessed the global regulatory environment with regard to RWE based on regional availability of the following 3 key regulatory elements: (1) RWE regulatory framework, (2) data quality and standards guidance. and (3) study methods guidance.

FINDINGS

This article reviews the available frameworks and existing guidance from across the globe and discusses the observed gaps and opportunities for further development and harmonization.

IMPLICATIONS

Cross-country collaborations are encouraged to further shape and align RWE policies and help establish frameworks in countries without current policies with the goal of creating efficiencies when considering RWE to support regulatory decision-making globally.

Real World-Evidence for Regulatory Use Decision Aid: An Interactive Tool To Inform Clinical Development and Regulatory Strategies

Real-world evidence (RWE) is increasingly used to complement clinical trial data for regulatory decision-making and in certain cases utilized to establish the clinical effectiveness of a therapy. However, the use of RWE is not applicable for all regulatory submissions, and it can be challenging to identify appropriate use cases. An interactive tool was developed ("Decision Aid," https://sn.pub/TpDjZx ) to assist researchers, industry, and other stakeholders in identifying regulatory situations that can benefit from leveraging RWE by organizing precedent cases based on a given regulatory objective (new product approval, labeling expansion for new indication or additional clinical data, post-marketing requirement) and type of RWE study design (external control, observational study, pragmatic trial). Key success factors ensuring fit-for-purpose data and rigorous methods (e.g., clear endpoints, minimizing bias, data completeness) are also described. The tool allows the user to navigate through the precedent cases by selecting certain regulatory objectives and/or study designs. The Decision Aid supports regulatory activities in the RWE space and encourages further use of RWE in regulatory decision-making.

Naoxintong capsule delay the progression of diabetic kidney disease: A real-world cohort study

INTRODUCTION

Diabetic kidney disease (DKD) is a severe and growing health problem, associated with a worse prognosis and higher overall mortality rates than non-diabetic renal disease. Chinese herbs possess promising clinical benefits in alleviating the progression of DKD due to their multi-target effect. This real-world retrospective cohort trial aimed to investigate the efficacy and safety of Naoxintong (NXT) capsules in the treatment of DKD. Our study is the first real-world study (RWS) of NXT in the treatment of DKD based on a large database, providing a basis for clinical application and promotion.

METHODS

The data was collected from Tianjin Healthcare and Medical Big Data Platform. Patients with DKD were enrolled from January 1, 2011, to March 31, 2021. NXT administration was defined as the exposure. The primary outcome was the change in estimated glomerular filtration rate (eGFR). We employed the propensity score matching (PSM) method to deal with confounding factors.

RESULTS

A total of 1,798 patients were enrolled after PSM, including 899 NXT users (exposed group) and 899 non-users (control group). The eGFR changes from baseline to the end of the study were significantly different in the exposed group compared to the control group (-1.46 ± 21.94 vs -5.82 ± 19.8 mL/(min·1.73m²), P< 0.01). Patients in the NXT group had a lower risk of composite renal outcome event (HR, 0.71; 95%CI, 0.55 to 0.92; P = 0.009) and deterioration of renal function (HR, 0.74; 95% CI, 0.56 to 0.99; P = 0.039).

CONCLUSION

NXT can significantly slow the decline of eGFR and reduce the risk of renal outcomes. However, large cohort studies and RCTs are needed to further confirm our results.

Use of Real-World Evidence to Drive Drug Development Strategy and Inform Clinical Trial Design

Interest in real-world data (RWD) and real-world evidence (RWE) to expedite and enrich the development of new biopharmaceutical products has proliferated in recent years, spurred by the 21st Century Cures Act in the United States and similar policy efforts in other countries, willingness by regulators to consider RWE in their decisions, demands from third-party payers, and growing concerns about the limitations of traditional clinical trials. Although much of the recent literature on RWE has focused on potential regulatory uses (e.g., product approvals in oncology or rare diseases based on single-arm trials with external control arms), this article reviews how biopharmaceutical companies can leverage RWE to inform internal decisions made throughout the product development process. Specifically, this article will review use of RWD to guide pipeline and portfolio strategy; use of novel sources of RWD to inform product development, use of RWD to inform clinical development, use of advanced analytics to harness "big" RWD, and considerations when using RWD to inform internal decisions. Topics discussed will include the use of molecular, clinicogenomic, medical imaging, radiomic, and patient-derived xenograft data to augment traditional sources of RWE, the use of RWD to inform clinical trial eligibility criteria, enrich trial population based on predicted response, select endpoints, estimate sample size, understand disease progression, and enhance diversity of participants, the growing use of data tokenization and advanced analytical techniques based on artificial intelligence in RWE, as well as the importance of data quality and methodological transparency in RWE.

Use of Real-Time Continuous Glucose Monitoring Improves Glycemic Control and Other Clinical Outcomes in Type 2 Diabetes Patients Treated with Less Intensive Therapy

Objective: Use of real-time continuous glucose monitoring (rtCGM) has been shown to improve glycemic control in patients with type 2 diabetes (T2D) who are treated with intensive insulin therapy. However, most T2D patients are denied coverage for rtCGM due to failure to meet payer eligibility requirements: treatment with ≥3 insulin injections (or pump) and history of 4 × /day blood glucose testing. We investigated the relevance of these criteria to successful rtCGM use. Methods: This 6-month, prospective, interventional, single-arm study assessed the clinical effects of use rtCGM in patients with T2D treated with basal insulin only or noninsulin therapy. Primary outcomes were changes in HbA1c, average glucose, glycemic variability (% coefficient of variation), and percent of time in range (%TIR), below range (%TBR) and above range (%TAR). Results: Thirty-eight patients were included in the analysis (10.1% ± 1.8% HbA1c, 54.7 ± 10.2 years, 35.6 ± 6.4 body mass index). At 6 months, we observed reductions in HbA1c (-3.0% ± 1.3%, P < 0.001) and average glucose (-23.6 ± 38.8, P < 0.001). %TIR increased 15.2 ± 22.3, from 57.0 ± 29.9 to 72.2 ± 23.6, P < 0.001, with all patients maintaining %TBR targets (<4% at 70 mg/dL, <1% at <54 mg/dL). No changes in glycemic variability were observed. The greatest improvements in %TIR and %TAR were seen in patients treated with ≤1 medication. Conclusions: rtCGM use was associated with significant glycemic improvements in T2D patients treated with basal insulin only or noninsulin therapy. Given the growing body of evidence supporting rtCGM use in this population, insurance eligibility criteria should be modified to expand rtCGM use by T2D patients treated with less intensive therapies.

Comparing Findings From a Friends of Cancer Research Exploratory Analysis of Real-World End Points With the Cancer Analysis System in England

PURPOSE

This study compared real-world end points extracted from the Cancer Analysis System (CAS), a national cancer registry with linkage to national mortality and other health care databases in England, with those from diverse US oncology data sources, including electronic health care records, insurance claims, unstructured medical charts, or a combination, that participated in the Friends of Cancer Research Real-World Evidence Pilot Project 1.0. Consistency between data sets and between real-world overall survival (rwOS) was assessed in patients with immunotherapy-treated advanced non-small-cell lung cancer (aNSCLC).

PATIENTS AND METHODS

Patients with aNSCLC, diagnosed between January 2013 and December 2017, who initiated treatment with approved programmed death ligand-1 (PD-[L]1) inhibitors until March 2018 were included. Real-world end points, including rwOS and real-world time to treatment discontinuation (rwTTD), were assessed using Kaplan-Meier analysis. A synthetic data set, Simulacrum, on the basis of conditional random sampling of the CAS data was used to develop and refine analysis scripts while protecting patient privacy.

RESULTS

Characteristics (age, sex, and histology) of the 2,035 patients with immunotherapy-treated aNSCLC included in the CAS study were broadly comparable with US data sets. In CAS, a higher proportion (46.7%) of patients received a PD-(L)1 inhibitor in the first line than in US data sets (18%-30%). Median rwOS (11.4 months; 95% CI, 10.4 to 12.7) and rwTTD (4.9 months; 95% CI, 4.7 to 5.1) were within the range of US-based data sets (rwOS, 8.6-13.5 months; rwTTD, 3.2-7.0 months).

CONCLUSION

The CAS findings were consistent with those from US-based oncology data sets. Such consistency is important for regulatory decision making. Differences observed between data sets may be explained by variation in health care settings, such as the timing of PD-(L)1 approval and reimbursement, and data capture.

Analysis of the Regulatory, Legal, and Medical Conditions for the Prescription of Mobile Health Applications in the United States, The European Union, and France

INTRODUCTION

Mobile health (mHealth) is now considered an important approach to extend traditional health services and to meet the growing medical needs. The prescribability of mHealth applications is a complex problem because it depends on a large number of factors and concerns a wide range of disciplines and actors in the industrial, health, normative, and regulatory domains.

OBJECTIVE

Our study correlated data from the scientific literature with data on regulatory developments in the United States, the European Union, and France with the aim of identifying the conditions for the prescription of mHealth applications.

METHODS

The search method adopted was the systematic literature review process by Brereton et al. All empirical evidence from the relevant fields of study was gathered and then evaluated to answer our predefined research questions. The WoS and PubMed databases were queried for the period between 1 January 1975 and 30 November 2020. A total of 165 articles (15 with a direct focus and 150 with an indirect focus on mHealth prescribing) were analyzed/cross-referenced. The ScienceDirect database was consulted to complement the collected data when needed. Data published by international and national regulatory bodies were analyzed in light of the scientific data obtained from the WoS, PubMed, and ScienceDirect databases.

RESULTS

The International Medical Device Regulators Forum has ensured the international structuring of the regulatory field in collaboration with participating countries. The creation and updating of databases have allowed the tracking of medical device versions/upgrades and incidents. The regulatory organizations of the United States, the European Union, and France are currently consulting healthcare personnel, manufacturers, and patients to establish evaluation criteria for usability and quality of instructions for use that take into consideration patients' level of literacy. These organizations are also providing support to manufacturers who wish to file marketing applications. Marketing, privacy, and cybersecurity measures are evolving with developments in technology and state cooperation policies. The prescription of mHealth applications will gain social acceptance only if consistency and coordination are ensured at all stages of the process: from pre-design, through verification of medical effectiveness, to ethical consideration during data collection and use, and on to marketing.

CONCLUSION

The conditions for mHealth prescribability include the adaptation of international regulation by the different states, the state provision of marketing support, and the evaluation of mHealth applications. For mHealth to gain social acceptance, increased collaboration among physicians, manufacturers, and "information technology stakeholders" is needed. Once this is achieved, MHealth can become the cornerstone of successful health care reform.

Real World Evidence in Medical Cannabis Research

BACKGROUND

Whilst access to cannabis-based medicinal products (CBMPs) has increased globally subject to relaxation of scheduling laws globally, one of the main barriers to appropriate patient access remains a paucity of high-quality evidence surrounding their clinical effects.

DISCUSSION

Whilst randomised controlled trials (RCTs) remain the gold-standard for clinical evaluation, there are notable barriers to their implementation. Development of CBMPs requires novel approaches of evidence collection to address these challenges. Real world evidence (RWE) presents a solution to not only both provide immediate impact on clinical care, but also inform well-conducted RCTs. RWE is defined as evidence derived from health data sourced from non-interventional studies, registries, electronic health records and insurance data. Currently it is used mostly to monitor post-approval safety requirements allowing for long-term pharmacovigilance. However, RWE has the potential to be used in conjunction or as an extension to RCTs to both broaden and streamline the process of evidence generation.

CONCLUSION

Novel approaches of data collection and analysis will be integral to improving clinical evidence on CBMPs. RWE can be used in conjunction or as an extension to RCTs to increase the speed of evidence generation, as well as reduce costs. Currently, there is an abundance of potential data however, whilst a number of platforms now exist to capture real world data it is important the right tools and analysis are utilised to unlock potential insights from these.

The Role of Real-World Evidence in FDA-Approved New Drug and Biologics License Applications

The US Food and Drug Administration (FDA) is open to accepting real‐world evidence (RWE) to support its assessment of medical products. However, RWE stakeholders lack a shared understanding of FDA’s evidentiary expectations for the use of RWE in applications for new drugs and biologics. We conducted a systematic review of publicly available FDA approval documents from January 2019 to June 2021. We sought to quantify, by year, how many approvals incorporated RWE in any form, and the intended use of RWE in those applications. Among approvals with RWE intended to support safety and/or effectiveness, we classified whether and how those studies impacted FDA’s benefit‐risk considerations, whether those studies were incorporated into the product label, and the therapeutic area of the medical product. Finally, we qualified FDA’s documented feedback where available. We found that 116 approvals incorporated RWE in any form, with the proportion of approvals incorporating RWE increasing each year. Of these approvals, 88 included an RWE study intended to provide evidence of safety or effectiveness. Among these 88 approvals, 65 of the studies influenced FDA’s final decision and 38 were included in product labels. The 88 approvals spanned 18 therapeutic areas. FDA’s feedback on RWE study quality included methodological issues, sample size concerns, omission of patient level data, and other limitations. Based on these findings, we would anticipate that future guidance on FDA’s evidentiary expectations of RWE use will incorporate fit‐for‐purpose real‐world data selection and careful attention to study design and analysis.

Use of Real-World Data and Evidence in Drug Development of Medicinal Products Centrally Authorized in Europe in 2018-2019

Real‐world data/real‐world evidence (RWD/RWE) are considered to have a great potential to complement, in some cases, replace the evidence generated through randomized controlled trials. By tradition, use of RWD/RWE in the postauthorization phase is well‐known, whereas published evidence of use in the pre‐authorization phase of medicines development is lacking. The primary aim of this study was to identify and quantify the role of potential use of RWD/RWE (RWE signatures) during the pre‐authorization phase, as presented in the initial marketing authorization applications of new medicines centrally evaluated with a positive opinion in 2018–2019 (n = 111) by the European Medicines Agency (EMA). Data for the study was retrieved from the evaluation overviews of the European Public Assessment Reports (EPARs), which reflect the scientific conclusions of the assessment process and are accessible through the EMA website. RWE signatures were extracted into an RWE Data Matrix, including 11 categories divided over 5 stages of the drug development lifecycle. Nearly all EPARs included RWE signatures for the discovery (98.2%) and life‐cycle management (100.0%). Half of them included RWE signatures for the full development phase (48.6%) and for supporting regulatory decisions at the registration (46.8%), whereas over a third (35.1%) included RWE signatures for the early development. RWE signatures were more often seen for orphan and conditionally approved medicines. Oncology, hematology, and anti‐infectives stood out as therapeutic areas with most RWE signatures in their full development phase. The findings bring unprecedented insights about the vast use of RWD/RWE in drug development supporting the regulatory decision making.

Use of RWE to Inform Regulatory, Public Health Policy, and Intervention Priorities for the Developing World

For low‐ and middle‐income countries (LMICs) to benefit from real‐world evidence (RWE)/real‐world data (RWD) in both product registration (“regulatory”) decision making and in product utilization policy (“policy”) decision making, they need to overcome several challenges. They need to deploy more electronic health records systems (EHRs), adjust for confounder variables, build trust between stakeholders, and create laws and regulations for local generation of data that are assented for secondary use. The role of procurers and their use of RWE/RWD in the LMIC context likewise is in a state of ongoing development. Procurers of health products are strong players currently in the “access” chain as LMICs continue to work on strengthening governmental health technology assessment (HTA) bodies. Procurers’ use of RWE is presently at an early stage and is mostly indirect, leveraging RWE results that are produced by researchers in high‐income countries (HICs), often under considerably different regulatory and policy objectives and constraints compared to LMICs’ epidemiology and priorities. Pending wider deployment of EHRs and other RWE sources, stakeholders must realize that populations from HIC RWE (i) can be devised to closely resemble phenotypic patterns in LMIC populations and (ii) can be analyzed to align with LMICs’ unmet needs.

KOBIO, the First Web-based Korean Biologics Registry Operated With a Unified Platform Among Distinct Disease Entities

The KOrean College of Rheumatology BIOlogics and targeted therapy (KOBIO) registry is a nationwide observational cohort that captures detailed data on exposure of patients to biologic and targeted synthetic disease-modifying anti-rheumatic drugs (DMARDs). This registry was launched in December 2012 with an aim to prospectively investigate clinical manifestations and outcomes of patients with rheumatoid arthritis (RA), ankylosing spondylitis, and psoriatic arthritis who initiated a biologic or targeted synthetic DMARD or switched to another. Demographic data, disease activity, current treatment, adverse events, terms based on Medical Dictionary for Regulatory Activities, and so on are registered for patients who are then followed up annually in a web-based unified platform. The KOBIO registry also recruits and collects data of patients with RA on conventional DMARDs for comparison. As of today, more than 5,500 patients were enrolled from 47 academic and community Rheumatology centers across Korea. The KOBIO registry has evolved to become a powerful database for clinical research to improve clinical outcomes and quality of treatment.

Co-production of randomized clinical trials with patients: a case study in autologous hematopoietic stem cell transplant for patients with scleroderma

BACKGROUND

Increasingly, it is argued that clinical trials struggle to recruit participants because they do not respond to key questions or study treatments that patients will be willing or able to use. This study explores how elicitation of patient-preferences can help designers of randomized controlled trials (RCTs) understand the impact of changing modifiable aspects of treatments or trial design on recruitment.

METHODS

Focus groups and a discrete choice experiment (DCE) survey were used to elicit preferences of people with scleroderma for autologous hematopoietic stem cell transplant (AHSCT) treatment interventions. Preferences for seven attributes of treatment (effectiveness, immediate and long-term risk, care team composition and experience, cost, travel distance) were estimated using a mixed-logit model and used to predict participation in RCTs.

RESULTS

Two hundred seventy-eight people with scleroderma answered the survey. All AHSCT treatment attributes significantly influenced preferences. Treatment effectiveness and risk of late complications contributed the most to participants' choices, but modifiable factors of distance to treatment center and cost also affected preferences. Predicted recruitment rates calibrated with participation in a recent trial (33%) and suggest offering a treatment closer to home, at lower patient cost, and with holistic, multidisciplinary care could increase participation to 51%.

CONCLUSIONS

Through a patient engaged approach to preference elicitation for different features of AHSCT treatment options, we were able to predict what drives the decisions of people with scleroderma to participate in RCTs. Knowledge regarding concerns and the trade-offs people are willing to make can inform clinical study design, improving recruitment rates and potential uptake of the treatment of interest.

Incidence of Infusion Reactions and Clinical Effectiveness of Intravenous Golimumab Versus Infliximab in Patients with Rheumatoid Arthritis: The Real-World AWARE Study

OBJECTIVE

Evaluate tolerability and effectiveness of golimumab-IV versus infliximab in patients with rheumatoid arthritis (RA) in a real-world setting.

METHODS

AWARE, a prospective, real-world, pragmatic, observational, multicenter, phase 4 study, enrolled RA patients when initiating golimumab-IV or infliximab. Treatment decisions were made by the treating rheumatologist. The approved doses for RA are 2 mg/kg at weeks 0, 4, then Q8W for golimumab-IV and 3 mg/kg at weeks 0, 2, 6, then Q8W (dose escalation permitted) for infliximab. A prespecified formal interim analysis was conducted. The primary endpoint was the incidence of infusion reactions (any adverse event that occurred during or within 1 h of infusion) through week 52. Major secondary endpoints were mean change from baseline in CDAI at months 6 and 12 in biologic-naïve patients (non-inferiority margin in the CDAI = 6). Baseline characteristics were adjusted using propensity scores with inverse probability of treatment weights (IPTW).

RESULTS

In the formal interim analysis (golimumab-IV, n = 479; infliximab, n = 354), the incidence of infusion reactions was significantly lower with golimumab-IV vs. infliximab (3.6 vs. 17.6%, p < 0.001, IPTW-adjusted). Among biologic-naïve patients, mean changes from baseline in CDAI at month 6 (- 9.5 golimumab-IV vs. - 10.1 infliximab) and at month 12 (- 9.4 golimumab-IV vs. - 10.1 infliximab) demonstrated non-inferiority.

CONCLUSIONS

The proportion of patients with an infusion reaction was significantly lower with golimumab-IV vs. infliximab. Among biologic-naïve patients, mean change from baseline in CDAI at months 6 and 12 was non-inferior for golimumab-IV vs. infliximab. Compared with fixed-dose golimumab-IV, infliximab dose escalation did not provide any greater improvements in CDAI for patients with RA.

TRIAL REGISTRATION

ClinicalTrials.gov identifier, NCT02728934.

Comparison of statistical methodologies used to estimate the treatment effect on time-to-event outcomes in observational studies

The use of real-world data became more and more popular in the pharmaceutical industry. The impact of real-world evidence is now well emphasized by the regulatory authorities. Indeed, the analysis of this type of data can play a key role for treatment efficacy and safety. The aim of this work is to assess various methods and give guidance on the comparisons of drugs, mostly with respect to time-to-event data, in non-randomized studies with potentially confounding variables. For that purpose, several statistical methodologies are compared based on simulation studies. These methodologies belong to family classes of methods that are widely used for this type of problem: regression, matching, weighting and subclassification methods. The evaluation criteria used to compare methods performances are the relative bias, the mean square error, the coverage probability and the width of the confidence interval. In this paper, we consider different scenarios of dataset features in order to study the effect of the sample size, the number of covariates and the magnitude of the treatment effect on the statistical methodologies performances. These statistical analyses are conducted within a proportional hazard model framework. Furthermore, we highlight the advantage of using techniques to identify relevant covariates for time-to-event outcomes by comparing two variable selection methods under a frequentist and a Bayesian inference. Based on simulation results, recommendations on each of the family of methods are provided to guide decision making.

Post-marketing analysis for biosimilar CT-P13 in inflammatory bowel disease compared with external data of originator infliximab in Japan

BACKGROUND AND AIM

CT-P13, an infliximab (IFX) biosimilar, was approved for treatment of inflammatory bowel disease. However, no comparison with the originator IFX in this indication has been conducted in Japan where endemic levels of tuberculosis and hepatitis virus infection are not low. We evaluated the safety and efficacy in real-world data of CT-P13 and compared with originator IFX data in Japan.

METHODS

In a prospective post-marketing surveillance (PMS) study, patients who received CT-P13 in a 28-month period from January 2015 were followed up for 2 years. By conducting Japanese administrative database search (DBS) for the same period of PMS, data of the originator IFX including treatment persistence, tuberculosis incidence, and liver injury were analyzed retrospectively and compared with the corresponding PMS data of CT-P13.

RESULTS

CT-P13 persistence in PMS (n = 640) and IFX persistence in DBS (n = 4113) were almost similar between patients who switched from the originator and patients who continued on the originator, and also between the biologics-naïve patient groups. There were no differences in the incidences of tuberculosis and hepatic injury (Tuberculosis: 2 patients [0.31%] with CT-P13, 10 patients [0.24%] with the originator, P = 0.75; Hepatic injury: 18.5% with CT-P13, 15.4% with the originator, P = 0.22). Most of the patients with hepatic injury continued treatment in PMS and DBS at similar rates (80.8% vs 83.6%, P = 0.65).

CONCLUSION

The results of long-term PMS of CT-P13 compared with external reference data from an administrative database suggested that the biosimilar and its originator were comparably useful in real-world clinical practice.

A Framework for Extension Studies Using Real-World Data to Examine Long-Term Safety and Effectiveness

Understanding the long-term benefits and risks of treatments, devices, and vaccines is critically important for individual- and population-level healthcare decision-making. Extension studies, or ‘roll-over studies,’ are studies that allow for patients participating in a parent clinical trial to ‘roll-over’ into a subsequent related study to continue to observe and measure long-term safety, tolerability, and/or effectiveness. These designs are not new and are often used as an approach to satisfy regulatory post-approval safety requirements. However, designs using traditional clinical trial infrastructure can be expensive and burdensome to conduct, particularly, when following patients for many years post trial completion. Given the increasing availability and access of real-world data (RWD) sources, direct-to-patient technologies, and novel real-world study designs, there are more cost-efficient approaches to conducting extension studies while assessing important long-term outcomes. Here, we describe various fit-for-purpose design options for extension studies, discuss related methodological considerations, and provide scientific and operational guidance on practices when planning to conduct an extension study using RWD. This manuscript is endorsed by the International Society for Pharmacoepidemiology (ISPE).

Systematic review of combinations of targeted or immunotherapy in advanced solid tumors

With rapid advances in our understanding of cancer, there is an expanding number of potential novel combination therapies, including novel-novel combinations. Identifying which combinations are appropriate and in which subpopulations are among the most difficult questions in medical research. We conducted a Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA)-guided systematic review of trials of novel-novel combination therapies involving immunotherapies or molecular targeted therapies in advanced solid tumors. A MEDLINE search was conducted using a modified Cochrane Highly Sensitive Search Strategy for published clinical trials between July 1, 2017, and June 30, 2020, in the top-ranked medical and oncology journals. Trials were evaluated according to a criterion adapted from previously published Food and Drug Administration guidance and other key considerations in designing trials of combinations. This included the presence of a strong biological rationale, the use of a new established or emerging predictive biomarker prospectively incorporated into the clinical trial design, appropriate comparator arms of monotherapy or supportive external data sources and a primary endpoint demonstrating a clinically meaningful benefit. Of 32 identified trials, there were 11 (34%) trials of the novel-novel combination of anti-programmed death 1 (PD-1)/programmed death ligand 1 (PD-L1) and anti-cytotoxic T lymphocyte-associated antigen-4 (CTLA-4) therapy, and 10 (31%) trials of anti-PD-1/PD-L1 and anti-vascular endothelial growth factor (VEGF) combination therapy. 20 (62.5%) trials were phase II trials, while 12 (37.5%) were phase III trials. Most (72%) trials lacked significant preclinical evidence supporting the development of the combination in the given indication. A majority of trials (69%) were conducted in biomarker unselected populations or used pre-existing biomarkers within the given indication for patient selection. Most studies (66%) were considered to have appropriate comparator arms or had supportive external data sources such as prior studies of monotherapy. All studies were evaluated as selecting a clinically meaningful primary endpoint. In conclusion, designing trials to evaluate novel-novel combination therapies presents numerous challenges to demonstrate efficacy in a comprehensive manner. A greater understanding of biological rationale for combinations and incorporating predictive biomarkers may improve effective evaluation of combination therapies. Innovative statistical methods and increasing use of external data to support combination approaches are potential strategies that may improve the efficiency of trial design. Designing trials to evaluate novel-novel combination therapies presents numerous challenges to demonstrate efficacy in a comprehensive manner. A greater understanding of biological rationale for combinations and incorporating predictive biomarkers may improve effective evaluation of combination therapies. Innovative statistical methods and increasing use of external data to support combination approaches are potential strategies that may improve the efficiency of trial design.

The Impact of Baseline Risk Factors on the Incidence of Febrile Neutropenia in Breast Cancer Patients Receiving Chemotherapy with Pegfilgrastim Prophylaxis: A Real-World Data Analysis

Background: There are sparse data addressing whether standard risk factors for febrile neutropenia (FN) are relevant in patients receiving myelosuppressive chemotherapy and primary prophylaxis for FN, which would have implications for variables to consider during real-world comparative analyses of FN incidence. Objective: To assess the impact of baseline patient-specific risk factors and regimen risk on the incidence of FN in patients receiving pegfilgrastim primary prophylaxis. Methods: This was a retrospective observational study in patients with breast cancer (BC) who received myelosuppressive chemotherapy and prophylactic pegfilgrastim identified January 1, 2017-May 31, 2018 from MarketScan® research databases. The outcomes were defined as incidence of FN in the first cycle and among all cycles of chemotherapy using three different definitions for FN. Logistic regression and generalized estimating equations models were used to compare outcomes among patients with and without patient-specific risk factors and among those receiving regimens categorized as high-, intermediate-, or other-risk for FN (low-risk or undefinable by clinical practice guidelines). Results: A total of 4460 patients were identified. In the first cycle of therapy, patients receiving intermediate-risk regimens were at up to 2 times higher risk for FN across all definitions than those receiving high-risk regimens (P<0.01). The odds ratio for main FN among patients with ≥4 versus 0 risk factors was 15.8 (95% confidence interval [CI]: 1.5, 169.4; P<0.01). Patients with ≥3 FN risk factors had significantly greater risks for FN across all cycles of treatment than those with no risk factors; this was true for all FN definitions. Discussion: The choice of FN definition significantly changed the impact of risk factors on the FN outcomes in our study, demonstrating the importance of evaluating all proxies for true FN events in a database study. This is particularly important during real-world study planning where potential missteps may lead to bias or confounding effects that render a study meaningless. Conclusions: In patients with BC receiving chemotherapy with pegfilgrastim prophylaxis, patient-specific risk factors and regimen risk levels are determinants of FN risk. In real-world studies evaluating FN incidence, it is imperative to consider and control for these risk factors when conducting comparative analyses.

EA3: A softmax algorithm for evidence appraisal aggregation

Real World Evidence (RWE) and its uses are playing a growing role in medical research and inference. Prominently, the 21st Century Cures Act—approved in 2016 by the US Congress—permits the introduction of RWE for the purpose of risk-benefit assessments of medical interventions. However, appraising the quality of RWE and determining its inferential strength are, more often than not, thorny problems, because evidence production methodologies may suffer from multiple imperfections. The problem arises to aggregate multiple appraised imperfections and perform inference with RWE. In this article, we thus develop an evidence appraisal aggregation algorithm called EA³. Our algorithm employs the softmax function—a generalisation of the logistic function to multiple dimensions—which is popular in several fields: statistics, mathematical physics and artificial intelligence. We prove that EA³ has a number of desirable properties for appraising RWE and we show how the aggregated evidence appraisals computed by EA³ can support causal inferences based on RWE within a Bayesian decision making framework. We also discuss features and limitations of our approach and how to overcome some shortcomings. We conclude with a look ahead at the use of RWE.

Characteristics of Clinical Studies Used for US Food and Drug Administration Supplemental Indication Approvals of Drugs and Biologics, 2017 to 2019

IMPORTANCE

After US Food and Drug Administration (FDA) approval of a new drug, sponsors can submit additional clinical data to obtain supplemental approval for use for new indications.

OBJECTIVE

To characterize pivotal trials supporting recent supplemental new indication approvals of drugs and biologics by the FDA and to compare them with pivotal trials that supported these therapeutics' original indication approvals.

DESIGN, SETTING, AND PARTICIPANTS

This is a cross-sectional study characterizing pivotal trials supporting supplemental indication approvals by the FDA between 2017 and 2019 and pivotal trials that supported these therapeutics' original indication approvals. Data analysis was performed from August to October 2020.

MAIN OUTCOMES AND MEASURES

Number and design of pivotal trials supporting both supplemental and original indication approvals.

RESULTS

From 2017 to 2019, the FDA approved 146 supplemental indications for 107 therapeutics on the basis of 181 pivotal efficacy trials. The median (interquartile range) number of trials per supplemental indication was 1 (1-1). Most trials used either placebo (77 trials [42.5%; 95% CI, 35.6%-49.8%]) or active comparators (65 trials [35.9%; 95% CI, 29.3%-43.1%]), and most of these multigroup trials were randomized (141 trials [99.3%; 95% CI, 96.0%-100.0%]) and double-blinded (106 trials [74.5%; 95% CI, 66.6%-81.0%]); 80 trials (44.2%; 95 CI, 37.2%-51.5%) used clinical outcomes as the primary efficacy end point. There was no difference between oncology therapies and those approved for other therapeutic areas to have supplemental indication approvals be based on at least 2 pivotal trials (11.5% vs 20.6%; difference, 9.1%; 95% CI, 2.9%-21.0%; P = .10). Similarly, there was no difference in use of randomization (98.3% vs 100.0%; difference, 1.7%; 95% CI, 1.6%-5.0%; P = .43) among multigroup trials, although these trials were less likely to be double-blinded (50.8% vs 92.3%; difference, 41.5%; 95% CI, 27.4%-55.5%; P < .001); overall, these trials were less likely to use either placebo or active comparators (64.9% vs 86.7%; difference, 21.8% 95% CI, 9.8%-33.9%; P < .001) or to use clinical outcomes as their primary efficacy end point (27.5% vs 61.1%; difference, 33.6%; 95% CI, 14.1%-40.9%; P < .001) and were longer (median [interquartile range], 17 [6-48] weeks vs 95 [39-146] weeks). Original approvals were more likely than supplemental indication approvals to be based on at least 2 pivotal trials (44.0% [95% CI, 33.7%-42.6%] vs 15.8% [95% CI, 10.7%-22.5%]; difference, 28.2%; 95% CI, 17.6%-39.6%; P < .001) and less likely to be supported by at least 1 trial of 12 months' duration (27.6% [95% CI, 17.9%-35.0%] vs 54.8% [95% CI, 46.7%-62.6%]; difference, 27.2%; 95% CI, 14.5%-37.8%; P < .001). Pivotal trial designs were otherwise not significantly different.

CONCLUSIONS AND RELEVANCE

These findings suggest that the number and design of the pivotal trials supporting supplemental indication approvals by the FDA varied across therapeutic areas, with the strength of evidence for cancer indications weaker than that for other indications. There was little difference in the design characteristics of the pivotal trials supporting supplemental indication and original approvals.

Integrating Real-World Evidence in the Regulatory Decision-Making Process: A Systematic Analysis of Experiences in the US, EU, and China Using a Logic Model

Real world evidence (RWE) and real-world data (RWD) are drawing ever-increasing attention in the pharmaceutical industry and drug regulatory authorities (DRAs) all over the world due to their paramount role in supporting drug development and regulatory decision making. However, there is little systematic documentary analysis about how RWE was integrated for the use by the DRAs in evaluating new treatment approaches and monitoring post-market safety. This study aimed to analyze and discuss the integration of RWE into regulatory decision-making process from the perspective of DRAs. Different development strategies to develop and adopt RWE by the DRAs in the US, Europe, and China were reviewed and compared, and the challenges encountered were discussed. It was found that different strategies on development of RWE were applied by FDA, EMA, and NMPA. The extent to which RWE was adopted in China was relatively limited compared to that in the US and EU, which was highly related to the national pharmaceutical environment and development stages. A better understanding of the overall goals, inputs, activities, outputs, and outcomes in developing RWE will help inform actions to harness RWD and leverage RWE for better health care decisions.

Use of real-world evidence for oncology clinical decision making in emerging economies

Real-world evidence (RWE) can provide insights into patient profiles, disease detection, treatment choice, dosing strategies, treatment sequencing, adverse event management and financial toxicity associated with oncology treatment. However, the full potential of RWE is untapped in emerging economies due to structural and behavioral factors. Structural barriers include lack of regulatory engagement, real-world data availability, quality and integrity. Behavioral barriers include entrenched healthcare professional behaviors that impede rapid RWE understanding and adoption. These barriers can be addressed with close collaboration of healthcare stakeholders; of whom, regulators need to be at the forefront given their ability to facilitate use of RWE in healthcare policy and legislation.

The use of external controls: To what extent can it currently be recommended?

With more and better clinical data being captured outside of clinical studies and greater data sharing of clinical studies, external controls may become a more attractive alternative to randomized clinical trials (RCTs). Both industry and regulators recognize that in situations where a randomized study cannot be performed, external controls can provide the needed contextualization to allow a better interpretation of studies without a randomized control. It is also agreed that external controls will not fully replace RCTs as the gold standard for formal proof of efficacy in drug development and the yardstick of clinical research. However, it remains unclear in which situations conclusions about efficacy and a positive benefit/risk can reliably be based on the use of an external control. This paper will provide an overview on types of external control, their applications and the different sources of bias their use may incur, and discuss potential mitigation steps. It will also give recommendations on how the use of external controls can be justified.

Understanding Use of Real-World Data and Real-World Evidence to Support Regulatory Decisions on Medical Product Effectiveness

RWE has potential to provide efficient and relevant information on the effectiveness of medical products, complementing the data generated in clinical trials; however, how RWE can support regulatory decision-making is unclear, potentially limiting its use. The objective of this study was to identify and characterize instances where RWE was included in the evidence package to support the effectiveness of a medical product regulated by U.S. Food and Drug Administration. A retrospective landscape analysis was conducted to identify instances where RWE was submitted to support effectiveness through targeted review of white and gray literature and publicly available FDA reviews of medical products. Trained evaluators examined FDA reviews to determine if and how RWE contributed to regulatory decision-making regarding effectiveness. Evaluators identified 34 instances of RWE submitted between 1954 and 2020, where 26% of instances were for oncology, 18% for hematology, and 12% for neurology. Over 50% of the products were indicated for use in rare disease or pediatric populations. 82% of products where RWE was submitted received an orphan designation. RWE was included in the product label in 59% of instances. Stated reasons indicating why submitted RWE did not significantly contribute to regulatory decision-making included lack of pre-specification of study design and analysis as well as data reliability and relevancy concerns. While there is historical use of RWE to support medical product effectiveness for oncology and rare diseases, potential exists to leverage the strengths of RWE to support other therapeutic areas and capture outcomes that are most relevant to patients.

Emulated Clinical Trials from Longitudinal Real-World Data Efficiently Identify Candidates for Neurological Disease Modification: Examples from Parkinson's Disease

Real-world healthcare data hold the potential to identify therapeutic solutions for progressive diseases by efficiently pinpointing safe and efficacious repurposing drug candidates. This approach circumvents key early clinical development challenges, particularly relevant for neurological diseases, concordant with the vision of the 21st Century Cures Act. However, to-date, these data have been utilized mainly for confirmatory purposes rather than as drug discovery engines. Here, we demonstrate the usefulness of real-world data in identifying drug repurposing candidates for disease-modifying effects, specifically candidate marketed drugs that exhibit beneficial effects on Parkinson's disease (PD) progression. We performed an observational study in cohorts of ascertained PD patients extracted from two large medical databases, Explorys SuperMart (N = 88,867) and IBM MarketScan Research Databases (N = 106,395); and applied two conceptually different, well-established causal inference methods to estimate the effect of hundreds of drugs on delaying dementia onset as a proxy for slowing PD progression. Using this approach, we identified two drugs that manifested significant beneficial effects on PD progression in both datasets: rasagiline, narrowly indicated for PD motor symptoms; and zolpidem, a psycholeptic. Each confers its effects through distinct mechanisms, which we explored via a comparison of estimated effects within the drug classification ontology. We conclude that analysis of observational healthcare data, emulating otherwise costly, large, and lengthy clinical trials, can highlight promising repurposing candidates, to be validated in prospective registration trials, beneficial against common, late-onset progressive diseases for which disease-modifying therapeutic solutions are scarce.

How well can we assess the validity of non-randomised studies of medications? A systematic review of assessment tools

OBJECTIVE

To determine whether assessment tools for non-randomised studies (NRS) address critical elements that influence the validity of NRS findings for comparative safety and effectiveness of medications.

DESIGN

Systematic review and Delphi survey.

DATA SOURCES

We searched PubMed, Embase, Google, bibliographies of reviews and websites of influential organisations from inception to November 2019. In parallel, we conducted a Delphi survey among the International Society for Pharmacoepidemiology Comparative Effectiveness Research Special Interest Group to identify key methodological challenges for NRS of medications. We created a framework consisting of the reported methodological challenges to evaluate the selected NRS tools.

STUDY SELECTION

Checklists or scales assessing NRS.

DATA EXTRACTION

Two reviewers extracted general information and content data related to the prespecified framework.

RESULTS

Of 44 tools reviewed, 48% (n=21) assess multiple NRS designs, while other tools specifically addressed case-control (n=12, 27%) or cohort studies (n=11, 25%) only. Response rate to the Delphi survey was 73% (35 out of 48 content experts), and a consensus was reached in only two rounds. Most tools evaluated methods for selecting study participants (n=43, 98%), although only one addressed selection bias due to depletion of susceptibles (2%). Many tools addressed the measurement of exposure and outcome (n=40, 91%), and measurement and control for confounders (n=40, 91%). Most tools have at least one item/question on design-specific sources of bias (n=40, 91%), but only a few investigate reverse causation (n=8, 18%), detection bias (n=4, 9%), time-related bias (n=3, 7%), lack of new-user design (n=2, 5%) or active comparator design (n=0). Few tools address the appropriateness of statistical analyses (n=15, 34%), methods for assessing internal (n=15, 34%) or external validity (n=11, 25%) and statistical uncertainty in the findings (n=21, 48%). None of the reviewed tools investigated all the methodological domains and subdomains.

CONCLUSIONS

The acknowledgement of major design-specific sources of bias (eg, lack of new-user design, lack of active comparator design, time-related bias, depletion of susceptibles, reverse causation) and statistical assessment of internal and external validity is currently not sufficiently addressed in most of the existing tools. These critical elements should be integrated to systematically investigate the validity of NRS on comparative safety and effectiveness of medications. SYSTEMATIC REVIEW PROTOCOL AND REGISTRATION: https://osf.io/es65q.

Current Advances in Clinical Trials for Rare Disease Populations: Spotlight on the Patient

Characterized by small, highly heterogeneous patient populations, rare disease trials magnify the challenges often encountered in traditional clinical trials. In recent years, there have been increased efforts by stakeholders to improve drug development in rare diseases through novel approaches to clinical trial designs and statistical analyses. We highlight and discuss some of the current and emerging approaches aimed at overcoming challenges in rare disease clinical trials, with a focus on the ultimate stakeholder, the patient.

How to choose a time zero for patients in external control arms

When a sponsor carries out a single-arm trial of a novel oncology compound, it may wish to assess the efficacy of the compound via comparison of overall survival to an external control arm, constructed using patients included in some retrospective registry. If efficacy of the novel compound is compared to efficacy of physician's choice of chemotherapy, patients in the retrospective registry might qualify for inclusion in the external control arm at multiple different points in time, when they receive different chemotherapy treatments. For example, a patient might qualify at the start of their second, third and fourth lines of therapy. From the start of which line of therapy should this patient's survival be compared to survival of participants in the single-arm trial? Some sponsors have elected to include patients in the external control arm from the last available line of therapy in the retrospective database. Another possibility is to randomly select a line of therapy for each external control arm patient from among those available. In this paper, we show, via probabilistic arguments and also via simulation based on real data, that both of these methods give rise to a bias in favor of the single-arm trial. We further show that this bias can be avoided by instead including external control arm patients multiple times in the external control arm, once for each time they receive qualifying treatment.

Assessing the uncertainty of treatment outcomes in a previous systematic review of venous leg ulcer randomized controlled trials: Additional secondary analysis

In this secondary analysis of a previous systematic review, we assessed randomized controlled trials evaluating treatments of venous leg ulcers in terms of factors that affect risk of bias at the study level and thus uncertainty of outcomes obtained from the interventions. Articles that assessed the wound bed condition in venous leg ulcers and that were published in English between 1998 and May 22, 2018 were previously searched in PubMed, Embase, CINAHL, CENTRAL, Scopus, Science Direct, and Web of Science. Duplicates and retracted articles were excluded. The following data were extracted to assess the risk of bias: treatment groups; primary and secondary endpoints that were statistically tested between groups, including their results and p values; whether blinding of patients and assessors was done; whether allocation concealment was adequate; whether an intention‐to‐treat analysis was conducted; whether an appropriate power calculation was correctly done; and whether an appropriate multiplicity adjustment was made, as necessary. Pre‐ and post‐study power calculations were made. The step‐up Hochberg procedure adjusted for multiplicity. Results were analysed for all studies, pre‐2013 studies, and 2013/post‐2013 studies. We included 142 randomized controlled trials that evaluated 14,141 patients. Most studies lacked blinding (72.5–77.5%) and allocation concealment (88.7%). Only 49.3% of trials provided a power calculation, with 27.5% having an appropriate calculation correctly done. Adequate statistical power of the primary endpoint was found in 27.2% of trials. The lack of multiplicity adjustment in 98.6% of studies affected the uncertainty of outcomes in 20% of studies, with the majority of the secondary endpoints (67.7%) in those studies becoming non‐significant after multiplicity adjustment. Recent studies tended to weakly demonstrate improved certainty of outcomes. Venous leg ulcer randomized controlled trials have a high degree of uncertainty associated with treatment outcomes. Greater attention to trial design and conduct is needed to improve the evidence base.

Demonstrating that Real World Evidence Is Fit-For-Purpose to Support Labeling: Parallels to Patient Reported Outcomes in the Pursuit of Labeling Claims

INTRODUCTION

In December 2021, U.S. Food & Drug Administration (FDA) will issue guidance on the use of real-world evidence (RWE) to support new indications or expanded product labeling. While difficult to foresee what FDA will require, learnings can be gleaned from previous paradigm shifts at FDA, such as for patient reported outcomes (PROs) in 2006-2009.

METHODS

We contrast published requirements for justifying PROs as fit-for-purpose for a specific labeling claim with a potential approach to justify RWE as fit-for-purpose to support expanded labeling or a new indication.

RESULTS

PRO labeling claims require a PRO Evidence Dossier that includes: specific wording of claim, clinical trial hypothesis structure and endpoint model, and justification that the PRO is relevant and meaningful to patients in the target population (content validity) with adequate psychometric properties. FDA's 2018 RWE Framework outlined critical considerations for using RWE to support regulatory decisions, including data quality, relevancy, provenance, and transparency. Strong parallels exist between the evidence required to justify that PROs are fit-for-purpose to support specific labeling claims and evidence to justify RWE as fit-for-purpose for specific research questions and labeling. Early discussion with FDA is encouraged.

CONCLUSION

Drawing on parallels with use of PROs in labeling, RWE for regulatory purposes should be evaluated within the context of specific labeling or indication, specific study design and analysis plans, and the data attributes of data source. Sponsors seeking a new indication or labeling expansion based on RWE should justify that a specific data source and specific study design are fit-for-purpose.

Bridging the Gap Between RCTs and RWE Through Endpoint Selection

This commentary is authored by several industry real-world evidence (RWE) experts, with support from IQVIA, as part of the 'RWE Leadership Forum': a group of Industry Leaders who have come together as non-competitive partners to understand and respond to RWD/E challenges and opportunities with a single expert voice. Here, the forum discusses the value in bridging the industry disconnect between RTCs and RWE, with a view to promoting the use of RWE in the RCT environment. RCT endpoints are explored along several axes including their clinical relevance and their measure of direct patient benefit, and then compared with their real-world counterparts to identify suitable paths, or gaps, for assimilating RWE endpoints into the RCT environment.

Single-arm Trials With External Comparators and Confounder Misclassification: How Adjustment Can Fail

BACKGROUND

"Single-arm trials" with external comparators that contrast outcomes in those on experimental therapy to real-world patients have been used to evaluate efficacy and safety of experimental drugs in rare and severe diseases. Regulatory agencies are considering expanding the role these studies can play; guidance thus far has explicitly considered outcome misclassification with little discussion of misclassification of confounding variables.

OBJECTIVES

This work uses causal diagrams to illustrate how adjustment for a misclassified confounder can result in estimates farther from the truth than ignoring it completely. This theory is augmented with quantitative examples using plausible values for misclassification of smoking in real-world pharmaceutical claims data. A tool is also provided for calculating bias of adjusted estimates with specific input parameters.

RESULTS

When confounder misclassification is similar in both data sources, adjustment generally brings estimates closer to the truth. When it is not, adjustment can generate estimates that are considerably farther from the truth than the crude. While all nonrandomized studies are subject to this potential bias, single-arm studies are particularly vulnerable due to perfect alignment of confounder measurement and treatment group. This is most problematic when the prevalence of the confounder does not differ between data sources and misclassification does, but can occur even with strong confounder-data source associations.

DISCUSSION

Researchers should consider differential confounder misclassification when designing protocols for these types of studies. Subsample validation of confounders, followed by imputation or other bias correction methods, may be a key tool for combining trial and real-world data going forward.

Expanded Access Programs, compassionate drug use, and Emergency Use Authorizations during the COVID-19 pandemic

The US Food and Drug Administration (FDA) Expanded Access (EA) Program, which allows for compassionate uses of unapproved therapeutics and diagnostics outside of clinical trials, has gained significant traction during the Coronavirus 2019 (COVID-19) pandemic. While development of vaccines has been the major focus, uncertainties around new vaccine safety and effectiveness have spawned interest in other pharmacological options. Experimental drugs can also be approved under the FDA Emergency Use Authorization (EUA) program, designed to combat infectious disease and other threats. Here, we review the US experience in 2020 with pharmacological EA and EUA approvals during the pandemic. We also provide a description of, and clinical rationale for, each of the EA- or EUA-approved drugs (e.g. remdesivir, convalescent plasma, propofol 2%, hydroxychloroquine, ruxolitinib, bamlanivimab, baricitinib, casirivimab plus imdevimab) during the pandemic and concluding reflections on the EA program and its potential future uses.

Harnessing real-world evidence to reduce the burden of noncommunicable disease: health information technology and innovation to generate insights

Noncommunicable diseases (NCDs) are the leading causes of mortality and morbidity across the world and factors influencing global poverty and slowing economic development. We summarize how the potential power of real-world data (RWD) and real-world evidence (RWE) can be harnessed to help address the disease burden of NCDs at global, national, regional and local levels. RWE is essential to understand the epidemiology of NCDs, quantify NCD burdens, assist with the early detection of vulnerable populations at high risk of NCDs by identifying the most influential risk factors, and evaluate the effectiveness and cost-benefits of treatments, programs, and public policies for NCDs. To realize the potential power of RWD and RWE, challenges related to data integration, access, interoperability, standardization of analytical methods, quality control, security, privacy protection, and ethical standards for data use must be addressed. Finally, partnerships between academic centers, governments, pharmaceutical companies, and other stakeholders aimed at improving the utilization of RWE can have a substantial beneficial impact in preventing and managing NCDs.

Harnessing Real-World Data for Regulatory Use and Applying Innovative Applications

A vast quantity of real-world data (RWD) are available to healthcare researchers. Such data come from diverse sources such as electronic health records, insurance claims and billing activity, product and disease registries, medical devices used in the home, and applications on mobile devices. The analysis of RWD produces real-world evidence (RWE), which is clinical evidence that provides information about usage and potential benefits or risks of a drug. This review defines and explains RWD, and it also details how regulatory authorities are using RWD and RWE. The main challenges in harnessing RWD include collating and analyzing numerous disparate types or categories of available information including both structured (eg, field entries) and unstructured (eg, doctor notes, discharge summaries, social media posts) data. Although the use of artificial intelligence to capture, amalgamate, standardize, and analyze RWD is still evolving, it has the potential to support the increased use of RWE to improve global health and healthcare.

Trial designs using real-world data: The changing landscape of the regulatory approval process

Purpose

There is a need to develop hybrid trial methodology combining the best parts of traditional randomized controlled trials (RCTs) and observational study designs to produce real‐world evidence (RWE) that provides adequate scientific evidence for regulatory decision‐making.

Methods

This review explores how hybrid study designs that include features of RCTs and studies with real‐world data (RWD) can combine the advantages of both to generate RWE that is fit for regulatory purposes.

Results

Some hybrid designs include randomization and use pragmatic outcomes; other designs use single‐arm trial data supplemented with external comparators derived from RWD or leverage novel data collection approaches to capture long‐term outcomes in a real‐world setting. Some of these approaches have already been successfully used in regulatory decisions, raising the possibility that studies using RWD could increasingly be used to augment or replace traditional RCTs for the demonstration of drug effectiveness in certain contexts. These changes come against a background of long reliance on RCTs for regulatory decision‐making, which are labor‐intensive, costly, and produce data that can have limited applicability in real‐world clinical practice.

Conclusions

While RWE from observational studies is well accepted for satisfying postapproval safety monitoring requirements, it has not commonly been used to demonstrate drug effectiveness for regulatory purposes. However, this position is changing as regulatory opinions, guidance frameworks, and RWD methodologies are evolving, with growing recognition of the value of using RWE that is acceptable for regulatory decision‐making.