Reframing the perception of outliers and negative data in translational research.
Ontology highlight
ABSTRACT:
SUBMITTER: Emborg ME
PROVIDER: S-EPMC9891652 | biostudies-literature | 2023 Jan
REPOSITORIES: biostudies-literature
Publications
Reframing the perception of outliers and negative data in translational research.
Brain research bulletin 20221201
Negative results can be a source of disappointment for scientists, yet their publication is needed for scientific progress, in particular for cutting-edge translational research of novel therapeutics. This manuscript is directed to scientists, junior and senior, that produce and review data for publication. It discusses the difference between 'negative' or 'unexpected' data and 'useless' data, re-evaluates the importance of the experimental design to generate valuable data and proposes strategie ...[more]
Similar Datasets
Project description:BackgroundThe changing clinical research recruitment landscape involves practical challenges but introduces opportunities. Researchers can now identify large numbers of eligible patients through electronic health record review and can directly contact those who have authorized contact. Applying behavioral science-driven strategies to design and frame communication could affect patients' willingness to authorize contact and their understanding of these programs. The ethical and practical implications of various strategies warrant empirical evaluation.MethodsWe conducted an online survey (n = 1070) using a nationally-representative sample. Participants were asked to imagine being asked for authorization for research contact in clinic. They were randomly assigned to view one of three flyers: #1-neutral text flyer; #2-a positive text flyer; or #3-positive graphics-based flyer. Primary outcomes included likelihood of enrollment and comprehension of the program. Chi-Square tests and regression analyses were used to examine whether those who saw the positive flyers were more likely to enroll and had increased comprehension.ResultsCompared to the neutral flyer, individuals who received the positive text flyer were numerically more likely to enroll, but this was not statistically significant (24.2% v. 19.0%, p = 0.11). Individuals who received the positive graphics flyer were more likely to enroll (28.7% v. 19.0%, p = 0.002). After adjustment, individuals assigned to both novel flyers had increased odds of being likely to enroll (OR = 1.55 95%CI [1.04, 2.31] and OR = 1.95 95%CI [1.31, 2.91]). Flyer type did not affect overall comprehension (p = 0.21), and greater likelihood of enrollment was observed only in individuals with better comprehension.ConclusionsThis study demonstrated that employing behavioral science-driven communication strategies for authorization for research contact had an effect on likelihood of hypothetical enrollment but did not significantly affect comprehension. Strategies using simple, positive language and visual tools may be effective and ethically appropriate. Further studies should explore how these and other approaches can help to optimize research recruitment.
| S-EPMC10788686 | biostudies-literature
Project description:The unprecedented advances in technology and scientific research over the past few years have provided the scientific community with new and more complex forms of data. Large data sets collected from single groups or cross-institution consortiums containing hundreds of omic and clinical variables corresponding to thousands of patients are becoming increasingly commonplace in the research setting. Before any core analyses are performed, visualization often plays a key role in the initial phases of research, especially for projects where no initial hypotheses are dominant. Proper visualization of data at a high level facilitates researcher's abilities to find trends, identify outliers and perform quality checks. In addition, research has uncovered the important role of visualization in data analysis and its implied benefits facilitating our understanding of disease and ultimately improving patient care. In this work, we present a review of the current landscape of existing tools designed to facilitate the visualization of multidimensional data in translational research platforms. Specifically, we reviewed the biomedical literature for translational platforms allowing the visualization and exploration of clinical and omics data, and identified 11 platforms: cBioPortal, interactive genomics patient stratification explorer, Igloo-Plot, The Georgetown Database of Cancer Plus, tranSMART, an unnamed data-cube-based model supporting heterogeneous data, Papilio, Caleydo Domino, Qlucore Omics, Oracle Health Sciences Translational Research Center and OmicsOffice® powered by TIBCO Spotfire. In a health sector continuously witnessing an increase in data from multifarious sources, visualization tools used to better grasp these data will grow in their importance, and we believe our work will be useful in guiding investigators in similar situations.
| S-EPMC5862238 | biostudies-literature
Project description:This second paper in a 3-part series on antiracism in pain research across the translational spectrum focuses on study design factors. Although objectivity is a cornerstone value of science, subjectivity is embedded in every step of the research process as investigators make choices about who they collaborate with, which research questions they ask, how they recruit participants, which research tools they use, and how they analyze and interpret data. We present theory and evidence from disciplines such as sociology, medical anthropology, statistics, and public health to discuss 4 common study design factors, including 1) the dominant biomedical narrative of pain that restricts funding and exploration of social indicators of pain, 2) low diversity and inclusion in pain research enrollment that restricts generalizability to racialized groups, 3) the use of "race" or "ethnicity" as a statistical variable and proxy for lived experiences (eg, racism, resilience), and 4) limited modeling in preclinical research for the impact of social factors on pain physiology. The information presented in this article is intended to start conversations across stakeholders in the pain field to explore how we can come together to adopt antiracism practices in our work at large to achieve equity for racialized groups. PERSPECTIVE: This is the second paper in a 3-part series on antiracism in pain research. This part identifies common study design factors that risk hindering progress toward pain care equity. We suggest reframes using an antiracism framework for these factors to encourage all pain investigators to collectively make strides toward equity.
| S-EPMC9472383 | biostudies-literature
Project description:ObjectiveAmong National Institutes of Health Clinical and Translational Science Award (CTSA) hubs, adoption of electronic data warehouses for research (EDW4R) containing data from electronic health record systems is nearly ubiquitous. Although benefits of EDW4R include more effective, efficient support of scientists, little is known about how CTSA hubs have implemented EDW4R services. The goal of this qualitative study was to understand the ways in which CTSA hubs have operationalized EDW4R to support clinical and translational researchers.Materials and methodsAfter conducting semistructured interviews with informatics leaders from 20 CTSA hubs, we performed a directed content analysis of interview notes informed by naturalistic inquiry.ResultsWe identified 12 themes: organization and data; oversight and governance; data access request process; data access modalities; data access for users with different skill sets; engagement, communication, and literacy; service management coordinated with enterprise information technology; service management coordinated within a CTSA hub; service management coordinated between informatics and biostatistics; funding approaches; performance metrics; and future trends and current technology challenges.DiscussionThis study is a step in developing an improved understanding and creating a common vocabulary about EDW4R operations across institutions. Findings indicate an opportunity for establishing best practices for EDW4R operations in academic medicine. Such guidance could reduce the costs associated with developing an EDW4R by establishing a clear roadmap and maturity path for institutions to follow.ConclusionsCTSA hubs described varying approaches to EDW4R operations that may assist other institutions in better serving investigators with electronic patient data.
| S-EPMC7647350 | biostudies-literature
Project description:BackgroundTo foster responsible data sharing in health research, ethical governance complementary to the EU General Data Protection Regulation is necessary. A governance framework for Big Data-driven research platforms will at least need to consider the conditions as specified a priori for individual datasets. We aim to identify and analyze these conditions for the Innovative Medicines Initiative's (IMI) BigData@Heart platform.MethodsWe performed a unique descriptive case study into the conditions for data sharing as specified for datasets participating in BigData@Heart. Principle investigators of 56 participating databases were contacted via e-mail with the request to send any kind of documentation that possibly specified the conditions for data sharing. Documents were qualitatively reviewed for conditions pertaining to data sharing and data access.ResultsQualitative content analysis of 55 relevant documents revealed overlap on the conditions: (1) only to share health data for scientific research, (2) in anonymized/coded form, (3) after approval from a designated review committee, and while (4) observing all appropriate measures for data security and in compliance with the applicable laws and regulations.ConclusionsDespite considerable overlap, prespecified conditions give rise to challenges for data sharing. At the same time, these challenges inform our thinking about the design of an ethical governance framework for data sharing platforms. We urge current data sharing initiatives to concentrate on: (1) the scope of the research questions that may be addressed, (2) how to deal with varying levels of de-identification, (3) determining when and how review committees should come into play, (4) align what policies and regulations mean by "data sharing" and (5) how to deal with datasets that have no system in place for data sharing.
| S-EPMC6936121 | biostudies-literature
Project description:The OneFlorida Data Trust is a centralized research patient data repository created and managed by the OneFlorida Clinical Research Consortium ("OneFlorida"). It comprises structured electronic health record (EHR), administrative claims, tumor registry, death, and other data on 17.2 million individuals who received healthcare in Florida between January 2012 and the present. Ten healthcare systems in Miami, Orlando, Tampa, Jacksonville, Tallahassee, Gainesville, and rural areas of Florida contribute EHR data, covering the major metropolitan regions in Florida. Deduplication of patients is accomplished via privacy-preserving entity resolution (precision 0.97-0.99, recall 0.75), thereby linking patients' EHR, claims, and death data. Another unique feature is the establishment of mother-baby relationships via Florida vital statistics data. Research usage has been significant, including major studies launched in the National Patient-Centered Clinical Research Network ("PCORnet"), where OneFlorida is 1 of 9 clinical research networks. The Data Trust's robust, centralized, statewide data are a valuable and relatively unique research resource.
| S-EPMC8922180 | biostudies-literature
Project description:Context: When working with health-related questionnaires, outlier detection is important. However, traditional methods of outlier detection (e.g., boxplots) can miss participants with "atypical" responses to the questions that otherwise have similar total (subscale) scores. In addition to detecting outliers, it can be of clinical importance to determine the reason for the outlier status or "atypical" response. Objective: The aim of the current study was to illustrate how to derive person fit statistics for outlier detection through a statistical method examining person fit with a health-based questionnaire. Design and Participants: Patients treated for Cushing's syndrome (n = 394) were recruited from the Cushing's Support and Research Foundation's (CSRF) listserv and Facebook page. Main Outcome Measure: Patients were directed to an online survey containing the CushingQoL (English version). A two-dimensional graded response model was estimated, and person fit statistics were generated using the Zh statistic. Results: Conventional outlier detections methods revealed no outliers reflecting extreme scores on the subscales of the CushingQoL. However, person fit statistics identified 18 patients with "atypical" response patterns, which would have been otherwise missed (Zh > |±2.00|). Conclusion: While the conventional methods of outlier detection indicated no outliers, person fit statistics identified several patients with "atypical" response patterns who otherwise appeared average. Person fit statistics allow researchers to delve further into the underlying problems experienced by these "atypical" patients treated for Cushing's syndrome. Annotated code is provided to aid other researchers in using this method.
| S-EPMC5445123 | biostudies-literature
Project description:ObjectiveThe goal of this study was to explore the impact of 5 decision rules for removing outliers from adolescent food frequency questionnaire (FFQ) data.DesignThis secondary analysis used baseline and 3-month data from a weight loss intervention clinical trial.ParticipantsAfrican American adolescents (n = 181) were recruited from outpatient clinics and community health fairs.Variables measuredData collected included self-reported FFQ and mediators of weight (food addiction, depressive symptoms, and relative reinforcing value of food), caregiver-reported executive functioning, and objectively measured weight status (percentage overweight).AnalysisDescriptive statistics examined patterns in study variables at baseline and follow-up. Correlational analyses explored the relationships between FFQ data and key study variables at baseline and follow-up.ResultsCompared with not removing outliers, using decision rules reduced the number of cases and restricted the range of data. The magnitude of baseline FFQ-mediator relationships was attenuated under all decision rules but varied (increasing, decreasing, and reversing direction) at follow-up. Decision rule use increased the magnitude of change in FFQ estimated energy intake and significantly strengthened its relationship with weight change under 2 fixed range decision rules.Conclusions and implicationsResults suggest careful evaluation of outliers and testing and reporting the effects of different outlier decision rules through sensitivity analyses.
| S-EPMC7855646 | biostudies-literature
Project description:BACKGROUND:Translational researchers need robust IT solutions to access a range of data types, varying from public data sets to pseudonymised patient information with restricted access, provided on a case by case basis. The reason for this complication is that managing access policies to sensitive human data must consider issues of data confidentiality, identifiability, extent of consent, and data usage agreements. All these ethical, social and legal aspects must be incorporated into a differential management of restricted access to sensitive data. METHODS:In this paper we present a pilot system that uses several common open source software components in a novel combination to coordinate access to heterogeneous biomedical data repositories containing open data (open access) as well as sensitive data (restricted access) in the domain of biobanking and biosample research. Our approach is based on a digital identity federation and software to manage resource access entitlements. RESULTS:Open source software components were assembled and configured in such a way that they allow for different ways of restricted access according to the protection needs of the data. We have tested the resulting pilot infrastructure and assessed its performance, feasibility and reproducibility. CONCLUSIONS:Common open source software components are sufficient to allow for the creation of a secure system for differential access to sensitive data. The implementation of this system is exemplary for researchers facing similar requirements for restricted access data. Here we report experience and lessons learnt of our pilot implementation, which may be useful for similar use cases. Furthermore, we discuss possible extensions for more complex scenarios.
| S-EPMC5363029 | biostudies-other
Project description:Biomedical informatics has traditionally adopted a linear view of the informatics process (collect, store and analyse) in translational medicine (TM) studies; focusing primarily on the challenges in data integration and analysis. However, a data management challenge presents itself with the new lifecycle view of data emphasized by the recent calls for data re-use, long term data preservation, and data sharing. There is currently a lack of dedicated infrastructure focused on the 'manageability' of the data lifecycle in TM research between data collection and analysis. Current community efforts towards establishing a culture for open science prompt the creation of a data custodianship environment for management of TM data assets to support data reuse and reproducibility of research results. Here we present the development of a lifecycle-based methodology to create a metadata management framework based on community driven standards for standardisation, consolidation and integration of TM research data. Based on this framework, we also present the development of a new platform (PlatformTM) focused on managing the lifecycle for translational research data assets.
| S-EPMC6692384 | biostudies-literature