European Medicines Agency

Abstract Background The process of generating ‘signals’ of possible unrecognized hazards from spontaneous adverse drug reaction reporting data has been likened to looking for a needle in a haystack. However, statistical approaches to the data have been underutilised. Methods Using the UK Yellow Card database, we have developed and evaluated a statistical aid to signal generation called a Proportional Reporting Ratio (PRR). The proportion of all reactions to a drug which are for a particular medical condition of interest is compared to the same proportion for all drugs in the database, in a 2 × 2 table. We investigated a group of newly‐marketed drugs using as minimum criteria for a signal, 3 or more cases, PRR at least 2, chi‐squared of at least 4. Findings The database was used to examine retrospectively 15 drugs newly‐marketed in the UK, with the highest levels of ADR reporting. The method identified 481 signals meeting the minimum criteria during the period 1996–8. Further evaluation of these showed that 70% were known adverse reactions, 13% were events which were likely to be related to the underlying disease and 17% were signals requiring further evaluation. Implications Proportional reporting ratios are a valuable aid to signal generation from spontaneous reporting data which are easy to calculate and interpret, and various refinements are possible. © Crown copyright 2001. Reproduced with the permission of Her Majesty's Stationary Office. Published by John Wiley & Sons, Ltd.

AIMS: This report from the European Society of Cardiology (ESC) Atlas Project updates and expands upon the widely cited 2019 report in presenting cardiovascular disease (CVD) statistics for the 57 ESC member countries. METHODS AND RESULTS: Statistics pertaining to 2019, or the latest available year, are presented. Data sources include the World Health Organization, the Institute for Health Metrics and Evaluation, the World Bank, and novel ESC sponsored data on human and capital infrastructure and cardiovascular healthcare delivery. New material in this report includes sociodemographic and environmental determinants of CVD, rheumatic heart disease, out-of-hospital cardiac arrest, left-sided valvular heart disease, the advocacy potential of these CVD statistics, and progress towards World Health Organization (WHO) 2025 targets for non-communicable diseases. Salient observations in this report: (i) Females born in ESC member countries in 2018 are expected to live 80.8 years and males 74.8 years. Life expectancy is longer in high income (81.6 years) compared with middle-income (74.2 years) countries. (ii) In 2018, high-income countries spent, on average, four times more on healthcare than middle-income countries. (iii) The median PM2.5 concentrations in 2019 were over twice as high in middle-income ESC member countries compared with high-income countries and exceeded the EU air quality standard in 14 countries, all middle-income. (iv) In 2016, more than one in five adults across the ESC member countries were obese with similar prevalence in high and low-income countries. The prevalence of obesity has more than doubled over the past 35 years. (v) The burden of CVD falls hardest on middle-income ESC member countries where estimated incidence rates are ∼30% higher compared with high-income countries. This is reflected in disability-adjusted life years due to CVD which are nearly four times as high in middle-income compared with high-income countries. (vi) The incidence of calcific aortic valve disease has increased seven-fold during the last 30 years, with age-standardized rates four times as high in high-income compared with middle-income countries. (vii) Although the total number of CVD deaths across all countries far exceeds the number of cancer deaths for both sexes, there are 15 ESC member countries in which cancer accounts for more deaths than CVD in males and five-member countries in which cancer accounts for more deaths than CVD in females. (viii) The under-resourced status of middle-income countries is associated with a severe procedural deficit compared with high-income countries in terms of coronary intervention, ablation procedures, device implantation, and cardiac surgical procedures. CONCLUSION: Risk factors and unhealthy behaviours are potentially reversible, and this provides a huge opportunity to address the health inequalities across ESC member countries that are highlighted in this report. It seems clear, however, that efforts to seize this opportunity are falling short and present evidence suggests that most of the WHO NCD targets for 2025 are unlikely to be met across ESC member countries.

The aim of an equivalence trial is to show the therapeutic equivalence of two treatments, usually a new drug under development and an existing drug for the same disease used as a standard active comparator. Unfortunately the principles that govern the design, conduct, and analysis of equivalence trials are not as well understood as they should be. Consequently such trials often include too few patients or have intrinsic design biases which tend towards the conclusion of no difference. In addition the application of hypothesis testing in analysing and interpreting data from such trials sometimes compounds the drawing of inappropriate conclusions, and the inclusion and exclusion of patients from analysis may be poorly managed. The design of equivalence trials should mirror that of earlier successful trials of the active comparator as closely as possible. Patient losses and other deviations from the protocol should be minimised; analysis strategies to deal with unavoidable problems should not centre on an "intention to treat" analysis but should seek to show the similarity of results from a range of approaches. Analysis should be based on confidence intervals, and this also carries implications for the estimation of the required numbers of patients at the design stage.

The chemical composition of St. John's wort has been well-studied. Documented pharmacological activities, including antidepressant, antiviral, and antibacterial effects, provide supporting evidence for several of the traditional uses stated for St John's wort. Many pharmacological activities appear to be attributable to hypericin and to the flavonoid constituents; hypericin is also reported to be responsible for the photosensitive reactions that have been documented for St. John's wort. With regard to the antidepressant effects of St John's wort, hyperforin, rather than hypericin as originally thought, has emerged as one of the major constituents responsible for antidepressant activity. Further research is required to determine which other constituents contribute to the antidepressant effect. Evidence from randomised controlled trials has confirmed the efficacy of St John's wort extracts over placebo in the treatment of mild-to-moderately severe depression. Other randomised controlled studies have provided some evidence that St John's wort extracts are as effective as some standard antidepressants in mild-to-moderate depression. There is still a need for further trials to assess the efficacy of St John's wort extracts, compared with that of standard antidepressants, particularly newer antidepressant agents, such as the selective serotonin reuptake inhibitors (recent comparative studies with fluoxetine and sertraline have been conducted). Also, there is a need for further studies in well-defined groups of patients, in different types of depression, and conducted over longer periods in order to determine long-term safety. St John's wort does appear to have a more favourable short-term safety profile than do standard antidepressants, a factor that is likely to be important in patients continuing to take medication. Concerns have been raised over interactions between St John's wort and certain prescribed medicines (including warfarin, ciclosporin, theophylline, digoxin, HIV protease inhibitors, anticonvulsants, selective serotonin reuptake inhibitors, triptans, oral contraceptives); advice is that patients taking these medicines should stop taking St John's wort, generally after seeking professional advice as dose adjustment of conventional treatment may be necessary.

After evaluating a safety signal regarding pancreatitis and pancreatic cancer in patients using incretin-based drugs, the Food and Drug Administration and the European Medicines Agency conclude that assertions of a causal association are inconsistent with the data. With approximately 25.8 million diabetic patients in the United States and 33 million in the European Union alone, the growing prevalence of diabetes worldwide poses a major public health challenge. Both the U.S. Food and Drug Administration (FDA) and the European Medicines Agency (EMA) are committed to ensuring the safety of drug products marketed for the treatment of diabetes, and postmarketing reports of pancreatitis and pancreatic cancer in patients taking certain antidiabetic medications have been of concern to both agencies. Working in parallel, the agencies have reviewed nonclinical toxicology studies, clinical trial data, and epidemiologic data pertaining to blood glucose-lowering ...

This paper reviews the chemistry, pharmacology and clinical properties of Echinacea species used medicinally. The Echinacea species Echinacea angustifolia, Echinacea pallida and Echinacea purpurea have a long history of medicinal use for a variety of conditions, particularly infections, and today echinacea products are among the best-selling herbal preparations in several developed countries. Modern interest in echinacea is focused on its immunomodulatory effects, particularly in the prevention and treatment of upper respiratory tract infections. The chemistry of Echinacea species is well documented, and several groups of constituents, including alkamides and caffeic acid derivatives, are considered important for activity. There are, however, differences in the constituent profile of the three species. Commercial echinacea samples and marketed echinacea products may contain one or more of the three species, and analysis of samples of raw material and products has shown that some do not meet recognized standards for pharmaceutical quality. Evidence from preclinical studies supports some of the traditional and modern uses for echinacea, particularly the reputed immunostimulant (or immunomodulatory) properties. Several, but not all, clinical trials of echinacea preparations have reported effects superior to those of placebo in the prevention and treatment of upper respiratory tract infections. However, evidence of efficacy is not definitive as studies have included different patient groups and tested various different preparations and dosage regimens of echinacea. On the basis of the available limited safety data, echinacea appears to be well tolerated. However, further investigation and surveillance are required to establish the safety profiles of different echinacea preparations. Safety issues include the possibility of allergic reactions, the use of echinacea by patients with autoimmune diseases and the potential for echinacea preparations to interact with conventional medicines.

AIMS: The aim of this work is to identify the medicines which interact with the herbal remedy St John's wort (SJW), and the mechanisms responsible. METHODS: A systematic review of all the available evidence, including worldwide published literature and spontaneous case reports provided by healthcare professionals and regulatory authorities within Europe has been undertaken. RESULTS: A number of clinically significant interactions have been identified with prescribed medicines including warfarin, phenprocoumon, cyclosporin, HIV protease inhibitors, theophylline, digoxin and oral contraceptives resulting in a decrease in concentration or effect of the medicines. These interactions are probably due to the induction of cytochrome P450 isoenzymes CYP3A4, CYP2C9, CYP1A2 and the transport protein P-glycoprotein by constituent(s) in SJW. The degree of induction is unpredictable due to factors such as the variable quality and quantity of constituent(s) in SJW preparations. In addition, possible pharmacodynamic interactions with selective serotonin re-uptake inhibitors and serotonin (5-HT(1d)) receptor-agonists such as triptans used to treat migraine were identified. These interactions are associated with an increased risk of adverse reactions. CONCLUSIONS: In Sweden and the UK the potential risks to patients were judged to be significant and therefore information about the interactions was provided to health care professionals and patients. The product information of the licensed medicines involved has been amended to reflect these newly identified interactions and SJW preparations have been voluntarily labelled with appropriate warnings.

Background: Autologous adipose-derived stem cells may represent a novel approach for the management of complex fistula-in-ano. After successful phase I and II clinical trials, a phase III trial was performed to investigate the safety and efficacy. Design: In this multicenter, randomized, single-blind, add-on clinical trial, 200 adult patients from 19 centers were randomly assigned to receive 20 million stem cells (group A, 64 patients), 20 million adipose-derived stem cells plus fibrin glue (group B, 60 patients), or fibrin glue (group C, 59 patients) after closure of the internal opening. Fistula healing was defined as reepithelization of the external opening and absence of collection >2 cm by MRI. If the fistula had not healed at 12 weeks, a second dose (40 million stem cells in groups A and B) was administered. Patients were evaluated at 24 to 26 weeks (primary end point) and at 1 year (long-term follow-up). Results: All results are according to the “blinded evaluator” assessment. After 24 to 26 weeks, the healing rate was 39.1%, 43.3%, 37.3% in groups A, B, and C (p = 0.79). At 1 year, the healing rates were 57.1%, 52.4%, and 37.3 % (p = 0.13). On analysis of the subpopulation treated at the technique’s pioneer center, healing rates were 54.55%, 83.33%, and 18.18%, at 24 to 26 weeks (p < 0.001). No SAEs were reported. Conclusions: In treatment of complex fistula-in-ano, a dose of 20 or 60 million adipose-derived stem cells alone or in combination with fibrin glue was considered a safe treatment, achieving healing rates of approximately 40% at 6 months and of more than 50% at 1-year follow-up. It was equivalent to fibrin glue alone. No statistically significant differences were found when the 3 groups where compared. Clinical trials registration: www.clinicaltrials.gov, identifier NCT00475410; Sponsor, Cellerix SA.

Sarcopenia, the age-related skeletal muscle decline, is associated with relevant clinical and socioeconomic negative outcomes in older persons. The study of this phenomenon and the development of preventive/therapeutic strategies represent public health priorities. The present document reports the results of a recent meeting of the International Working Group on Sarcopenia (a task force consisting of geriatricians and scientists from academia and industry) held on June 7-8, 2011 in Toulouse (France). The meeting was specifically focused at gaining knowledge on the currently available biomarkers (functional, biological, or imaging-related) that could be utilized in clinical trials of sarcopenia and considered the most reliable and promising to evaluate age-related modifications of skeletal muscle. Specific recommendations about the assessment of aging skeletal muscle in older people and the optimal methodological design of studies on sarcopenia were also discussed and finalized. Although the study of skeletal muscle decline is still in a very preliminary phase, the potential great benefits derived from a better understanding and treatment of this condition should encourage research on sarcopenia. However, the reasonable uncertainties (derived from exploring a novel field and the exponential acceleration of scientific progress) require the adoption of a cautious and comprehensive approach to the subject.

Real-world data (RWD) offers the possibility to derive novel insights on the use and performance of medicines in everyday clinical use, complementing rather than competing with evidence from randomized control trials. While Europe is rich in healthcare data, its heterogeneous nature brings operational, technical, and methodological challenges. We present a number of potential solutions to address the full spectrum of regulatory use cases and emphasize the importance of early planning of data collection. There is increasing interest in the use of real-world data (RWD) to support regulatory decision making across the product life cycle. Key sources of RWD are electronic health records, claims data, prescription data, and patient registries. Increasingly incorporated into the definition is data from wearables, m-health apps, and environmental data including data on social status, education, and other lifestyle factors. These latter data offer much promise to deliver a holistic picture of an individual's health status but from a regulatory standpoint present substantial challenges in deriving actionable evidence. From the perspective of the European Medicines Agency (EMA), RWD are defined as “routinely collected data relating to a patient's health status or the delivery of health care from a variety of sources other than traditional clinical trials.” We specifically exclude traditional clinical trials even if single arm but would incorporate data from pragmatic clinical trials if data were collected remotely through an electronic health record or other observational data source and solely under conditions of normal clinical care.1 Real-world evidence (RWE) is then defined as the information derived from analysis of RWD, and it is the acceptability of this evidence for regulatory decision making in different use cases across the product life that has become the subject of intense debate. The use of RWD to support regulatory decision making is not new. For decades such data have been used for safety signal evaluation, risk management and for studies to support life cycle benefit-risk evaluation;2, 3 contexts where opportunities to capture data, especially in a timely fashion, are more limited and where multiple sources of information of varying quality from multiple stakeholders must be balanced to inform decision making. In fact, for pharmacovigilance decisions, it could be argued that it is essential that safety is understood in the context of how care is delivered rather than under the stringent and highly monitored conditions of the clinical trial. To directly support EMA committees, the EMA is routinely using three real-world databases for in-house studies and over recent years has commissioned 15 external studies, most of them multidatabase and multinational. It is also well recognized that RWD are an underutilized source for assessing the public health impact of risk minimization measures, including any unintended consequences4 and for informing health technology assessment, pricing, and reimbursement decisions.5 The natural extension to these safety-orientated questions includes disease characterization and prevalence, understanding current standard of care, and confirming the clinical outcome of short term surrogate markers. To date, however, there is a lower acceptability of RWD where the outcome of interest is efficacy/effectiveness.6 Great caution is generally exercised where positive regulatory decisions result in patients being exposed to a new medical product, and hence an estimate of efficacy free from the biases of observational data is required.7 The best available standard of evidence to date has been the randomized control trial (RCT). The RCT will, in our view, remain the best available standard and be required in many circumstances, but the rapid pace of change in the scientific and technological landscapes is shifting the regulatory landscape. We are seeing an increasing number of products that face challenges to align with the traditional drug development pathway; often these are advanced therapies or orphan products for conditions with significant unmet need and for which a traditional RCT may be unfeasible or unethical. Table S1 provides recent examples where RWE has been pivotal for European regulatory decisions in terms of supporting the initial regulatory decision or postmarketing obligations. For many of these examples, the need was to enable both safe and early access to promising medicines for patients with limited treatment options or when uncertainties around the medicines remained. Where sufficient efficacy is demonstrated but uncertainties exist around long-term safety and efficacy (Strimvelis, nusinersen (Spinraza)), postauthorization evidence generation coupled with adequate pharmacovigilance activities needs to be in place to quickly address uncertainties. However, where available evidence of efficacy requires contextualization, there have been examples where RWD provided an external control arm (Zalmoxis), were used to confirm a response rate in a single-arm trial (axicabtagene ciloleucel (Yescarta), tisagenlecleucel (Kymirah)) or provided data to extend an indication (eculizumab (Soliris)). As personalized medicine becomes a closer reality, it is anticipated that such examples are likely to increase. From a European perspective, utilizing RWD is faced with operational, technical, and methodological challenges, but possible solutions exist (Table 1). Operational challenges include feasibility, governance, and sustainability issues, which complicate access to and the routine use of multiple national data sources, many of which will have different legal and ethical requirements for sharing data. As a minimum, appropriate consents and data anonymization techniques are required to ensure data privacy obligation requirements are met; while of paramount importance, current operational processes designed to address obligations may prevent efficient and timely delivery of data, which may be particularly problematic in the context of safety decisions where urgent access to data is needed to inform a regulatory decision. Technical Technical Technological challenges describe those associated with the data, and solutions require addressing differences in terminologies, data formats, quality, and content that exist across multiple European databases. Europe is fortunate in the richness of its healthcare data and in particular its longitudinal nature, which stems from the principle of universal healthcare coverage, which remains at the heart of most European healthcare systems. However, the data are heterogeneous as differences in healthcare systems, national guidelines, and clinical practice have driven different content; a recent analysis revealed that the number of European databases that meet minimum regulatory requirements across a broad range of regulatory use cases and which are readily accessible is disappointingly low and geographically skewed to Western and Northern Europe.8 This poses problems when results from multiple datasets must be pooled in order to deliver evidence representative of the wider European population or when larger numbers are needed to explore rare diseases, events, or outcomes. Resolving differences across data sources requires agreement on common sets of data elements, data formats and terminologies, or mapping of these components to an international system. Obvious advantages of common data quality systems and common data analytics are to facilitate data exchange, data analysis, and interpretation of results arising from multiple datasets. New approaches to harmonization that involve a priori transformation of the data into a common data model (i.e., same data structure, format, and terminology) independent of any particular study have become possible in recent years due to improvements in the computational capacity to store, extract, and analyze large datasets. By enabling the use of common standardized analytics, this facilitates a consistency of approach and minimizes the need for decision making at the level of individual data sources. Within Europe, such approaches have the potential to significantly accelerate studies, but a careful characterization is required to determine whether there is loss of information or validity when EU data are transformed into a common data model and to assess any impact on downstream outputs. Methodological challenges arise from the fundamental fact that observational data are not collected with research as their principle purpose, may be derived from different care settings, and therefore suffer from variable amounts of missing data and from multiple different biases and confounders.7 However, in all these scenarios, a significant barrier to acceptability remains concerns around the reliability and validity of the evidence generated through RWD, especially when conducted across multiple countries and databases across Europe. Even when the protocol is standardized, significant variability may remain, increasing the heterogeneity of the results.9 Such issues have long been recognized, but compliance with the best methodological standards, a detailed description of study design and data collected, and full transparency on the protocol and study report (with registration in a publicly available database) would do much to build confidence in results and avoid the confusion created by disparate results. The European Network for Centres of Pharmacoepidemiology and Pharmacovigilance (ENCePP) has developed, and updates annually, standards for pharmacoepidemiology research, and there have been multiple publications recently proposing the establishment of reporting requirements.10 Ideally such reporting would be consistent with common parameters and terminology to enable comparability and be publicly available at a single source. All studies imposed by European regulators must be registered in the European Union electronic Registry of Post Authorisation Studies (EU PAS Register), and extending this requirement to all studies would seem one obvious route. The digitization of health care and, increasingly, lifestyle data bring new opportunities to complement and enhance the data traditionally utilized in regulatory decision making. The hope is that this will improve the timeliness, accuracy, and relevance of decisions across the product life cycle. Defining the exact evidentiary standards of such RWE a priori is challenging as necessary standards will vary depending on the context within which the question is asked. Given the broad range of regulatory use cases, it seems clear that a one-size-fits-all approach will not be sufficient; a hybrid approach to evidence generation will be required, depending on the question being asked and the context in which the derived evidence will be used, and early planning of the strengths and limitations of the possible approaches is required. However, whatever the approach, there is a need to address operational, technical, and methodological challenges in both designing, running, and assessing a study to enhance the quality of evidence generated and the consistency of regulatory decision making. Moreover, as more data sources become available and infrastructures are developed to enable access, there is an urgent need to consider and plan for the data needs for the future. Standardizing and validating data retrospectively is expensive, time consuming, and potentially introduces errors and biases, and hence it is important to consider in advance the scope, depth, and quality of data that will be required to generate reliable evidence suitable for multiple regulatory use cases. This work requires effort from the multiple stakeholders who may potentially wish to utilize these data for decision making. With the combination of technological and scientific advances available today, there has never been a more opportune time to address this. No funding was received for this work. The authors declared no competing interests for this work. The views expressed in this article are the personal views of the authors and may not be understood or quoted as being made on behalf of or reflecting the position of the European Medicines Agency or one of its committees or working parties. Please note: The publisher is not responsible for the content or functionality of any supporting information supplied by the authors. Any queries (other than missing content) should be directed to the corresponding author for the article.

OBJECTIVE: To examine the safety of pertussis vaccination in pregnancy. DESIGN: Observational cohort study. SETTING: The UK Clinical Practice Research Datalink. PARTICIPANTS: 20,074 pregnant women with a median age of 30 who received the pertussis vaccine and a matched historical unvaccinated control group. MAIN OUTCOME MEASURE: Adverse events identified from clinical diagnoses during pregnancy, with additional data from the matched child record identified through mother-child linkage. The primary event of interest was stillbirth (intrauterine death after 24 weeks' gestation). RESULTS: There was no evidence of an increased risk of stillbirth in the 14 days immediately after vaccination (incidence rate ratio 0.69, 95% confidence interval 0.23 to 1.62) or later in pregnancy (0.85, 0.44 to 1.61) compared with historical national rates. Compared with a matched historical cohort of unvaccinated pregnant women, there was no evidence that vaccination accelerated the time to delivery (hazard ratio 1.00, 0.97 to 1.02). Furthermore, there was no evidence of an increased risk of stillbirth, maternal or neonatal death, pre-eclampsia or eclampsia, haemorrhage, fetal distress, uterine rupture, placenta or vasa praevia, caesarean delivery, low birth weight, or neonatal renal failure, all serious events that can occur naturally in pregnancy. CONCLUSION: In women given pertussis vaccination in the third trimester, there is no evidence of an increased risk of any of an extensive predefined list of adverse events related to pregnancy. In particular, there was no evidence of an increased risk of stillbirth. Given the recent increases in the rate of pertussis infection and morbidity and mortality in neonates, these early data provide initial evidence for evaluating the safety of the vaccine in pregnancy for health professionals and the public and can help to inform vaccination policy making.

OBJECTIVE: To provide a comprehensive meta-analysis on the efficacy of psychological and medical treatments for binge-eating disorder (BED), including those targeting weight loss. METHOD: Through a systematic search before March 2018, 81 published and unpublished randomized-controlled trials (RCTs), totaling 7,515 individuals with BED (Diagnostic and Statistical Manual of Mental Disorders-Fourth Edition [DSM-IV] and Fifth Edition [DSM-5]), were retrieved and analyzed using random-effect modeling. RESULTS: In RCTs with inactive control groups, psychotherapy, mostly consisting of cognitive-behavioral therapy, showed large-size effects for the reduction of binge-eating episodes and abstinence from binge eating, followed by structured self-help treatment with medium-to-large effects when compared with wait-list. Pharmacotherapy and pharmacological weight loss treatment mostly outperformed pill placebo conditions with small effects on binge-eating outcome. These results were confirmed for the most common treatments of cognitive-behavioral therapy, self-help treatment based on cognitive-behavioral therapy, and lisdexamfetamine. In RCTs with active control groups, there was limited evidence for the superiority of one treatment category or treatment. In a few studies, psychotherapy outperformed behavioral weight loss treatment in short- and long-term binge-eating outcome and led to lower longer-term abstinence than self-help treatment, while combined treatment revealed no additive effect on binge-eating outcome over time. Overall study quality was heterogeneous and the quality of evidence for binge-eating outcome was generally very low. CONCLUSIONS: This comprehensive meta-analysis demonstrated the efficacy of psychotherapy, structured self-help treatment, and pharmacotherapy for patients with BED. More high quality research on treatments for BED is warranted, with a focus on long-term maintenance of therapeutic gains, comparative efficacy, mechanisms through which treatments work, and complex models of care. (PsycINFO Database Record (c) 2018 APA, all rights reserved).

The constitutive androstane receptor (CAR) and pregnane X receptor (PXR) are important nuclear receptors involved in the regulation of cellular responses from exposure to many xenobiotics and various physiological processes. Phenobarbital (PB) is a non-genotoxic indirect CAR activator, which induces cytochrome P450 (CYP) and other xenobiotic metabolizing enzymes and is known to produce liver foci/tumors in mice and rats. From literature data, a mode of action (MOA) for PB-induced rodent liver tumor formation was developed. A MOA for PXR activators was not established owing to a lack of suitable data. The key events in the PB-induced liver tumor MOA comprise activation of CAR followed by altered gene expression specific to CAR activation, increased cell proliferation, formation of altered hepatic foci and ultimately the development of liver tumors. Associative events in the MOA include altered epigenetic changes, induction of hepatic CYP2B enzymes, liver hypertrophy and decreased apoptosis; with inhibition of gap junctional intercellular communication being an associative event or modulating factor. The MOA was evaluated using the modified Bradford Hill criteria for causality and other possible MOAs were excluded. While PB produces liver tumors in rodents, important species differences were identified including a lack of cell proliferation in cultured human hepatocytes. The MOA for PB-induced rodent liver tumor formation was considered to be qualitatively not plausible for humans. This conclusion is supported by data from a number of epidemiological studies conducted in human populations chronically exposed to PB in which there is no clear evidence for increased liver tumor risk.

OBJECTIVE: To compare the risk of non-fatal self harm and suicide in patients taking selective serotonin reuptake inhibitors (SSRIs) with that of patients taking tricyclic antidepressants, as well as between different SSRIs and different tricyclic antidepressants. DESIGN: Nested case-control study. SETTING: Primary care in the United Kingdom. PARTICIPANTS: 146,095 individuals with a first prescription of an antidepressant for depression. MAIN OUTCOME MEASURES: Suicide and non-fatal self harm. RESULTS: 1968 cases of non-fatal self harm and 69 suicides occurred. The overall adjusted odds ratio of non-fatal self harm was 0.99 (95% confidence interval 0.86 to 1.14) and that of suicide 0.57 (0.26 to 1.25) in people prescribed SSRIs compared with those prescribed tricyclic antidepressants. We found little evidence that associations differed over time since starting or stopping treatment. We found some evidence that risks of non-fatal self harm in people prescribed SSRIs compared with those prescribed tricyclic antidepressants differed by age group (interaction P = 0.02). The adjusted odds ratio of non-fatal self harm for people prescribed SSRIs compared with users of tricylic antidepressants for those aged 18 or younger was 1.59 (1.01 to 2.50), but no association was apparent in other age groups. No suicides occurred in those aged 18 or younger currently or recently prescribed tricyclic antidepressants or SSRIs. CONCLUSION: We found no evidence that the risk of suicide or non-fatal self harm in adults prescribed SSRIs was greater than in those prescribed tricyclic antidepressants. We found some weak evidence of an increased risk of non-fatal self harm for current SSRI use among those aged 18 or younger. However, preferential prescribing of SSRIs to patients at higher risk of suicidal behaviour cannot be ruled out.

A number of recent applications have led to CPMP discussions concerning the interpretation of superiority, noninferiority and equivalence trials. These issues are covered in ICH E9 (Statistical Principles for Clinical Trials). There is further relevant material in the Step 2 draft of ICH E10 (Choice of Control Group) and in the CPMP Note for Guidance on the Investigation of Bioavailability and Bioequivalence. However, the guidelines do not address some specific difficulties that have arisen in practice. In broad terms, these difficulties relate to switching from one design objective to another at the time of analysis. The types of trials in question are those designed to compare a new product with an active comparator. The objective may be to demonstrate: the superiority of the new product the noninferiority of the new product or the equivalence of the two products. When the results of the trial become available, they may suggest an alternative interpretation. Thus the results of a superiority trial may only appear to be sufficient to support noninferiority, while the results of a noninferiority trial may appear to support superiority. Alternatively, the results of an equivalence trial may appear to support a tighter range of equivalence. A satisfactory approach to this subject requires an understanding of confidence intervals and the manner in which they capture the results of the trial and indicate the conclusions that can be drawn from them. Such an understanding also leads to an appreciation of why power calculations are of relatively little interest when a trial is complete. For simplicity, this paper addresses the issues of superiority, noninferiority and equivalence from the perspective of an efficacy trial with a single primary variable. Some comments on other situations are made in Section VI. It is assumed throughout this document that switching the objective of a trial does not lead to any change in the selection or definition of the primary variable. A superiority trial is designed to detect a difference between treatments. The first step of the analysis is usually a test of statistical significance to evaluate whether the results of the trial are consistent with the assumption of there being no difference in the clinical effect of the two treatments. In a trial of good quality, the degree of statistical significance (P value) indicates the probability that the observed difference, or a larger one, could have arisen by chance assuming that no difference really existed. The smaller this probability is, the more implausible is the assumption that there really is no difference between the treatments. Once it is accepted that the assumption of ‘no difference’ is untenable, it then becomes important to estimate the size of the difference in order to assess whether the effect is clinically relevant. This has two aspects. First there is the best estimate of the size of the difference between treatments (point estimate). For normally distributed data this is usually taken as the observed difference between the mean values on each. Next, there is the range of values of the true difference that are plausible in the light of the results of the trial (confidence interval). It is clear that this range should not include zero since the possibility of a zero difference has already been rejected as unreasonable. The method of constructing confidence intervals generally ensures that this is so, provided it corresponds to the choice of significance test. Thus the following two statements are usually equivalent: The two-sided 95% confidence interval for the difference between the means excludes zero. The two means are statistically significantly different at the 5% level (P < 0.05) two-sided. The above text addresses the situation where the difference between two mean values is the statistic of interest and a zero difference represents no effect. In practice a number of other summary statistics are used for the evaluation of differences between treatments, for example the odds ratio for proportions or the ratio of geometric means in bio-equivalence studies. (The latter arises from the logarithmic transformation used for bioavailability data.) In such cases the same principles apply but ‘no difference’ may be represented by a value other than zero – a value of 1 in both the examples quoted here. In these cases it is the position of the confidence interval for the test statistic relative to this ‘no difference’ value that is of interest. When significance tests are carried out in practice, precise numerical values of probabilities are usually quoted, for example P = 0.032, because this is more informative than P < 0.05. This allows judgement to be based more precisely on the extent of the disagreement between the null hypothesis and the observed data rather than on the approximations implied by using cut-off points of 0.05, 0.01 and 0.001. However, confidence intervals have to be associated with a specific probability value (coverage probability) and this is nearly always taken as 95% (0.95). When a difference is statistically significant at a more extreme level, e.g. P = 0.002, the two-sided 95% confidence interval will exclude zero by a wider margin. Figure 1 illustrates these points. Relationship between significance tests and confidence intervals. Whether the observed difference is indeed clinically relevant is a matter of judgement. In contrast to an equivalence or noninferiority trial where clinical relevance is addressed through the prestudy choice of Δ (see II.2 and II.3), in a superiority trial clinical relevance requires separate consideration: a statistically significant difference may not be clinically relevant. The difference taken as the basis of the power calculation in a superiority trial cannot be assumed to provide a suitable value. Note that in Figure 1, and throughout the rest of the document, it is assumed that values to the right of zero correspond to a better response on the new treatment so that values to the left are worse, i.e. better on the control treatment. An equivalence trial is designed to confirm the absence of a meaningful difference between treatments. In this case it is more informative to conduct the analysis by means of the calculation and examination of the confidence interval although there are closely related methods using significance test procedures. (See also II.3.) A margin of clinical equivalence (Δ) is chosen by defining the largest difference that is clinically acceptable, so that a difference bigger than this would matter in practice. There are well-recognized difficulties associated with this task which will not be discussed in any detail here. If the two treatments are to be declared equivalent, then the two-sided 95% confidence interval – which defines the range of plausible differences between the two treatments – should lie entirely within the interval −Δ to + Δ, see Figure 2. There are situations in which the equivalence margins may be chosen asymmetrically with respect to zero. Confidence interval approach to analysis of equivalence trial. In the case of bioequivalence studies a coverage probability of 90% for the confidence interval has become the accepted standard when evaluating whether the average values of the pharmacokinetic parameters of two formulations are sufficiently close. Clinical equivalence trials, with two-sided 95% confidence intervals, may be carried out when conventional bio-equivalence trials are impossible, for example in the case of a generic inhaled or topically applied product. In Phase III drug development, noninferiority trials are more common than equivalence trials. In these we wish to show that a new treatment is no less effective than an existing treatment – it may be more effective or it may have a similar effect. Again a confidence interval approach is the most straightforward way of performing the analysis but now we are only interested in a possible difference in one direction. Hence the two-sided 95% confidence interval should lie entirely to the right of the value −Δ, see Figure 3. Non-inferiority trials are sometimes mistakenly referred to, and designed as, equivalence trials. This distinction is important and can be a source of confusion. Confidence interval approach to analysis of non-inferiority trial. Note also that by using the closely related significance testing procedures referred to in II.2, it is possible to calculate a P value associated with the null hypothesis of inferiority. This is a valuable further aid to assessing the strength of the evidence in favour of noninferiority. It will be assumed throughout this document that two-sided 95% confidence intervals are to be used for all clinical trials whatever their objective. Among other benefits, this preserves consistency between significance testing and subsequent estimation. It is also consistent with the guidance provided in the ICH E9 Note for Guidance. If one-sided intervals are used, then they should be used with a coverage probability of 97.5%. In the special case of bioequivalence studies, two-sided 90% confidence intervals have been established as the norm as recommended, for example, in the CPMP Note for Guidance on the Investigation of Bioavailability and Bioequivalence. A conclusion of equivalence or noninferiority clearly depends upon the value of Δ chosen as the maximum acceptable difference. It is always possible to choose a value of Δ which leads to a conclusion of equivalence or noninferiority if it is chosen after the data have been inspected. Since the choice of Δ is generally a difficult one, there is ample room for bias here, however, well intentioned the researcher may be. Plausible arguments may often be advanced for a retrospective choice. In the design of equivalence and noninferiority trials, this reason (amongst others) makes it necessary for the choice of Δ, and the reasoning behind the choice, to be set down in advance by the researcher in the study protocol. The corresponding coverage probability for the confidence interval (usually 95%) should also be chosen at this time. (See Section IV.2 for how these requirements apply when objectives are changed.) The question of how to choose an appropriate Δ will be addressed in a subsequent CPMP Points to Consider. Pre-definition of a trial as a superiority trial, an equivalence trial or a noninferiority trial is necessary for numerous reasons including the following: to ensure that comparator treatments, doses, patient populations and endpoints are appropriate (see ICH E10) to allow sample size estimates to be based on the correct power calculations to ensure that equivalence and noninferiority criteria are predefined to permit appropriate analysis plans to be described in the protocol to ensure that the trial has sufficient sensitivity to achieve its objectives (see ICH E10) If the objective of a trial is switched from superiority to noninferiority, or vice versa, these aspects may lead to greater difficulty than the interpretation of significance tests and confidence intervals. The only switching which is likely to have any practical relevance is switching between superiority and noninferiority. The place of equivalence trials is so specific that they stand alone. If the 95% confidence interval for the treatment effect not only lies entirely above −Δ but also above zero then there is evidence of superiority in terms of statistical significance at the 5% level (P < 0.05). See Figure 4. In this case it is acceptable to calculate the P value associated with a test of superiority and to evaluate whether this is sufficiently small to reject convincingly the hypothesis of no difference. There is no multiplicity argument that affects this interpretation because, in statistical terms, it corresponds to a simple closed test procedure. Usually this demonstration of a benefit is sufficient on its own, provided the safety profiles of the new agent and the comparator are similar. When there is an increase in adverse events, however, it is important to estimate the size of the effect to evaluate whether it is sufficient in clinical terms to outweigh the adverse effects. Non-inferiority to superiority. There are a number of other factors that might be affected by this changed objective. If the comparator was suitable for a demonstration of noninferiority, then there should be well-controlled data to show that it is an effective treatment. Hence, for proof of efficacy, a clear demonstration of superiority to the comparator in terms of statistical significance should be acceptable. Non-inferiority trials are generally large because of their need to exclude the possibility of a small degree of inferiority of a new agent relative to an active control. However if the new agent is actually superior to control by a small amount, then the power to show its noninferiority is increased. Demonstrating the small amount of superiority to control might in principle require the planning of an even larger trial. When the trial is completed, however, the results provided by the confidence interval supply a concrete assessment of the precision actually achieved, superseding any calculations of power carried out before the trial was undertaken. Since the comparator in a noninferiority trial must be an effective agent, any superiority to that agent should carry the implication of acceptable superiority to no treatment (placebo). For this reason the size of the additional clinical benefit demonstrated is not likely to be relevant to a claim of efficacy except in relation to any increase in adverse effects and hence relative risk/benefit. However, when the proposed licence includes a claim of superiority to the comparator, the size of the additional benefit should be discussed in clinical terms. In a superiority trial the full analysis set, based on the ITT (intention-to-treat) principle, is the analysis set of choice, with appropriate support provided by the PP (per protocol) analysis set. In a noninferiority trial, the full analysis set and the PP analysis set have equal importance and their use should lead to similar conclusions for a robust interpretation. A switch of objective would require this difference of emphasis to be recognized. More details of the relative importance of these two analysis sets in superiority and noninferiority trials can be found in the ICH E9 Note for guidance. A trial to show equivalence or noninferiority must show a high degree of consistency with protocolled plans if it is to be reliable. Deviations from the inclusion criteria, from the intended treatment regimen, from the schedule, manner and precision of taking measurements, and so on, all tend to reduce the sensitivity of a trial and to make a conclusion of ‘no difference’ more likely, even when the deviations are of an unsystematic or random nature. The size of the bias associated with these and other departures from the protocol is generally unknown and may render such a trial uninterpretable. Failure to show a difference between two treatments can also arise when both treatments are inefficacious, perhaps as a result of being inappropriately administered. This problem does not affect superiority trials to the same extent because the demonstration of a difference is itself validation of the sensitivity of the trial. The estimate of the size of the effect may however, be similarly affected. For these reasons, switching from noninferiority to superiority is likely to carry with it a greater degree of confidence in the conclusion. Switching the objective of a trial from noninferiority to superiority is feasible provided: The trial has been properly designed and carried out in accordance with the strict requirements of a noninferiority trial. Actual P values for superiority are presented to allow independent assessment of the strength of the evidence. Analysis according to the intention-to-treat principle is given greatest emphasis. If a superiority trial fails to detect a significant difference between treatments, there may be interest in the lesser objective of establishing noninferiority. If the results of the superiority trial are summarized by means of a 95% confidence interval for the treatment difference, the lower end of that confidence interval provides a quantitative estimate of the minimum estimated effect of the new treatment relative to the comparator. When the study protocol an acceptable, margin −Δ for noninferiority, the objective less a noninferiority margin would appear only to make in trials with noninferiority as an However, in any superiority trial where noninferiority may be an acceptable for it is to a noninferiority margin in the protocol in order to the difficulties that can arise from such it is also to design to the possible need to that the study sufficient sensitivity to detect the drug effects of interest (see It is important to that there are of where noninferiority to an active control is to be acceptable as the or evidence of efficacy, and trials are to In trials where there is no noninferiority such a has to be after the and in situations this will not be It is likely that the will have to be after the results have been and there may be little basis for an objective choice of margin. there does not appear to be a statistical multiplicity related to this switch of that does not the difficulties associated with the definition of A number of other issues require A comparator chosen for a demonstration of superiority may not be acceptable for a conclusion of noninferiority. In order for it to be acceptable, it will be necessary to that there are data from good superiority trials consistent evidence that the comparator is an effective treatment with and establishing the size of its effect relative to no treatment. There should also be a basis for that the same degree of efficacy would be in the trial. For example, the patient and the endpoints should be similar. These issues are covered in ICH in the results provided by the confidence interval supply a concrete assessment of the precision actually by a clinical trial, superseding any calculations of power carried out before the trial was undertaken. The position of the lower end of the confidence interval relative to the of noninferiority provides the for noninferiority. In a superiority trial the full analysis set, based on the ITT (intention-to-treat) principle, is the analysis set of choice, with appropriate support provided by the PP (per protocol) analysis set. In a noninferiority trial the full analysis set and the PP analysis set have equal importance and their use should lead to similar conclusions for a robust interpretation. A switch of objective would require this difference of emphasis to be recognized. More details of the relative importance of these two analysis sets in superiority and noninferiority trials can be found in the ICH E9 Note for Guidance. A trial to show equivalence or noninferiority must show a high degree of consistency with protocolled plans if it is to be reliable. Deviations from the inclusion criteria, from the intended treatment regimen, from the schedule, manner and precision of taking measurements, and so on, all tend to reduce the sensitivity of a trial and to make a conclusion of ‘no difference’ more likely, even when the deviations are of an unsystematic or random nature. The size of the bias associated with these and other departures from the protocol is generally unknown and may render such a trial uninterpretable. Failure to show a difference between two treatments can also arise when both treatments are inefficacious, perhaps as a result of being inappropriately administered. This problem does not affect superiority trials to the same extent because the demonstration of a difference is itself validation of the sensitivity of the trial. For these reasons, switching from superiority to noninferiority is likely to carry with it a lesser degree of confidence in the It will be necessary to to the sensitivity of the trial by or that the control treatment is its efficacy the trial with trials which demonstrated the efficacy of the control agent in of and of of and data that are at to those in the trials similar results from the full analysis set and PP analysis set. Switching the objective of a trial from superiority to noninferiority may be feasible provided: The noninferiority margin with respect to the control treatment was predefined or can be (The latter is likely to difficult and to be to cases where there is a accepted value for Analysis according to the intention-to-treat principle and PP confidence intervals and P values for the null hypothesis of similar The trial was properly designed and carried out in accordance with the strict requirements of a noninferiority trial (see ICH E9 and The sensitivity of the trial is high to ensure that it is of relevant differences if they There is or evidence that the control treatment is its level of A further related that has arisen in with equivalence and noninferiority trials to the equivalence margins when the trial is complete. that a bioequivalence trial a 90% confidence interval for the relative bioavailability of a new that from to we only that the relative bioavailability lies between the conventional of and because these the predefined equivalence can we that it lies between and The interval based on the data is the appropriate one to Hence, if the changed to this study would have satisfactory There is no question of a selection However, if the trial in a confidence interval from to then a change of equivalence margins to would not be acceptable because of the conclusion that the equivalence margin was chosen to the These apply to the 95% confidence intervals used for clinical equivalence and for noninferiority. The confidence interval based on the results of the trial is always the best summary of the It is the choice of equivalence margin that is subject to This should be chosen on the basis of and not chosen to the This Points to has been from the perspective of an efficacy trial active with a single primary variable. In practice some studies have more than one primary and most studies have respect to switching of these requires separate in the of the specific drug development, separate conclusions superiority or noninferiority for in judgement whether the trial as a has established the superiority or noninferiority of the new treatment will upon the requirements for that clinical and the of results all relevant The covered in these Points to can also be applied to specific safety when these have been as endpoints of a trial to compare active In practice the of switching objectives is not relevant to trials, even where noninferiority to is a valuable i.e. for safety The problem of switching objectives can be by a trial in the that both noninferiority and superiority are of value. In this case all the issues in this document should be addressed In the statistical analysis should be using an appropriate from noninferiority to superiority. The interpretation of superiority trials as noninferiority trials and vice is best by the results as a confidence interval for the difference between the test treatment and control. There is no problem associated with the use of this confidence interval as a basis for of interpretation. For a and trial, there are difficulties with the change from noninferiority to superiority that cannot be addressed by appropriate analysis. However, there are more difficulties associated with the switch from superiority to noninferiority because of the possible need to a basis and on, a margin of equivalence after the and because of the difficulties of noninferiority trials. There are for the design of a superiority trial in which noninferiority might be an acceptable When the results with respect to alternative of the equivalence margins the problem from to switch to wider acceptable that equivalence margins may be in this

Worldwide, the hepatitis B virus (HBV) and the hepatitis C virus (HCV) cause, respectively, 600,000 and 350,000 deaths each year. Viral hepatitis is the leading cause of cirrhosis and liver cancer, which in turn ranks as the third cause of cancer death worldwide. Within the WHO European region, approximately 14 million people are chronically infected with HBV, and nine million people are chronically infected with HCV. Lack of reliable epidemiological data on HBV and HCV is one of the biggest hurdles to advancing policy. Risk groups such as migrants and injecting drug users (IDU) tend to be under-represented in existing prevalence studies; thus, targeted surveillance is urgently needed to correctly estimate the burden of HBV and HCV. The most effective means of prevention against HBV is vaccination, and most European Union (EU) countries have universal vaccination programmes. For both HBV and HCV, screening of individuals who present a high risk of contracting the virus is critical given the asymptomatic, and thereby silent, nature of disease. Screening of migrants and IDUs has been shown to be effective and potentially cost-effective. There have been significant advances in the treatment of HCV and HBV in recent years, but health care professionals remain poorly aware of treatment options. Greater professional training is needed on the management of hepatitis including the treatment of liver cancer to encourage adherence to guidelines and offer patients the best possible outcomes. Viral hepatitis knows no borders. EU Member States, guided by the EU, need to work in a concerted manner to implement lasting, effective policies and programmes and make tackling viral hepatitis a public health priority.

Endpoint selection is a critically important step in clinical trial design. It poses major challenges for investigators, regulators, and study sponsors, and it also has important clinical and practical implications for physicians and patients. Clinical outcomes of interest in heart failure trials include all-cause mortality, cause-specific mortality, relevant non-fatal morbidity (e.g., all-cause and cause-specific hospitalization), composites capturing both morbidity and mortality, safety, symptoms, functional capacity, and patient-reported outcomes. Each of these endpoints has strengths and weaknesses that create controversies regarding which is most appropriate in terms of clinical importance, sensitivity, reliability, and consistency. Not surprisingly, a lack of consensus exists within the scientific community regarding the optimal endpoint(s) for both acute and chronic heart failure trials. In an effort to address these issues, the Heart Failure Association of the European Society of Cardiology (HFA-ESC) convened a group of expert heart failure clinical investigators, biostatisticians, regulators, and pharmaceutical industry scientists (Nice, France, 12-13 February 2012) to evaluate the challenges of defining heart failure endpoints in clinical trials and to develop a consensus framework. This report summarizes the group's recommendations for achieving common views on heart failure endpoints in clinical trials.

OBJECTIVES: We examined major issues associated with sharing of individual clinical trial data and developed a consensus document on providing access to individual participant data from clinical trials, using a broad interdisciplinary approach. DESIGN AND METHODS: This was a consensus-building process among the members of a multistakeholder task force, involving a wide range of experts (researchers, patient representatives, methodologists, information technology experts, and representatives from funders, infrastructures and standards development organisations). An independent facilitator supported the process using the nominal group technique. The consensus was reached in a series of three workshops held over 1 year, supported by exchange of documents and teleconferences within focused subgroups when needed. This work was set within the Horizon 2020-funded project CORBEL (Coordinated Research Infrastructures Building Enduring Life-science Services) and coordinated by the European Clinical Research Infrastructure Network. Thus, the focus was on non-commercial trials and the perspective mainly European. OUTCOME: We developed principles and practical recommendations on how to share data from clinical trials. RESULTS: The task force reached consensus on 10 principles and 50 recommendations, representing the fundamental requirements of any framework used for the sharing of clinical trials data. The document covers the following main areas: making data sharing a reality (eg, cultural change, academic incentives, funding), consent for data sharing, protection of trial participants (eg, de-identification), data standards, rights, types and management of access (eg, data request and access models), data management and repositories, discoverability, and metadata. CONCLUSIONS: The adoption of the recommendations in this document would help to promote and support data sharing and reuse among researchers, adequately inform trial participants and protect their rights, and provide effective and efficient systems for preparing, storing and accessing data. The recommendations now need to be implemented and tested in practice. Further work needs to be done to integrate these proposals with those from other geographical areas and other academic domains.

Criteria for treatment-resistant depression (TRD) and partially responsive depression (PRD) as subtypes of major depressive disorder (MDD) are not unequivocally defined. In the present document we used a Delphi-method-based consensus approach to define TRD and PRD and to serve as operational criteria for future clinical studies, especially if conducted for regulatory purposes. We reviewed the literature and brought together a group of international experts (including clinicians, academics, researchers, employees of pharmaceutical companies, regulatory bodies representatives, and one person with lived experience) to evaluate the state-of-the-art and main controversies regarding the current classification. We then provided recommendations on how to design clinical trials, and on how to guide research in unmet needs and knowledge gaps. This report will feed into one of the main objectives of the EUropean Patient-cEntric clinicAl tRial pLatforms, Innovative Medicines Initiative (EU-PEARL, IMI) MDD project, to design a protocol for platform trials of new medications for TRD/PRD.

Compared with drugs from the blockbuster era, recently authorized drugs and those expected in the future present a heterogenous mix of chemicals, biologicals, and cell and gene therapies, a sizable fraction being for rare diseases, and even individualized treatments or individualized combinations. The shift in the nature of products entails secular trends for the definitions of "drugs" and "target population" and for clinical use and evidence generation. We discuss that the lessons learned from evidence generation for 20th century medicines may have limited relevance for 21st century medicines. We explain why the future is not about randomized controlled trials (RCTs) vs. real-world evidence (RWE) but RCTs and RWE-not just for the assessment of safety but also of effectiveness. Finally, we highlight that, in the era of precision medicine, we may not be able to reliably describe some small treatment effects-either by way of RCTs or RWE.