Research Registry

Protecting privacy while adequately adjusting for a large number of covariates poses methodological challenges for distributed data networks that can enable large-scale epidemiologic studies. Using 2 empirical examples, Li et al. determined that when used in conjunction with confounder summary scores, several combinations of data-sharing approaches and confounding adjustment methods allow researchers to perform multivariable-adjusted analysis using only aggregate-level information from participating sites and produce results identical to or comparable to those from pooled individual-level data analysis which help to protect privacy.

The objective of this study was to develop a patient reported measure to capture the overall experience, including benefits and harms, of treatment using Rheumatoid Arthritis (RA) as an example. The authors utilized Trajectory Mapping to organize adverse events and improvements from treatments to create a hierarchy of patient experiences. The results of this study help to contextualize what types of adverse events patients are willing to tolerate for levels of improvement when trying treatments.

This chapter of “21st Century Patient Registries: Registries for Evaluating Patient Outcomes: A User’s Guide: 3rd Edition” by Daughtery et al. discusses the growing focus on patient-centeredness in clinical research, medical care, and regulatory sciences which can lead to the increase of availability and dissemination of evidence that can be used to inform health care decision-making. There are some challenges in the use of patient registries as highlighted by patients such as accessibility of participation, translation of non-English materials, and privacy concerns that should be addressed. This chapter provides an overview of important topics relating to patient-centeredness in registries, engaging patients as stakeholders, the use of digital health technologies, and other patient-centric designs.

Coetzee et al. reports that nonprofit funders, including disease advocacy and patient-focused organizations, play an important role in the promotion and implementation of data sharing policies in clinical research trials which can help to drive policies and influence research culture. Eight goals are highlighted for nonprofit funders of clinical trials: encouraging the co-development of data sharing policies with patient and lay communities, incorporate data sharing concepts and policies as early as possible in clinical trials, use transparent and FAIR approval processes for data access, promote the development of a sustainable and feasible data sharing infrastructure, promote and support the adoption of standards, standard language, and common data elements, include incentives and enforce requirements in funding structures, provide funding for data sharing, and incorporate previous data sharing as a measure of impact. These goals promote a data sharing toolkit for nonprofit funders.