Power to the People: Why person-generated health data is important for pharmacoepidemiology.

Person-generated health data (PGHD) are valuable to study outcomes relevant to everyday living, to obtain information not otherwise available, for long-term follow-up and in situations where decisions cannot wait for traditional clinical research to be completed. While there is no dispute that these data are subject to bias, insights gained may be better than an information void, provided the biases are understood and acknowledged. People will share information known uniquely to them about exposures that may affect drug tolerance, safety and effectiveness, e.g., using non-prescription and complementary medications, alcohol, tobacco, illicit drugs, exercise, etc. Patients may be the best source of safety information when long-term follow-up is needed, e.g., the 5-15-year follow-up required for some gene therapies. Validation studies must be performed to evaluate what people can accurately report and when supplementary confirmation information is needed. But PGHD has already proven valuable in quantifying and contrasting COVID-19 vaccine benefits and risks, and for evaluating disease transmission and the accuracy of COVID-19 testing. Going forward, PGHD will be used for patient-measured and patient-relevant outcomes, including regulatory purposes, and will be linked to broader health data networks using tokenization, becoming a mainstay for signals about risks and benefits for diverse populations.

COVID-19 Vaccination Reactions and Risk of Breakthrough Infections Among People With Diabetes: Cohort Study Derived From Community Reporters.

BACKGROUND: This exploratory study compares self-reported COVID-19 vaccine side effects and breakthrough infections in people who described themselves as having diabetes with those who did not identify as having diabetes. OBJECTIVE: The study uses person-reported data to evaluate differences in the perception of COVID-19 vaccine side effects between adults with diabetes and those who did not report having diabetes. METHODS: This is a retrospective cohort study conducted using data provided online by adults aged 18 years and older residing in the United States. The participants who voluntarily self-enrolled between March 19, 2021, and July 16, 2022, in the IQVIA COVID-19 Active Research Experience project reported clinical and demographic information, COVID-19 vaccination, whether they had experienced any side effects, test-confirmed infections, and consented to linkage with prescription claims. No distinction was made for this study to differentiate prediabetes or type 1 and type 2 diabetes nor to verify reports of positive COVID-19 tests. Person-reported medication use was validated using pharmacy claims and a subset of the linked data was used for a sensitivity analysis of medication effects. Multivariate logistic regression was used to estimate the adjusted odds ratios of vaccine side effects or breakthrough infections by diabetic status, adjusting for age, gender, education, race, ethnicity (Hispanic or Latino), BMI, smoker, receipt of an influenza vaccine, vaccine manufacturer, and all medical conditions. Evaluations of diabetes medication-specific vaccine side effects are illustrated graphically to support the examination of the magnitude of side effect differences for various medications and combinations of medications used to manage diabetes. RESULTS: People with diabetes (n=724) reported experiencing fewer side effects within 2 weeks of vaccination for COVID-19 than those without diabetes (n=6417; mean 2.7, SD 2.0 vs mean 3.1, SD 2.0). The adjusted risk of having a specific side effect or any side effect was lower among those with diabetes, with significant reductions in fatigue and headache but no differences in breakthrough infections over participants' maximum follow-up time. Diabetes medication use did not consistently affect the risk of specific side effects, either using self-reported medication use or using only diabetes medications that were confirmed by pharmacy health insurance claims for people who also reported having diabetes. CONCLUSIONS: People with diabetes reported fewer vaccine side effects than participants not reporting having diabetes, with a similar risk of breakthrough infection. TRIAL REGISTRATION: ClinicalTrials.gov NCT04368065; https://clinicaltrials.gov/study/NCT04368065.

Tactical Considerations for Designing Real-World Studies: Fit-for-Purpose Designs That Bridge Research and Practice.

Real-world evidence (RWE) is being used to provide information on diverse groups of patients who may be highly impacted by disease but are not typically studied in traditional randomized clinical trials (RCT) and to obtain insights from everyday care settings and real-world adherence to inform clinical practice. RWE is derived from so-called real-world data (RWD), ie, information generated by clinicians in the course of everyday patient care, and is sometimes coupled with systematic input from patients in the form of patient-reported outcomes or from wearable biosensors. Studies using RWD are conducted to evaluate how well medical interventions, services, and diagnostics perform under conditions of real-world use, and may include long-term follow-up. Here, we describe the main types of studies used to generate RWE and offer pointers for clinicians interested in study design and execution. Our tactical guidance addresses (1) opportunistic study designs, (2) considerations about representativeness of study participants, (3) expectations for transparency about data provenance, handling and quality assessments, and (4) considerations for strengthening studies using record linkage and/or randomization in pragmatic clinical trials. We also discuss likely sources of bias and suggest mitigation strategies. We see a future where clinical records - patient-generated data and other RWD - are brought together and harnessed by robust study design with efficient data capture and strong data curation. Traditional RCT will remain the mainstay of drug development, but RWE will play a growing role in clinical, regulatory, and payer decision-making. The most meaningful RWE will come from collaboration with astute clinicians with deep practice experience and questioning minds working closely with patients and researchers experienced in the development of RWE.

Cognitive and other neuropsychiatric symptoms in COVID-19: analysis of person-generated longitudinal health data from a community-based registry.

OBJECTIVE: To describe cognitive symptoms in people not hospitalised at study enrolment for SARS-CoV-2 infection and associated demographics, medical history, other neuropsychiatric symptoms and SARS-CoV-2 vaccination. DESIGN: Longitudinal observational study. SETTING: Direct-to-participant registry with community-based recruitment via email and social media including Google, Facebook and Reddit, targeting adult US residents. Demographics, medical history, COVID-19-like symptoms, tests and vaccinations were collected through enrolment and follow-up surveys. PARTICIPANTS: Participants who reported positive COVID-19 test results between 15 December 2020 and 13 December 2021. Those with cognitive symptoms were compared with those not reporting such symptoms. MAIN OUTCOME MEASURE: Self-reported cognitive symptoms (defined as 'feeling disoriented or having trouble thinking' from listed options or related written-in symptoms) RESULTS: Of 3908 participants with a positive COVID-19 test result, 1014 (25.9%) reported cognitive symptoms at any time point during enrolment or follow-up, with approximately half reporting moderate/severe symptoms. Cognitive symptoms were associated with other neuropsychiatric symptoms, including dysgeusia, anosmia, trouble waking up, insomnia, headache, anxiety and depression. In multivariate analyses, female sex (OR, 95% CI): 1.7 (1.3 to 2.2), age (40-49 years (OR: 1.5 (1.2-1.9) compared with 18-29 years), history of autoimmune disease (OR: 1.5 (1.2-2.1)), lung disease (OR: 1.7 (1.3-2.2)) and depression (OR: 1.4 (1.1-1.7)) were associated with cognitive symptoms. Conversely, black race (OR: 0.6 (0.5-0.9)) and COVID-19 vaccination before infection (OR: 0.6 (0.4-0.7)) were associated with reduced occurrence of cognitive symptoms. CONCLUSIONS: In this study, cognitive symptoms among COVID-19-positive participants were associated with female gender, age, autoimmune disorders, lung disease and depression. Vaccination and black race were associated with lower occurrence of cognitive symptoms. A constellation of neuropsychiatric and psychological symptoms occurred with cognitive symptoms. Our findings suggest COVID-19's full health and economic burden may be underestimated. TRIAL REGISTRATION NUMBER: NCT04368065.

Evaluating real-world COVID-19 vaccine effectiveness using a test-negative case-control design.

Aim: It is important to assess if clinical trial efficacy translates into real-world effectiveness for COVID-19 vaccines. Materials & methods: We conducted a modified test-negative design (TND) to evaluate the real-world effectiveness of three COVID-19 vaccines. We defined cases in two ways: self-reported COVID-19-positive tests, and self-reported positive tests with ≥1 moderate/severe COVID-19 symptom. Results: Any vaccination was associated with a 95% reduction in subsequently reporting a positive COVID-19 test, and a 71% reduction in reporting a positive test and ≥1 moderate/severe symptom. Conclusion: We observed high effectiveness across all three marketed vaccines, both for self-reported positive COVID-19 tests and moderate/severe COVID-19 symptoms. This innovative TND approach can be implemented in future COVID-19 vaccine and treatment real-world effectiveness studies. Clinicaltrials.gov identifier: NCT04368065.

COVID-19 Vaccination Breakthrough Infections in a Real-World Setting: Using Community Reporters to Evaluate Vaccine Effectiveness.

Purpose: Coronavirus disease 2019 (COVID-19) has highlighted the need for new methods of pharmacovigilance. Here, we use adult community volunteers to obtain systematic information on vaccine effectiveness and the nature and severity of breakthrough infections. Methods: Between December 15, 2020 and September 16, 2021, 11,826 unpaid community-based volunteers reported the following information to an on-line registry: COVID-19 test results, vaccination (Pfizer, Moderna, or Johnson & Johnson) and COVID-19 symptoms. COVID-19 infections were described based on vaccination status at the time of infection: 1) fully vaccinated, 2) partially vaccinated (received first of two-dose vaccines or were <14 days post-final dose), or 3) unvaccinated. Results: Among 8554 participants who received any COVID-19 vaccine, COVID-19 infections were reported by 74 (1.0%) of those who were fully vaccinated and 198 (2.3%) of those who were partially vaccinated at the time of infection. Among the 74 participants who reported a breakthrough infection after full vaccination, the median time from vaccination to reported positive test result was 104.5 days (interquartile range: 77-135 days), with no difference among vaccine manufacturers. One quarter (25.7%) of breakthrough infections in the fully vaccinated cases were asymptomatic and most (>97%) fully vaccinated participants reported no symptoms or only mild symptoms compared to 89.3% of the unvaccinated cases. Only 1.4% of fully vaccinated participants reported experiencing at least 3 moderate-to-severe symptoms compared to 7.8% in the unvaccinated. Conclusion: Person-generated health data, also referred to as patient-reported outcomes, is a useful approach for quantifying breakthrough infections and their severity and for comparing vaccines. Trial Registration: Clinicaltrials.gov NCT04368065, EU PAS Register EUPAS36240.

Demonstrating Benefit-Risk Profiles of Novel Therapeutic Strategies in Kidney Transplantation: Opportunities and Challenges of Real-World Evidence.

While great progress has been made in transplantation medicine, long-term graft failure and serious side effects still pose a challenge in kidney transplantation. Effective and safe long-term treatments are needed. Therefore, evidence of the lasting benefit-risk of novel therapies is required. Demonstrating superiority of novel therapies is unlikely via conventional randomized controlled trials, as long-term follow-up in large sample sizes pose statistical and operational challenges. Furthermore, endpoints generally accepted in short-term clinical trials need to be translated to real-world (RW) care settings, enabling robust assessments of novel treatments. Hence, there is an evidence gap that calls for innovative clinical trial designs, with RW evidence (RWE) providing an opportunity to facilitate longitudinal transplant research with timely translation to clinical practice. Nonetheless, the current RWE landscape shows considerable heterogeneity, with few registries capturing detailed data to support the establishment of new endpoints. The main recommendations by leading scientists in the field are increased collaboration between registries for data harmonization and leveraging the development of technology innovations for data sharing under high privacy standards. This will aid the development of clinically meaningful endpoints and data models, enabling future long-term research and ultimately establish optimal long-term outcomes for transplant patients.

COVID-19 Vaccinations in Pregnancy: Comparative Evaluation of Acute Side Effects and Self-Reported Impact on Quality of Life between Pregnant and Nonpregnant Women in the United States.

OBJECTIVE: The objective of this study was to describe the acute side effects experienced by pregnant women who received a coronavirus disease 2019 (COVID-19) vaccine in the United States and to compare their experience to nonpregnant women of similar age. STUDY DESIGN: Adults who received a COVID-19 vaccine in the United States were invited via social media to enroll in an online, longitudinal, community-based registry ( www.helpstopCOVID19.com ). Participants self-reported pregnancy status, vaccination dates, manufacturer, acute side effects, impact on work and self-care, medical consultation, and hospitalization. This analysis was restricted to women aged 20 to 39 at the time of vaccination. Side effects reported by pregnant women were compared to those reported by nonpregnant women. RESULTS: This analysis included 946 pregnant women, with 572 (60%) receiving at least one dose of Pfizer, 321 (34%) Moderna, and 53 (6%) J&J, and 1,178 nonpregnant women. Demographic and medical history were similar across manufacturers for both cohorts.Overall, pregnant women reported similar side effects as nonpregnant women, with the most common being injection site reactions (83 vs. 87%), fatigue (72 vs.78%), and headache (45 vs. 59%). Pregnant women reported fewer side effects (median: 3 vs. 4, respectively). In both cohorts, very few reported seeking medical care (<5%) or being hospitalized (<0.3%) after vaccination. Fewer pregnant women reported working less after vaccination than nonpregnant women (32 vs. 40%) or trouble with self-care (32 vs. 46%), respectively. CONCLUSION: Pregnant women reported similar COVID-19 vaccine side effects as nonpregnant women, although fewer total side effects; pregnant women judged these side effects to have less impact on work and self-care. While these results do not address pregnancy outcomes or long-term effects, findings about acute side effects and impact offer reassurance for all three vaccines in terms of tolerability. KEY POINTS: · COVID vaccines were well tolerated by pregnant women.. · Pregnant women reported fewer total side effects.. · Pregnant women reported less impact on work and self-care..

Uncontrolled Extensions of Clinical Trials and the Use of External Controls-Scoping Opportunities and Methods.

Increased interest in real-world evidence (RWE) for clinical and regulatory decision making and the need to evaluate long-term benefits and risks of pharmaceutical products raise the importance of understanding the use of external controls (ECs) for uncontrolled extensions of randomized controlled trials (RCTs). We searched clinicaltrials.gov from 2009 to 2019 for uncontrolled extensions and assessed the use of ECs in the trial protocol registry and PubMed. We present characteristics of identified uncontrolled extensions, their adoption of ECs, and a qualitative appraisal of published uncontrolled extensions with ECs according to good pharmacoepidemiologic practice. The number of uncontrolled extensions increased slightly across the study period, resulting in a total of 1,115 studies. Most originated from phase III RCTs (62.2%) and specified safety outcomes (61.9% among those with specified outcomes). Most uncontrolled extensions incorporated no control group with only 7 out of 1,115 (0.6%) employing ECs. For those studies with ECs, all involved treatments for rare conditions and assessment of effectiveness. Attempts to balance comparison groups varied from none mentioned to propensity score matching. We noted consistent deficiencies in outcome ascertainment methods and approaches to address attrition bias. The contrast of the large and growing number of uncontrolled extensions with the small number of studies that utilized ECs showed clear opportunities for enhancement in design, measurement, and analysis of uncontrolled extensions to allow causal inferences on long-term treatment effects. As extensions continue to expand within RWE regulatory frameworks, development of guidelines for use of EC with uncontrolled extensions is needed.

When Can Nonrandomized Studies Support Valid Inference Regarding Effectiveness or Safety of New Medical Treatments?

The randomized controlled trial (RCT) is the gold standard for evaluating the causal effects of medications. Limitations of RCTs have led to increasing interest in using real-world evidence (RWE) to augment RCT evidence and inform decision making on medications. Although RWE can be either randomized or nonrandomized, nonrandomized RWE can capitalize on the recent proliferation of large healthcare databases and can often answer questions that cannot be answered in randomized studies due to resource constraints. However, the results of nonrandomized studies are much more likely to be impacted by confounding bias, and the existence of unmeasured confounders can never be completely ruled out. Furthermore, nonrandomized studies require more complex design considerations which can sometimes result in design-related biases. We discuss questions that can help investigators or evidence consumers evaluate the potential impact of confounding or other biases on their findings: Does the design emulate a hypothetical randomized trial design? Is the comparator or control condition appropriate? Does the primary analysis adjust for measured confounders? Do sensitivity analyses quantify the potential impact of residual confounding? Are methods open to inspection and (if possible) replication? Designing a high-quality nonrandomized study of medications remains challenging and requires broad expertise across a range of disciplines, including relevant clinical areas, epidemiology, and biostatistics. The questions posed in this paper provide a guiding framework for assessing the credibility of nonrandomized RWE and could be applied across many clinical questions.

When Can We Trust Real-World Data To Evaluate New Medical Treatments?

Concerns regarding both the limited generalizability and the slow pace of traditional randomized trials have led to calls for greater use of real-world evidence (RWE) in the evaluation of new treatments or products. RWE studies often rely on real-world data (RWD), including data extracted from healthcare records or data captured by mobile phones or other consumer devices. Global assessments of RWD sources are not helpful in assessing whether any specific RWD element is fit for any specific purpose. Instead, evidence generators and evidence consumers should clearly identify the specific health state or clinical phenomenon of interest and then consider each step between that clinical phenomenon and its representation in a research database. We propose specific questions regarding potential error or bias affecting each of those steps: Would a person experiencing this clinical phenomenon present for care in this setting or interact with this recording device? Would this clinical phenomenon be accurately recognized or assessed? How might the recording environment or tools affect accurate and consistent recording of this clinical phenomenon? Can data elements from different sources be harmonized, both technically (same format) and semantically (same meaning)? Can the original data elements be consistently reduced to a useful clinical phenotype? Addressing these questions requires a range of clinical, organizational, and technical expertise. Transparency regarding each step in the creation of RWD is essential if evidence consumers are to rely on RWE studies.

Real-world evidence to support regulatory decision making: New or expanded medical product indications.

There is increasing interest in utilizing real-world data (RWD) to produce real-world evidence (RWE) on the benefits and risks of medical products that could support regulatory approval decisions. The field of pharmacoepidemiology has a long history of focusing on data and evidence that would now be termed "real-world," including evidence from healthcare claims, registries, and electronic health records. However, several emerging trends over the past decade are converging to support the use of these and other RWD sources for approval decisions, and there are several recent examples and ongoing research that demonstrate how RWE may be used to support regulatory approval of new or expanded indications. The goal of this article is to review the current landscape and future directions of the use of RWE in this context. This manuscript is endorsed by the International Society for Pharmacoepidemiology (ISPE).

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.

The Certainty Framework for Assessing Real-World Data in Studies of Medical Product Safety and Effectiveness.

A fundamental question in using real-world data for clinical and regulatory decision making is: How certain must we be that the algorithm used to capture an exposure, outcome, cohort-defining characteristic, or confounder is what we intend it to be? We provide a practical framework to help researchers and regulators assess and classify the fit-for-purposefulness of real-world data by study variable for a range of data sources. The three levels of certainty (optimal, sufficient, and probable) must be considered in the context of each study variable, the specific question being studied, the study design, and the decision at hand.

Self-reported symptoms from exposure to Covid-19 provide support to clinical diagnosis, triage and prognosis: An exploratory analysis.

BACKGROUND: Symptomatic COVID-19 is prevalent in the community. We identify factors indicating COVID-19 positivity in non-hospitalized patients and prognosticators of moderate-to-severe disease. METHODS: Appeals conducted in April-June 2020 in social media, collaborating medical societies and patient advocacy groups recruited 20,476 participants ≥18 years who believed they had COVID-19 exposure. Volunteers consented on-line and reported height, weight, concomitant illnesses, medication and supplement use, residential, occupational or community COVID-19 exposure, symptoms and symptom severity on a 4-point scale. Of the 12,117 curated analytic population 2279 reported a COVID-19 viral test result: 865 positive (COVID+) and 1414 negative (COVID-). RESULTS: The triad of anosmia, ageusia and fever best distinguished COVID+ from COVID-participants (OR 6.07, 95% CI: 4.39 to 8.47). COVID + subjects with BMI≥30, concomitant respiratory disorders or an organ transplant had increased risk of moderate-to- severe dyspnoea. Race and anti-autoimmunity medication did not affect moderate-to-severe dyspnea risk. CONCLUSIONS: The triad of anosmia, ageusia and fever differentiates COVID-19. Elevated risks of severe symptoms outside the hospital were most evident among the obese and those with pulmonary comorbidity. Race and use of medication for autoimmune disease did not predict severe disease. These findings should facilitate rapid COVID-19 diagnosis and triage in settings without testing.

Replication of randomized clinical trial results using real-world data: paving the way for effectiveness decisions.

The FDA is preparing guidance about using real-world evidence (RWE) to support decisions about product effectiveness. Several ongoing efforts aim to replicate randomized clinical trial (RCT) results using RWE with the intent of identifying circumstances and methods that provide valid evidence of drug effects. Lack of agreement may not be due to faulty methods but rather to the challenges with emulating RCTs, differences in healthcare settings and patient populations, differences in effect measures and data analysis, bias, and/or the efficacy-effectiveness gap. In fact, for some decisions, RWE may lead to better understanding of how treatments work in usual care settings than a more constrained view from RCTs. Efforts to reconcile the role and opportunities for generating complementary evidence from RWE and RCTs will advance regulatory science.

Real-world evidence to support regulatory decision-making for medicines: Considerations for external control arms.

Randomized clinical trials (RCTs) are the gold standard in producing clinical evidence of efficacy and safety of medical interventions. More recently, a new paradigm is emerging-specifically within the context of preauthorization regulatory decision-making-for some novel uses of real-world evidence (RWE) from a variety of real-world data (RWD) sources to answer certain clinical questions. Traditionally reserved for rare diseases and other special circumstances, external controls (eg, historical controls) are recognized as a possible type of control arm for single-arm trials. However, creating and analyzing an external control arm using RWD can be challenging since design and analytics may not fully control for all systematic differences (biases). Nonetheless, certain biases can be attenuated using appropriate design and analytical approaches. The main objective of this paper is to improve the scientific rigor in the generation of external control arms using RWD. Here we (a) discuss the rationale and regulatory circumstances appropriate for external control arms, (b) define different types of external control arms, and (c) describe study design elements and approaches to mitigate certain biases in external control arms. This manuscript received endorsement from the International Society for Pharmacoepidemiology (ISPE).