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1. Correlates of Differences in Interactional Patterns among Black and White Respondents

   https://doi.org/10.1007/978-3-030-47256-6_12  

   Dykema, Jennifer; Garbarski, Dana; Schaeffer, Nora Cate; Anadon, Isabel; Edwards, Dorothy F. (October 2020, Understanding Survey Methodology. Frontiers in Sociology and Social Research)

   Brenner, P. S. (Ed.)

   Features of the survey measurement process may affect responses from respondents in various racial, ethnic, or cultural groups in different ways. When responses from multiethnic populations are combined, such variability in responding could increase variable error or bias results. The current study examines the survey response process among Black and White respondents answering questions about trust in medical researchers and participation in medical research. Using transcriptions from telephone interviews, we code a rich set of behaviors produced by respondents that past research has shown to be associated with measurement error, including long question-answer sequences, uncodable answers, requests for repetition or clarification, affective responses, and tokens. In analysis, we test for differences between Black and White respondents in the likelihood with which behaviors occur and examine whether the behaviors vary by specific categorizations of the questions, including whether the questions are racially focused. Overall, we find that White respondents produce more behaviors that indicate cognitive processing problems for racially focused questions, which may be interpreted as demonstrating a “cultural” difference in the display of cognitive processing and interaction. Data are provided by the 2013–2014 Voices Heard Survey, a computer-assisted telephone survey designed to measure respondents’ perceptions of barriers and facilitators to participating in medical research. 

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2. Public Concern About Monitoring Twitter Users and Their Conversations to Recruit for Clinical Trials: Survey Study

   https://doi.org/10.2196/15455  

   Reuter, Katja; Zhu, Yifan; Angyan, Praveen; Le, NamQuyen; Merchant, Akil A; Zimmer, Michael (January 2019, Journal of Medical Internet Research)

   Background Social networks such as Twitter offer the clinical research community a novel opportunity for engaging potential study participants based on user activity data. However, the availability of public social media data has led to new ethical challenges about respecting user privacy and the appropriateness of monitoring social media for clinical trial recruitment. Researchers have voiced the need for involving users’ perspectives in the development of ethical norms and regulations. Objective This study examined the attitudes and level of concern among Twitter users and nonusers about using Twitter for monitoring social media users and their conversations to recruit potential clinical trial participants. Methods We used two online methods for recruiting study participants: the open survey was (1) advertised on Twitter between May 23 and June 8, 2017, and (2) deployed on TurkPrime, a crowdsourcing data acquisition platform, between May 23 and June 8, 2017. Eligible participants were adults, 18 years of age or older, who lived in the United States. People with and without Twitter accounts were included in the study. Results While nearly half the respondents—on Twitter (94/603, 15.6%) and on TurkPrime (509/603, 84.4%)—indicated agreement that social media monitoring constitutes a form of eavesdropping that invades their privacy, over one-third disagreed and nearly 1 in 5 had no opinion. A chi-square test revealed a positive relationship between respondents’ general privacy concern and their average concern about Internet research (P<.005). We found associations between respondents’ Twitter literacy and their concerns about the ability for researchers to monitor their Twitter activity for clinical trial recruitment (P=.001) and whether they consider Twitter monitoring for clinical trial recruitment as eavesdropping (P<.001) and an invasion of privacy (P=.003). As Twitter literacy increased, so did people’s concerns about researchers monitoring Twitter activity. Our data support the previously suggested use of the nonexceptionalist methodology for assessing social media in research, insofar as social media-based recruitment does not need to be considered exceptional and, for most, it is considered preferable to traditional in-person interventions at physical clinics. The expressed attitudes were highly contextual, depending on factors such as the type of disease or health topic (eg, HIV/AIDS vs obesity vs smoking), the entity or person monitoring users on Twitter, and the monitored information. Conclusions The data and findings from this study contribute to the critical dialogue with the public about the use of social media in clinical research. The findings suggest that most users do not think that monitoring Twitter for clinical trial recruitment constitutes inappropriate surveillance or a violation of privacy. However, researchers should remain mindful that some participants might find social media monitoring problematic when connected with certain conditions or health topics. Further research should isolate factors that influence the level of concern among social media users across platforms and populations and inform the development of more clear and consistent guidelines. 

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3. Factors Associated with Interviewers’ Evaluations of Respondents’ Performance in Telephone Interviews: Behavior, Response Quality Indicators, and Characteristics of Respondents and Interviewers

   https://doi.org/10.1093/poq/nfad028  

   Garbarski, Dana; Dykema, Jennifer; Schaeffer, Nora_Cate; Jones, Cameron_P; Neman, Tiffany_S; Edwards, Dorothy_Farrar (July 2023, Public Opinion Quarterly)

   Abstract Interviewers’ postinterview evaluations of respondents’ performance (IEPs) are paradata, used to describe the quality of the data obtained from respondents. IEPs are driven by a combination of factors, including respondents’ and interviewers’ sociodemographic characteristics and what actually transpires during the interview. However, relatively few studies examine how IEPs are associated with features of the response process, including facets of the interviewer-respondent interaction and patterns of responding that index data quality. We examine whether features of the response process—various respondents’ behaviors and response quality indicators—are associated with IEPs in a survey with a diverse set of respondents focused on barriers and facilitators to participating in medical research. We also examine whether there are differences in IEPs across respondents’ and interviewers’ sociodemographic characteristics. Our results show that both respondents’ behaviors and response quality indicators predict IEPs, indicating that IEPs reflect what transpires in the interview. In addition, interviewers appear to approach the task of evaluating respondents with differing frameworks, as evidenced by the variation in IEPs attributable to interviewers and associations between IEPs and interviewers’ gender. Further, IEPs were associated with respondents’ education and ethnoracial identity, net of respondents’ behaviors, response quality indicators, and sociodemographic characteristics of respondents and interviewers. Future research should continue to build on studies that examine the correlates of IEPs to better inform whether, when, and how to use IEPs as paradata about the quality of the data obtained. 

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4. Network Analysis and Recommendation for Infectious Disease Clinical Trial Research

   Elkin, Magdalyn; Andrews, Whitney A.; Zhu, Xingquan (September 2019, Proc. of the 10th ACM Conference on Bioinformatics, Computational Biology, and Health Informatics (BCB-2019))

   Clinical trials are crucial for the advancement of treatment and knowledge within the medical community. Since 2007, US federal government took the initiative and requires organizations sponsoring clinical trials with at least one site in the United States to submit information on these clinical trials to the ClinicalTrials.gov database, resulting in a rich source of information for clinical trial research. Nevertheless, only a handful of analytic studies have been carried out to understand this valuable data source. In this study, we propose to use network analysis to understand infectious disease clinical trial research. Our goal is to answer two important questions: (1) what are the concentrations and characteristics of infectious disease clinical trail research? and (2) how to accurately predict what type of clinical trials a sponsor (or an investigator) is interested in? The answers to the first question provide effective ways to summarize clinical trial research related to particular disease(s), and the answers to the second question help match clinical trial sponsors and investigators for information recommendation. By using 4,228 clinical trails as the test bed, our study involves 4,864 sponsors and 1,879 research areas characterized by Medical Subject Heading (MeSH) keywords. We extract a set of network measures to show patterns of infectious disease clinical trials, and design a new community based link prediction approach to predict sponsors' interests, with significant improvement compared to baselines. This trans-formative study concludes that using network analysis can tremendously help the understanding of clinical trial research for effective summarization, characterization, and prediction. 

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5. Predictive modeling of clinical trial terminations using feature engineering and embedding learning

   https://doi.org/10.1038/s41598-021-82840-x  

   Elkin, Magdalyn E.; Zhu, Xingquan (December 2021, Scientific Reports)

   null (Ed.)

   Abstract In this study, we propose to use machine learning to understand terminated clinical trials. Our goal is to answer two fundamental questions: (1) what are common factors/markers associated to terminated clinical trials? and (2) how to accurately predict whether a clinical trial may be terminated or not? The answer to the first question provides effective ways to understand characteristics of terminated trials for stakeholders to better plan their trials; and the answer to the second question can direct estimate the chance of success of a clinical trial in order to minimize costs. By using 311,260 trials to build a testbed with 68,999 samples, we use feature engineering to create 640 features, reflecting clinical trial administration, eligibility, study information, criteria etc. Using feature ranking, a handful of features, such as trial eligibility, trial inclusion/exclusion criteria, sponsor types etc. , are found to be related to the clinical trial termination. By using sampling and ensemble learning, we achieve over 67% Balanced Accuracy and over 0.73 AUC (Area Under the Curve) scores to correctly predict clinical trial termination, indicating that machine learning can help achieve satisfactory prediction results for clinical trial study. 

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