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

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

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

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

The Aetion Coalition to Advance Real-World Evidence through Randomized Controlled Trial Emulation Initiative: Oncology.

Legislative and technological advancements over the past decade have given rise to the proliferation of healthcare data generated from routine clinical practice, often referred to as real-world data (RWD). These data have piqued the interest of healthcare stakeholders due to their potential utility in generating evidence to support clinical and regulatory decision making. In the oncology setting, studies leveraging RWD offer distinct advantages that are complementary to randomized controlled trials (RCTs). They also permit the conduct of investigations that may not be possible through prospective designs due to ethics or feasibility. Despite its promise, the use of RWD for the generation of clinical evidence remains controversial due to concerns of unmeasured confounding and other sources of bias that must be carefully addressed in the study design and analysis. To facilitate a better understanding of when RWD can provide reliable conclusions on drug effectiveness, we seek to conduct 10 RWD-based studies that emulate RCTs in oncology using a systematic, protocol-driven approach described herein. Results of this investigation will help inform clinical, scientific, and regulatory stakeholders on the applications of RWD in the context of product labeling expansion, drug safety, and comparative effectiveness in oncology.

Transferability of real-world data across borders for regulatory and health technology assessment decision-making.

Recently, there has been increased consideration of real-world data (RWD) and real-world evidence (RWE) in regulatory and health technology assessment (HTA) decision-making. Due to challenges in identifying high-quality and relevant RWD sources, researchers and regulatory/HTA bodies may turn to RWD generated in locales outside of the locale of interest (referred to as "transferring RWD"). We therefore performed a review of stakeholder guidance as well as selected case studies to identify themes for researchers to consider when transferring RWD from one jurisdiction to another. Our review highlighted that there is limited consensus on defining decision-grade, transferred RWD; certain stakeholders have issued relevant guidance, but the recommendations are high-level and additional effort is needed to generate comprehensive guidance. Additionally, the case studies revealed that RWD transferability has not been a consistent concern for regulatory/HTA bodies and that more focus has been put on the evaluation of internal validity. To help develop transferability best practices (alongside internal validity best practices), we suggest that researchers address the following considerations in their justification for transferring RWD: treatment pathways, nature of the healthcare system, incidence/prevalence of indication, and patient demographics. We also recommend that RWD transferability should garner more attention as the use of imported RWD could open doors to high-quality data sources and potentially reduce methodological issues that often arise in the use of local RWD; we thus hope this review provides a foundation for further dialogue around the suitability and utility of transferred RWD in the regulatory/HTA decision-making space.

The Structured Process to Identify Fit-For-Purpose Data: A Data Feasibility Assessment Framework.

To complement real-world evidence (RWE) guidelines, the 2019 Structured Preapproval and Postapproval Comparative study design framework to generate valid and transparent real-world Evidence (SPACE) framework elucidated a process for designing valid and transparent real-world studies. As an extension to SPACE, here, we provide a structured framework for conducting feasibility assessments-a step-by-step guide to identify decision grade, fit-for-purpose data, which complements the United States Food and Drug Administration (FDA)'s framework for a RWE program. The process was informed by our collective experience conducting systematic feasibility assessments of existing data sources for pharmacoepidemiology studies to support regulatory decisions. Used with the SPACE framework, the Structured Process to Identify Fit-For-Purpose Data (SPIFD) provides a systematic process for conducting feasibility assessments to determine if a data source is fit for decision making, helping ensure justification and transparency throughout study development, from articulation of a specific and meaningful research question to identification of fit-for-purpose data and study design.

A Structured Preapproval and Postapproval Comparative Study Design Framework to Generate Valid and Transparent Real-World Evidence for Regulatory Decisions.

Real-world evidence provides important information about the effects of medicines in routine clinical practice. To engender trust that evidence generated for regulatory purposes is sufficiently valid, transparency in the reasoning that underlies study design decisions is critical. Building on existing guidance and frameworks, we developed the Structured Preapproval and Postapproval Comparative study design framework to generate valid and transparent real-world Evidence (SPACE) as a process for identifying design elements and minimal criteria for feasibility and validity concerns, and for documenting decisions. Starting with an articulated research question, we identify key components of the randomized controlled trial needed to maximize validity, and pragmatic choices are considered when required. A causal diagram is used to justify the variables identified for confounding control, and key decisions, assumptions, and evidence are captured in a structured way. In this way, SPACE may improve dialogue and build trust among healthcare providers, patients, regulators, and researchers.

Causal identification: a charge of epidemiology in danger of marginalization.

The requirement for framing all causal questions as well-defined interventions is being promoted in the causal inference literature within epidemiology. One can consider this perspective as an intervention on the field which requires a refocusing of epidemiologic questions and retooling of epidemiologic methods. Although this intervention has produced many positive results, we think that its underlying assumptions and the possibilities of unintended consequences warrant examination. In so doing, we argue that this approach can lead to the neglect of causal identification as a useful link between associations and the estimation of intervention effects.

Acute nonarteritic anterior ischemic optic neuropathy and exposure to phosphodiesterase type 5 inhibitors.

INTRODUCTION: Nonarteritic anterior ischemic optic neuropathy (NAION), a rare visual disorder, has been reported in men using phosphodiesterase type 5 inhibitors (PDE5i) for erectile dysfunction. AIM: We examined whether intermittent use of PDE5i is associated with acute NAION onset within approximately five half-lives following drug ingestion. METHODS: One hundred two ophthalmology centers in the United States and Europe identified potential cases of NAION. An expert adjudication committee conducted a blind review of the records of those with recent PDE5i use to classify cases as Definite, Possible, or not NAION. Subjects provided information on PDEi use via telephone interview. Each NAION case's PDE5i exposure immediately prior to onset was compared against his recent patterns of use in an observational case-crossover design. A sample size of 40 cases with intermittent PDE5i exposure in the 30 days prior to NAION onset was needed to detect an odds ratio (OR) of 3.0 with 80% power. MAIN OUTCOME MEASURES: The daily relative risk for acute NAION on days within five half-lives of PDE5i use vs. other days was estimated via an OR obtained from conditional logistic regression. RESULTS: Among 43 Definite NAION cases with PDE5i exposure in the prior 30 days, the OR was 2.15 (95% confidence interval [CI]: 1.06, 4.34). When 21 Possible NAION cases were included (n = 64), the OR was 2.36 (95% CI: 1.33, 4.19). CONCLUSIONS: We found an approximately twofold increased risk of acute NAION within five half-lives of PDE5i use compared with use in a more prior time period. Bias from inaccurate recall of exposure was unlikely to have substantially affected the results. Based on our results, we estimate that weekly use of PDE5i adds three NAION cases per 100,000 men 50 years and older annually.

Toward a clarification of the taxonomy of "bias" in epidemiology textbooks.

Epidemiology textbooks typically divide biases into 3 general categories-confounding, selection bias, and information bias. Despite the ubiquity of this categorization, authors often use these terms to mean different things. This hinders communication among epidemiologists and confuses students who are just learning about the field. To understand the sources of this problem, we reviewed current general epidemiology textbooks to examine how the authors defined and categorized biases. We found that much of the confusion arises from different definitions of "validity" and from a mixing of 3 overlapping organizational features in defining and differentiating among confounding, selection bias, and information bias: consequence, the result of the problem; cause, the processes that give rise to the problem; and cure, how these biases can be addressed once they occur. By contrast, a consistent taxonomy would provide (1) a clear and consistent definition of what unites confounding, selection bias, and information bias and (2) a clear articulation and consistent application of the feature that distinguishes these categories. Based on a distillation of these textbook discussions, we provide an example of a taxonomy that we think meets these criteria.

An organizational schema for epidemiologic causal effects.

Epidemiologic textbooks and methodological papers define multiple causal effects. These causal effects can differ substantially; yet, the causal effect of interest is rarely specified in published epidemiologic studies perhaps because their distinctions are underappreciated. Here, we provide an organizational schema that distinguishes causal effects based on six characteristics. We use simple numeric examples to demonstrate the variability across effects and show why specifying the causal effect is necessary for an accurate intervention interpretation even under the simplest scenarios. The objective of our schema was to illuminate the distinguishing characteristics of various causal effects and clarify their interpretation, thus guiding epidemiologists in choosing an appropriate causal effect to estimate.

Extending the sufficient component cause model to describe the Stable Unit Treatment Value Assumption (SUTVA).

Causal inference requires an understanding of the conditions under which association equals causation. The exchangeability or no confounding assumption is well known and well understood as central to this task. More recently the epidemiologic literature has described additional assumptions related to the stability of causal effects. In this paper we extend the Sufficient Component Cause Model to represent one expression of this stability assumption--the Stable Unit Treatment Value Assumption. Approaching SUTVA from an SCC model helps clarify what SUTVA is and reinforces the connections between interaction and SUTVA.

Redundant causation from a sufficient cause perspective.

Sufficient causes of disease are redundant when an individual acquires the components of two or more sufficient causes. In this circumstance, the individual still would have become diseased even if one of the sufficient causes had not been acquired. In the context of a study, when any individuals acquire components of more than one sufficient cause over the observation period, the etiologic effect of the exposure (defined as the absolute or relative difference between the proportion of the exposed who develop the disease by the end of the study period and the proportion of those individuals who would have developed the disease at the moment they did even in the absence of the exposure) may be underestimated. Even in the absence of confounding and bias, the observed effect estimate represents only a subset of the etiologic effect. This underestimation occurs regardless of the measure of effect used.To some extent, redundancy of sufficient causes is always present, and under some circumstances, it may make a true cause of disease appear to be not causal. This problem is particularly relevant when the researcher's goal is to characterize the universe of sufficient causes of the disease, identify risk factors for targeted interventions, or construct causal diagrams. In this paper, we use the sufficient component cause model and the disease response type framework to show how redundant causation arises and the factors that determine the extent of its impact on epidemiologic effect measures.

Sleep, quality of life, and productivity impact of nasal symptoms in the United States: findings from the Burden of Rhinitis in America survey.

Rhinitis is a common chronic condition that has been shown in observational and interventional studies to have a substantial impact on the sufferer. This study was performed to describe the impact of symptoms of allergic rhinitis (AR) on sleep, quality of life, and productivity in a U.S. population. A cohort of AR sufferers and non-AR sufferers was assembled by screening a representative sample of 15,000 households with a self-administered questionnaire in January 2004. A subsample of respondents received a detailed follow-up questionnaire in the May/June pollen season. Of the 7024 individuals with complete data, 3831 met the case definition of AR sufferer; 3193 were non-AR sufferers. Overall, AR sufferers had consistently poorer average scores on the sleep, quality of life, cognition, and productivity scales compared with non-AR sufferers. Subjects with AR symptoms had more sleep impairment (51.2) compared with subjects with non-AR symptoms and those with no symptoms (59.8 and 63.3, respectively). Only 3.6% of subjects with AR symptoms experienced 100% sleep adequacy compared with 11.7% of subjects with non-AR symptoms and 19.2% of subjects with no symptoms. Quality of life and cognition scores were worse in subjects with AR symptoms compared with subjects with non-AR or no symptoms. Work and school productivity was significantly reduced in subjects with AR symptoms in the past 4 weeks compared with subjects with no symptoms (p < 0.05). Individuals who suffer from AR symptoms experience a substantial burden on their ability to sleep, quality of life, cognitive function, and school/workplace productivity.

Rhinitis symptoms and comorbidities in the United States: burden of rhinitis in America survey.

OBJECTIVE: To assess the burden of nasal symptoms in the United States (U.S.) and the comorbid conditions associated with nasal symptoms. SUBJECTS AND METHODS: A self-administered screening questionnaire and follow-up survey was sent to targeted households from a representative sample of 15,000 households in the U.S. Subjects with comorbid asthma completed the Asthma Control Test (ACT). RESULTS: Out of 7024 evaluable subjects who responded, 3831 subjects were classified as rhinitis "sufferers." Individuals with active rhinitis symptoms were 1.5 to 4.5 times more likely to suffer from comorbid conditions including asthma, conjunctivitis, otitis media, sinusitis, eczema, food and insect bite allergies, migraine, and depression. Almost half of all respondents with moderate or severe rhinitis symptoms and comorbid asthma had poorly controlled asthma as defined by an ACT score of < or =19. CONCLUSIONS: A strong relationship exists between rhinitis symptoms and various comorbidities, including asthma, in the U.S. population. Poorly controlled rhinitis contributes to the public health burden of rhinitis and asthma.