Peer Review in a General Medical Research Journal Before and During the COVID-19 Pandemic.

Importance: Although peer review is an important component of publication for new research, the viability of this process has been questioned, particularly with the added stressors of the COVID-19 pandemic.

Objective: To characterize rates of peer reviewer acceptance of invitations to review manuscripts, reviewer turnaround times, and editor-assessed quality of reviews before and after the start of the COVID-19 pandemic at a large, open-access general medical journal.

Design, Setting, And Participants: This retrospective, pre-post cohort study examined all research manuscripts submitted to JAMA Network Open between January 1, 2019, and June 29, 2021, either directly or via transfer from other JAMA Network journals, for which at least 1 peer review of manuscript content was solicited.

Advancing the Real-World Evidence for Medical Devices through Coordinated Registry Networks.

Objectives: Generating and using real-world evidence (RWE) is a pragmatic solution for evaluating health technologies. RWE is recognized by regulators, health technology assessors, clinicians, and manufacturers as a valid source of information to support their decision-making. Well-designed registries can provide RWE and become more powerful when linked with electronic health records and administrative databases in coordinated registry networks (CRNs).

Real-World Trends in the Evaluation of Medical Products.

There is a compelling need to evaluate the real-world health effects of medical products outside of tightly controlled preapproval clinical trials. This is done through pharmacoepidemiology, which is the study of the health effects of medical products (including drugs, biologicals, and medical devices and diagnostics) in populations, often using nonrandomized designs. Recent developments in pharmacoepidemiology span changes in the focus of research questions, research designs, data used, and statistical analysis methods.

Multi-institutional distributed data networks for real-world evidence about medical devices: building unique device identifiers into longitudinal data (BUILD).

Objectives: To support development of a robust postmarket device evaluation system using real-world data (RWD) from electronic health records (EHRs) and other sources, employing unique device identifiers (UDIs) to link to device information.

Methods: To create consistent device-related EHR RWD across 3 institutions, we established a distributed data network and created UDI-enriched research databases (UDIRs) employing a common data model comprised of 24 tables and 472 fields. To test the system, patients receiving coronary stents between 2010 and 2019 were loaded into each institution's UDIR to support distributed queries without sharing identifiable patient information.

Challenges of evaluating and modelling vaccination in emerging infectious diseases.

Outbreaks of emerging pathogens pose unique methodological and practical challenges for the design, implementation, and evaluation of vaccine efficacy trials. Lessons learned from COVID-19 highlight the need for innovative and flexible study design and application to quickly identify promising candidate vaccines. Trial design strategies should be tailored to the dynamics of the specific pathogen, location of the outbreak, and vaccine prototypes, within the regional socioeconomic constraints.

A systematic assessment of the epidemiologic literature regarding an association between acetaminophen exposure and cancer.

Introduced in the 1950s, acetaminophen is one of the most widely used antipyretics and analgesics worldwide. In 1999, the International Agency for Research on Cancer (IARC) reviewed the epidemiologic studies of acetaminophen and the data were judged to be "inadequate" to conclude that it is carcinogenic. In 2019 the California Office of Environmental Health Hazard Assessment initiated a review process on the carcinogenic hazard potential of acetaminophen.

Quantifying bias in epidemiologic studies evaluating the association between acetaminophen use and cancer.

Many observational studies explore the association between acetaminophen and cancer, but known limitations such as vulnerability to channeling, protopathic bias, and uncontrolled confounding hamper the interpretability of results. To help understand the potential magnitude of bias, we identify key design choices in these observational studies and specify 10 study design variants that represent different combinations of these design choices. We evaluate these variants by applying them to 37 negative controls - outcome presumed not to be caused by acetaminophen - as well as 4 cancer outcomes in the Clinical Practice Research Datalink (CPRD) database.

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

In alignment with the International Council of Harmonization’s strategic goals, a public-private consortium has developed a structured template for planning and reporting on the implementation of real world evidence (RWE) studies of the safety and effectiveness of treatments. The template serves as a guiding tool for designing and conducting reproducible RWE studies; set clear expectations for transparent communication of RWE methods; reduce misinterpretation of prose that lacks specificity; allow reviewers to quickly orient and find key information; and facilitate reproducibility, validity assessment, and evidence synthesis. The template is intended for use with studies of the effectiveness and safety of medical products and is compatible with multiple study designs, data sources, reporting guidelines, checklists, and bias assessment tools.

Methods for external control groups for single arm trials or long-term uncontrolled extensions to randomized clinical trials.

Purpose: Clinical trials compare outcomes among patients receiving study treatment with comparators drawn from the same source. These internal controls are missing in single arm trials and from long-term extensions (LTE) of trials including only the treatment arm. An external control group derived from a different setting is then required to assess safety or effectiveness.

Learning from local to global: An efficient distributed algorithm for modeling time-to-event data.

Objective: We developed and evaluated a privacy-preserving One-shot Distributed Algorithm to fit a multicenter Cox proportional hazards model (ODAC) without sharing patient-level information across sites.

Materials And Methods: Using patient-level data from a single site combined with only aggregated information from other sites, we constructed a surrogate likelihood function, approximating the Cox partial likelihood function obtained using patient-level data from all sites. By maximizing the surrogate likelihood function, each site obtained a local estimate of the model parameter, and the ODAC estimator was constructed as a weighted average of all the local estimates.

Designing, Conducting, Monitoring, and Analyzing Data from Pragmatic Randomized Clinical Trials: Proceedings from a Multi-stakeholder Think Tank Meeting.

In late 2018, the Food and Drug Administration (FDA) outlined a framework for evaluating the possible use of real-world evidence (RWE) to support regulatory decision-making. This framework was created to facilitate studies that would generate high-quality RWE, including pragmatic clinical trials (PCTs), which are randomized trials designed to inform clinical or policy decisions by assessing the real-world effectiveness of an intervention. There is general agreement among experts that the use of existing healthcare and patient-generated data holds promise for making randomized trials more efficient, less costly, and more generalizable.

Channeling Bias in the Analysis of Risk of Myocardial Infarction, Stroke, Gastrointestinal Bleeding, and Acute Renal Failure with the Use of Paracetamol Compared with Ibuprofen.

Introduction: Observational studies estimating severe outcomes for paracetamol versus ibuprofen use have acknowledged the specific challenge of channeling bias. A previous study relying on negative controls suggested that using large-scale propensity score (LSPS) matching may mitigate bias better than models using limited lists of covariates.

Objective: The aim was to assess whether using LSPS matching would enable the evaluation of paracetamol, compared to ibuprofen, and increased risk of myocardial infarction, stroke, gastrointestinal (GI) bleeding, or acute renal failure.

Acute pancreatitis risk in type 2 diabetes patients treated with canagliflozin versus other antihyperglycemic agents: an observational claims database study.

Observational evidence suggests that patients with type 2 diabetes mellitus (T2DM) are at increased risk for acute pancreatitis (AP) versus those without T2DM. A small number of AP events were reported in clinical trials of the sodium glucose co-transporter 2 inhibitor canagliflozin, though no imbalances were observed between treatment groups. This observational study evaluated risk of AP among new users of canagliflozin compared with new users of six classes of other antihyperglycemic agents (AHAs).

Learning from electronic health records across multiple sites: A communication-efficient and privacy-preserving distributed algorithm.

Objectives: We propose a one-shot, privacy-preserving distributed algorithm to perform logistic regression (ODAL) across multiple clinical sites.

Materials And Methods: ODAL effectively utilizes the information from the local site (where the patient-level data are accessible) and incorporates the first-order (ODAL1) and second-order (ODAL2) gradients of the likelihood function from other sites to construct an estimator without requiring iterative communication across sites or transferring patient-level data. We evaluated ODAL via extensive simulation studies and an application to a dataset from the University of Pennsylvania Health System.

Practical help for specifying the target difference in sample size calculations for RCTs: the DELTA five-stage study, including a workshop.

Background: The randomised controlled trial is widely considered to be the gold standard study for comparing the effectiveness of health interventions. Central to its design is a calculation of the number of participants needed (the sample size) for the trial. The sample size is typically calculated by specifying the magnitude of the difference in the primary outcome between the intervention effects for the population of interest.

Correction to: Choosing the target difference and undertaking and reporting the sample size calculation for a randomised controlled trial - the development of the DELTA2 guidance.

Following publication of the original article [1], we have been notified of a few mistakes.