Secondary consent to biospecimen use in a prostate cancer biorepository

BACKGROUND: Biorepository research has substantial societal benefits. This is one of the few studies to focus on male willingness to allow future research use of biospecimens. METHODS: This study analyzed the future research consent questions from a prostate cancer biorepository study (N = 1931). The consent form asked two questions regarding use of samples in future studies (1) without and (2) with protected health information (PHI). Yes to both questions of use of samples was categorized as Yes-Always; Yes to without and No to with PHI was categorized as Yes-Conditional; No to without PHI was categorized as Never. We analyzed this outcome to determine significant predictors for consent to Yes-Always vs. Yes-Conditional. RESULTS: 99.33 % consented to future use of samples; 88.19 % consented to future use without PHI, and among those men 10.2 % consented to future use with PHI. Comparing Yes Always and Yes Conditional responses, bivariate analyses showed that race, family history, stage of cancer, and grade of cancer (Gleason), were significant at the α = 0.05 level. Using stepwise multivariable logistic regression, we found that African–American men were significantly more likely to respond Yes Always when compared to White men (p < 0.001). Those with a family history of prostate cancer were significantly more likely to respond Yes Always (p = 0.002). CONCLUSIONS: There is general willingness to consent to future use of specimens without PHI among men

An Open-Publishing Response to the COVID-19 Infodemic

The COVID-19 pandemic catalyzed the rapid dissemination of papers and preprints investigating the disease and its associated virus, SARS-CoV-2. The multifaceted nature of COVID-19 demands a multidisciplinary approach, but the urgency of the crisis combined with the need for social distancing measures present unique challenges to collaborative science. We applied a massive online open publishing approach to this problem using Manubot. Through GitHub, collaborators summarized and critiqued COVID-19 literature, creating a review manuscript. Manubot automatically compiled citation information for referenced preprints, journal publications, websites, and clinical trials. Continuous integration workflows retrieved up-to-date data from online sources nightly, regenerating some of the manuscript\u27s figures and statistics. Manubot rendered the manuscript into PDF, HTML, LaTeX, and DOCX outputs, immediately updating the version available online upon the integration of new content. Through this effort, we organized over 50 scientists from a range of backgrounds who evaluated over 1,500 sources and developed seven literature reviews. While many efforts from the computational community have focused on mining COVID-19 literature, our project illustrates the power of open publishing to organize both technical and non-technical scientists to aggregate and disseminate information in response to an evolving crisis

Proportional odds model examining the impact of seeing uncertainty in the initial recommendation on accepting that recommendation.

Proportional odds model examining the impact of seeing uncertainty in the initial recommendation on accepting that recommendation.</p

Distribution of agreement with the final recommendation.

The top panel (A) shows the agreement between treatment arms among those who were more likely to agree with the final recommendation at baseline; the bottom panel (B) shows agreement between treatment arms among those less likely to agree with the final recommendation at baseline.</p

Causal Inference Is Not Just a Statistics Problem

AbstractThis article introduces a collection of four datasets, similar to Anscombe’s quartet, that aim to highlight the challenges involved when estimating causal effects. Each of the four datasets is generated based on a distinct causal mechanism: the first involves a collider, the second involves a confounder, the third involves a mediator, and the fourth involves the induction of M-Bias by an included factor. The article includes a mathematical summary of each dataset, as well as directed acyclic graphs that depict the relationships between the variables. Despite the fact that the statistical summaries and visualizations for each dataset are identical, the true causal effect differs, and estimating it correctly requires knowledge of the data-generating mechanism. These example datasets can help practitioners gain a better understanding of the assumptions underlying causal inference methods and emphasize the importance of gathering more information beyond what can be obtained from statistical tools alone. The article also includes R code for reproducing all figures and provides access to the datasets themselves through an R package named “quartets.” Supplementary materials for this article are available online

Baseline characteristics.

Baseline characteristics.</p

Proportional odds model examining the impact of seeing uncertainty in the initial recommendation on accepting a future recommendation.

Proportional odds model examining the impact of seeing uncertainty in the initial recommendation on accepting a future recommendation.</p

Sensitivity analysis: Proportional odds model fit stratified by baseline likelihood to sanitize mobile phone (with 5 levels).

Sensitivity analysis: Proportional odds model fit stratified by baseline likelihood to sanitize mobile phone (with 5 levels).</p

Quantitative Evaluation of the Community Research Fellows Training Program

Context: The Community Research Fellows Training (CRFT) program is a community-based participatory research (CBPR) initiative for the St. Louis area. This fifteen week program, based on a Master in Public Health curriculum, was implemented by the Division of Public Health Sciences at Washington University School of Medicine in partnership with the Siteman Cancer Center. Objectives: We measure the knowledge gained by participants and evaluate participant and faculty satisfaction of the CRFT program both in terms of meeting learning objectives and actively engaging the community in the research process.Participants: We conducted analyses on 44 community members who participated in the CRFT program and completed the baseline and follow-up knowledge assessments.Main Outcome Measures: Knowledge gain is measured by a baseline and follow-up assessment given at the first and final session. Additionally, pre- and post-tests are given after the first 12 sessions. To measure satisfaction, program evaluations are completed by both the participants and faculty after each topic. Mid-way through the program, a mid-term assessment was administered to assess the program’s community engagement. We analyzed the results from the assessments, pre- and post-tests, and evaluations.Results: The CRFT participants’ knowledge increased at follow-up as compared with baseline on average by a 16.5 point difference (p<0.0001). Post-test scores were higher than pre-test scores for 11 of the 12 sessions. Both participants and faculty enjoyed the training and rated all session well.Conclusions: The CRFT program was successful in increasing community knowledge, in participant satisfaction, and in faculty satisfaction. This success has enhanced the infrastructure for CBPR as well as led to CBPR pilot projects that address health disparities in the St. Louis Greater Metropolitan Area

Illustration of the false discovery rate (red) and false confirmation rate (blue) for second-generation <i>p</i>-values (solid lines).

<p>The false discovery rate (red) and false non-discovery rate (blue) from a comparable hypothesis test are shown as dotted lines. This example uses r = 1, α = 0.05, δ = σ/2, and n = 16, but the ordering of the curves is quite general.</p