Accuracy of a Cancer Registry Versus Clinical Care Team Chart Abstraction in Identifying Cancer Recurrence.

Objective: To evaluate the completeness and reliability of recurrence data from an institutional cancer registry for patients with head and neck cancer. Patients and Methods: Recurrence information was collected by radiation oncology and otolaryngology researchers. This was compared with the institutional cancer registry for continuous patients treated with radiation therapy for head and neck cancer at a tertiary cancer center. The sensitivity and specificity of institutional cancer registry data was calculated using manual review as the gold standard. False negative recurrences were compared to true positive recurrences to assess for differences in patient characteristics. Results: A total of 1338 patients who were treated from January 1, 2010, through December 31, 2017, were included in a cancer registry and underwent review. Of them, 375 (30%) had confirmed cancer recurrences, 45 (3%) had concern for recurrence without radiologic or pathologic confirmation, and 31 (2%) had persistent disease. Most confirmed recurrences were distant (37%) or distant plus locoregional (29%), whereas few were local (11%), regional (9%), or locoregional (14%) alone. The cancer registry accuracy was 89.4%, sensitivity 61%, and specificity 99%. Time to recurrence was associated with registry accuracy. True positives had recurrences at a median of 414 days vs 1007 days for false negatives. Conclusion: Currently, institutional cancer registry recurrence data lacks the required accuracy for implementation into studies without manual confirmation. Longer follow-up of cancer status will likely improve sensitivity. No identified differences in patients accounted for differences in sensitivity. New, ideally automated, data abstraction tools are needed to improve detection of cancer recurrences and minimize manual chart review.

How to apply variable selection machine learning algorithms with multiply imputed data: A missing discussion.

Psychological researchers often use standard linear regression to identify relevant predictors of an outcome of interest, but challenges emerge with incomplete data and growing numbers of candidate predictors. Regularization methods like the LASSO can reduce the risk of overfitting, increase model interpretability, and improve prediction in future samples; however, handling missing data when using regularization-based variable selection methods is complicated. Using listwise deletion or an ad hoc imputation strategy to deal with missing data when using regularization methods can lead to loss of precision, substantial bias, and a reduction in predictive ability. In this tutorial, we describe three approaches for fitting a LASSO when using multiple imputation to handle missing data and illustrate how to implement these approaches in practice with an applied example. We discuss implications of each approach and describe additional research that would help solidify recommendations for best practices. (PsycInfo Database Record (c) 2023 APA, all rights reserved).

Successes and lessons learned in database development for national multi-site cancer care delivery research trials: the Alliance for Clinical Trials in Oncology experience.

INTRODUCTION: Alliance for Clinical Trials in Oncology (Alliance) coordinated trials utilize Medidata Rave® (Rave) as the primary clinical data capture system. A growing number of innovative and complex cancer care delivery research (CCDR) trials are being conducted within the Alliance with the aims of studying and improving cancer-related care. Because these trials encompass patients, providers, practices, and their interactions, a defining characteristic of CCDR trials is multilevel data collection in pragmatic settings. Consequently, CCDR trials necessitated innovative strategies for database development, centralized data management, and data monitoring in the presence of these real-world multilevel relationships. Having real trial experience in working with community and academic centers, and having recently implemented five CCDR trials in Rave, we are committed to sharing our strategies and lessons learned in implementing such pragmatic trials in oncology. METHODS: Five Alliance CCDR trials are used to describe our approach to analyzing the database development needs and the novel strategies applied to overcome the unanticipated challenges we encountered. The strategies applied are organized into 3 categories: multilevel (clinic, clinic stakeholder, patient) enrollment, multilevel quantitative and qualitative data capture, including nontraditional data capture mechanisms being applied, and multilevel data monitoring. RESULTS: A notable lesson learned in each category was (1) to seek long-term solutions when developing the functionality to push patient and non-patient enrollments to their respective Rave study database that affords flexibility if new participant types are later added; (2) to be open to different data collection modalities, particularly if such modalities remove barriers to participation, recognizing that additional resources are needed to develop the infrastructure to exchange data between that modality and Rave; and (3) to facilitate multilevel data monitoring, orient site coordinators to the their trial's multiple study databases, each corresponding to a level in the hierarchy, and remind them to establish the link between patient and non-patient participants in the site-facing NCI web-based enrollment system. CONCLUSION: Although the challenges due to multilevel data collection in pragmatic settings were surmountable, our shared experience can inform and foster collaborations to collectively build on our past successes and improve on our past failures to address the gaps.

Response Patterns to Weekly Short Message Service Health Surveys Among Diverse Youth at High Risk for Acquiring HIV.

HIV researchers use short messaging service (SMS)-based surveys to monitor health behaviors more closely than what would be possible with in-person assessment. Benefits are tempered by nonresponse to completing surveys. Understanding response patterns and their associated study participant characteristics would guide more tailored use of SMS-based surveys for HIV studies. We examined response to weekly 7-item SMS surveys administered as part of an HIV prevention trial. Using Mixture hidden Markov models (MHMM), we identified the underlying response patterns shared by subgroups of participants over time and quantified the association between these response patterns and participant characteristics. Three underlying response patterns were identified; responders, responders with phone-related errors, and non-responders. Non-responders versus responders were more likely to be younger, male, cis-gender, Black and Latinx participants with histories of homelessness, incarceration, and social support service utilization. Responders with phone-related errors compared to non-responders were more likely to be Black, Latinx, female, students, and have a history of incarceration and social support service utilization. More nuanced results from MHMM analyses better inform what strategies to use for increasing SMS response rates, including assisting in securing phone ownership/service for responders with phone-related errors and identifying alternative strategies for non-responders. Actively collecting and monitoring non-delivery notification data available from SMS gateway service companies offers another opportunity to identify and connect with participants when they are willing but unable to respond during follow-up.

Collaborative biostatistics and epidemiology in academic medical centres: A survey to assess relationships with health researchers and ethical implications.

The role of collaborative biostatisticians and epidemiologists in academic medical centers and how their degree type, supervisor type, and sex influences recognition and feelings of respect is poorly understood. We conducted a cross-sectional survey of self-identified biostatisticians and epidemiologists working in academic medical centers in the US or Canada. The survey was sent to 341 contacts at 125 institutions who were asked to forward the survey invitation to faculty and staff at their institution and posted on Community sections of the American Statistical Association website. Participants were asked a variety of questions including if they felt pressured to produce specific results, whether they had intellectual and ethical freedom to pursue appropriate use of statistical methods in collaborative research, and if they felt their contributions were appropriately recognized by collaborators. We received responses from 314 biostatisticians or related methodologists. A majority were female (59%), had a doctorate degree (52%), and reported a statistician or biostatistician supervisor (69%). Overall, most participants felt valued by their collaborators, but that they did not have sufficient calendar time to meet deadlines. Doctoral-level participants reported more autonomy in their collaborations than master's level participants. Females were less likely to feel recognized and respected compared to males. The survey results suggest that while most respondents felt valued by their collaborators, they have too many projects and need more time to critically review research. Further research is needed to understand why response differs by sex and how these responses fluctuate over time.

Using Machine Learning to Predict Young People's Internet Health and Social Service Information Seeking.

Machine learning creates new opportunities to design digital health interventions for youth at risk for acquiring HIV (YARH), capitalizing on YARH's health information seeking on the internet. To date, researchers have focused on descriptive analyses that associate individual factors with health-seeking behaviors, without estimating of the strength of these predictive models. We developed predictive models by applying machine learning methods (i.e., elastic net and lasso regression models) to YARH's self-reports of internet use. The YARH were aged 14-24 years old (N = 1287) from Los Angeles and New Orleans. Models were fit to three binary indicators of YARH's lifetime internet searches for general health, sexual and reproductive health (SRH), and social service information. YARH responses regarding internet health information seeking were fed into machine learning models with potential predictor variables based on findings from previous research, including sociodemographic characteristics, sexual and gender minority identity, healthcare access and engagement, sexual behavior, substance use, and mental health. About half of the YARH reported seeking general health and SRH information and 26% sought social service information. Areas under the ROC curve (≥ .75) indicated strong predictive models and results were consistent with the existing literature. For example, higher education and sexual minority identification was associated with seeking general health, SRH, and social service information. New findings also emerged. Cisgender identity versus transgender and non-binary identities was associated with lower odds of general health, SRH, and social service information seeking. Experiencing intimate partner violence was associated with higher odds of seeking general health, SRH, and social service information. Findings demonstrate the ability to develop predictive models to inform targeted health information dissemination strategies but underscore the need to better understand health disparities that can be operationalized as predictors in machine learning algorithms.

Streamlined Prevention and Early Intervention for Pediatric Anxiety Disorders: A Randomized Controlled Trial.

There is a need to optimize the fit between psychosocial interventions with known efficacy and the demands of real-word service delivery settings. However, adaptation of evidence-based interventions (EBI) raises questions about whether effectiveness can be retained. This randomized controlled trial (RCT) evaluated a streamlined package of cognitive, behavior, and social skills training strategies known to prevent and reduce anxiety symptom and disorder escalation in youth. A total of 109 youth (Mage = 9.72; 68% girls; 54% Latinx) at risk based on high anxiety were randomized to the streamlined prevention and early intervention (SPEI) (n = 59) or control (n = 50) and were assessed at pretest, posttest, and 12-month follow-up. A main objective was to determine whether our redesign could be delivered by community providers, with acceptable levels of fidelity, quality, and impact. In terms of process evaluation results, there was high protocol fidelity, excellent clinical process skills, few protocol adaptations, and high satisfaction with the SPEI. In terms of outcomes, there were no significant main or moderated effects of the SPEI at the immediate posttest. However, at the follow-up, youth in the SPEI reported greater self-efficacy for managing anxiety-provoking situations, greater social skills, and fewer negative cognitive errors relative to controls. Collectively, findings suggest that the redesigned SPEI might be an attractive and efficient solution for service delivery settings.

Intergenerational gaps in Mexican American values trajectories: Associations with parent-adolescent conflict and adolescent psychopathology.

Growth mixture modeling with a sample of 749 Mexican heritage families identified parallel trajectories of adolescents' and their mothers' heritage cultural values and parallel trajectories of adolescents' and their fathers' heritage cultural values from Grades 5 to 10. Parallel trajectory profiles were then used to test cultural gap-distress theory that predicts increased parent-adolescent conflict and adolescent psychopathology over time when adolescents become less aligned with Mexican heritage values compared to their parents. Six similar parallel profiles were identified for the mother-youth and father-youth dyads, but only one of the six was consistent with the hypothesized problem gap pattern in which adolescents' values were declining over time to become more discrepant from their parents. When compared to families in the other trajectory groups as a whole, mothers in the mother-adolescent problem gap trajectory group reported higher levels of mother-adolescent conflict in the 10th grade that accounted for subsequent increases in internalizing and externalizing symptoms assessed in 12th grade. Although the findings provided some support for cultural gap-distress predictions, they were not replicated with adolescent report of conflict nor with the father-adolescent trajectory group analyses. Exploratory pairwise comparisons between all six mother-adolescent trajectory groups revealed additional differences that qualified and extended these findings.

Multigroup Propensity Score Approach to Evaluating an Effectiveness Trial of the New Beginnings Program.

We used a multigroup propensity score approach to evaluate a randomized effectiveness trial of the New Beginnings Program (NBP), an intervention targeting divorced or separated families. Two features of effectiveness trials, high nonattendance rates and inclusion of an active control, make program effects harder to detect. To estimate program effects based on actual intervention participation, we created a synthetic inactive control comprised of nonattenders and assessed the impact of attending the NBP or active control relative to no intervention (inactive control). We estimated propensity scores using generalized boosted models and applied inverse probability of treatment weighting for the comparisons. Relative to the inactive control, NBP strengthened parenting quality as well as reduced child exposure to interparental conflict, parent psychological distress, and child internalizing problems. Some effects were moderated by parent gender, parent ethnicity, or child age. On the other hand, the effects of active versus inactive control were minimal for parenting and in the unexpected direction for child internalizing problems. Findings from the propensity score approach complement and enhance the interpretation of findings from the intention-to-treat approach.