Advantages of a Truly Open-Access Data-Sharing Model

Up Front and Open? Shrouded in Secrecy? Or Somewhere in Between? A Meta-Research Systematic Review of Open Science Practices in Sport Medicine Research

OBJECTIVE: To investigate open science practices in research published in the top 5 sports medicine journals from May 1, 2022, and October 1, 2022. DESIGN: A meta-research systematic review. LITERATURE SEARCH: Open science practices were searched in MEDLINE. STUDY SELECTION CRITERIA: We included original scientific research published in one of the identified top 5 sports medicine journals in 2022 as ranked by Clarivate: (1) British Journal of Sports Medicine, (2) Journal of Sport and Health Science, (3) American Journal of Sports Medicine, (4) Medicine and Science in Sports and Exercise, and (5) Sports Medicine-Open. Studies were excluded if they were systematic reviews, qualitative research, gray literature, or animal or cadaver models. DATA SYNTHESIS: Open science practices were extracted in accordance with the Transparency and Openness Promotion guidelines and patient and public involvement. RESULTS: Two hundred forty-three studies were included. The median number of open science practices in each study was 2, out of a maximum of 12 (range: 0-8; interquartile range: 2). Two hundred thirty-four studies (96%, 95% confidence interval [CI]: 94%-99%) provided an author conflict-of-interest statement and 163 (67%, 95% CI: 62%-73%) reported funding. Twenty-one studies (9%, 95% CI: 5%-12%) provided open-access data. Fifty-four studies (22%, 95% CI: 17%-27%) included a data availability statement and 3 (1%, 95% CI: 0%-3%) made code available. Seventy-six studies (32%, 95% CI: 25%-37%) had transparent materials and 30 (12%, 95% CI: 8%-16%) used a reporting guideline. Twenty-eight studies (12%, 95% CI: 8%-16%) were preregistered. Six studies (3%, 95% CI: 1%-4%) published a protocol. Four studies (2%, 95% CI: 0%-3%) reported an analysis plan a priori. Seven studies (3%, 95% CI: 1%-5%) reported patient and public involvement. CONCLUSION: Open science practices in the sports medicine field are extremely limited. The least followed practices were sharing code, data, and analysis plans. J Orthop Sports Phys Ther 2023;53(12):1-13. Epub 20 October 2023. doi:10.2519/jospt.2023.12016.

Heterogeneity and Utility of Pharmaceutical Company Sharing of Individual-Participant Data Packages

Importance

The pharmaceutical industry has made substantial investments in developing processes for sharing individual-participant data (IPD) from clinical trials. However, the utility and completeness of shared IPD and supporting documents must be evaluated to ensure the potential for scientific advancements from the data sharing ecosystem can be realized.

Objective

To assess the utility and completeness of IPD and supporting documents provided from industry-sponsored clinical trials.

Design, Setting, and Participants

From February 9, 2022, to February 9, 2023, 91 of 203 clinical trials supporting US Food and Drug Administration registrations of anticancer medicines for the treatment of solid tumors from the past decade were confirmed as eligible for IPD request. This quality improvement study performed a retrospective audit of the utility and completeness of the IPD and supporting documents provided from the 91 clinical trials for a planned meta-analysis.

Exposures

Request for IPD from 91 clinical oncology trials indicated as eligible for the request.

Main Outcomes and Measures

The utility and completeness of the IPD and supporting documents provided.

Results

The IPD packages were obtained from 70 of 91 requested clinical trials (77%). The median time to data provision was 123 (range, 117-352) days. Redactions were observed in 18 of the acquired IPD packages (26%) for outcome data, 11 (16%) for assessment variables, and 19 (27%) for adjustment data. Additionally, 20 IPD packages (29%) lacked a clinical study report, 4 (6%) had incomplete or missing data dictionaries, and 20 (29%) were missing anonymization or redaction description files. Access to IPD from 21 eligible trials (23%) was not granted.

Conclusions and Relevance

In this quality improvement study, there was substantial variability within the provided IPD packages regarding the completeness of key data variables and supporting documents. To improve the data sharing ecosystem, key areas for enhancement include (1) ensuring that clinical trials are eligible for IPD sharing, (2) making eligible IPD transparently accessible, and (3) ensuring that IPD packages meet a standard of utility and completeness.

Medicines and Healthcare products Regulatory Agency's "Consultation on proposals for legislative changes for clinical trials": a response from the Trials Methodology Research Partnership Adaptive Designs Working Group, with a focus on data sharing

In the UK, the Medicines and Healthcare products Regulatory Agency consulted on proposals "to improve and strengthen the UK clinical trials legislation to help us make the UK the best place to research and develop safe and innovative medicines". The purpose of the consultation was to help finalise the proposals and contribute to the drafting of secondary legislation. We discussed these proposals as members of the Trials Methodology Research Partnership Adaptive Designs Working Group, which is jointly funded by the Medical Research Council and the National Institute for Health and Care Research. Two topics arose frequently in the discussion: the emphasis on legislation, and the absence of questions on data sharing. It is our opinion that the proposals rely heavily on legislation to change practice. However, clinical trials are heterogeneous, and as a result some trials will struggle to comply with all of the proposed legislation. Furthermore, adaptive design clinical trials are even more heterogeneous than their non-adaptive counterparts, and face more challenges. Consequently, it is possible that increased legislation could have a greater negative impact on adaptive designs than non-adaptive designs. Overall, we are sceptical that the introduction of legislation will achieve the desired outcomes, with some exceptions. Meanwhile the topic of data sharing - making anonymised individual-level clinical trial data available to other investigators for further use - is entirely absent from the proposals and the consultation in general. However, as an aspect of the wider concept of open science and reproducible research, data sharing is an increasingly important aspect of clinical trials. The benefits of data sharing include faster innovation, improved surveillance of drug safety and effectiveness and decreasing participant exposure to unnecessary risk. There are already a number of UK-focused documents that discuss and encourage data sharing, for example, the Concordat on Open Research Data and the Medical Research Council's Data Sharing Policy. We strongly suggest that data sharing should be the norm rather than the exception, and hope that the forthcoming proposals on clinical trials invite discussion on this important topic.

The Trade Secret Taboo: Open Science Methods are Required to Improve Prediction Models in Sports Medicine and Performance

Clinical prediction models in sports medicine that utilize regression or machine learning techniques have become more widely published, used, and disseminated. However, these models are typically characterized by poor methodology and incomplete reporting, and an inadequate evaluation of performance, leading to unreliable predictions and weak clinical utility within their intended sport population. Before implementation in practice, models require a thorough evaluation. Strong replicable methods and transparency reporting allow practitioners and researchers to make independent judgments as to the model's validity, performance, clinical usefulness, and confidence it will do no harm. However, this is not reflected in the sports medicine literature. As shown in a recent systematic review of models for predicting sports injury models, most were typically characterized by poor methodology, incomplete reporting, and inadequate performance evaluation. Because of constraints imposed by data from individual teams, the development of accurate, reliable, and useful models is highly reliant on external validation. However, a barrier to collaboration is a desire to maintain a competitive advantage; a team's proprietary information is often perceived as high value, and so these 'trade secrets' are frequently guarded. These 'trade secrets' also apply to commercially available models, as developers are unwilling to share proprietary (and potentially profitable) development and validation information. In this Current Opinion, we: (1) argue that open science is essential for improving sport prediction models and (2) critically examine sport prediction models for open science practices.

Pharmacokinetic analysis across studies to drive knowledge-integration: A tutorial on individual patient data meta-analysis (IPDMA)

Answering challenging questions in drug development sometimes requires pharmacokinetic (PK) data analysis across different studies, for example, to characterize PKs across diverse regions or populations, or to increase statistical power for subpopulations by combining smaller size trials. Given the growing interest in data sharing and advanced computational methods, knowledge integration based on multiple data sources is increasingly applied in the context of model-informed drug discovery and development. A powerful analysis method is the individual patient data meta-analysis (IPDMA), leveraging systematic review of databases and literature, with the most detailed data type of the individual patient, and quantitative modeling of the PK processes, including capturing heterogeneity of variance between studies. The methodology that should be used in IPDMA in the context of population PK analysis is summarized in this tutorial, highlighting areas of special attention compared to standard PK modeling, including hierarchical nested variability terms for interstudy variability, and handling between-assay differences in limits of quantification within a single analysis. This tutorial is intended for any pharmacological modeler who is interested in performing an integrated analysis of PK data across different studies in a systematic and thorough manner, to answer questions that transcend individual primary studies.

Whole Person Modeling: a transdisciplinary approach to mental health research

The growing global burden of mental illness has prompted calls for innovative research strategies. Theoretical models of mental health include complex contributions of biological, psychosocial, experiential, and other environmental influences. Accordingly, neuropsychiatric research has self-organized into largely isolated disciplines working to decode each individual contribution. However, research directly modeling objective biological measurements in combination with cognitive, psychological, demographic, or other environmental measurements is only now beginning to proliferate. This review aims to (1) to describe the landscape of modern mental health research and current movement towards integrative study, (2) to provide a concrete framework for quantitative integrative research, which we call Whole Person Modeling, (3) to explore existing and emerging techniques and methods used in Whole Person Modeling, and (4) to discuss our observations about the scarcity, potential value, and untested aspects of highly transdisciplinary research in general. Whole Person Modeling studies have the potential to provide a better understanding of multilevel phenomena, deliver more accurate diagnostic and prognostic tests to aid in clinical decision making, and test long standing theoretical models of mental illness. Some current barriers to progress include challenges with interdisciplinary communication and collaboration, systemic cultural barriers to transdisciplinary career paths, technical challenges in model specification, bias, and data harmonization, and gaps in transdisciplinary educational programs. We hope to ease anxiety in the field surrounding the often mysterious and intimidating world of transdisciplinary, data-driven mental health research and provide a useful orientation for students or highly specialized researchers who are new to this area.

FDA Oncology Center of Excellence Crowdsourcing Initiative: Outreach to the Scientific Community to Identify Research Questions for Pooled Analyses of Oncology Clinical Trial Data

The FDA Oncology Center of Excellence recently launched a crowdsourcing pilot to request ideas from the scientific community for research questions that FDA could address with pooled analyses of clinical trial data submitted to the agency for regulatory purposes. This effort builds on FDA's track record of publishing pooled analyses to explore scientific questions that cannot be addressed in a single trial due to limited sample size. The research crowdsourcing pilot tested a new approach for obtaining external input on regulatory science activities, because FDA is generally unable to share patient-level data outside of the agency due to federal disclosure laws and regulations protecting different types of data submitted in regulatory applications. We received 29 submissions over the 28-day crowdsourcing campaign, including one research idea that we are exploring for possible follow-up. Based on our experience with this pilot, we learned that crowdsourcing is a promising new approach to gather external input and feedback. We identified opportunities to build understanding in the external oncology community about the types of data typically included in regulatory applications and expand the dissemination of published FDA pooled analyses to help inform future drug development and clinical practice.

Operational Challenges of an Asia-Pacific Academic Oncology Clinical Trial

PURPOSE

The Asia-Pacific (APAC) region is a major focus for multinational clinical trials, although its cultural, linguistic, economic, and regulatory diversity pose significant challenges for trial conduct, particularly for academic clinical trials.

METHODS

We describe our experience running the investigator-initiated phase III randomized, fully accrued, Aspirin for Dukes C and high-risk Dukes B Colorectal cancer trial (ASCOLT, ClinicalTrials.gov identifier: NCT00565708, N = 1,587), studying the benefit of aspirin in resected high-risk colorectal cancer. ASCOLT opened in 2008 and is the first large academic adjuvant trial fully conducted in the APAC region. Centrally coordinated by the Trial Management Team at the National Cancer Centre Singapore, it has involved 74 sites across 12 APAC countries/regions, including five middle-income countries.

RESULTS

Challenges encountered included regulatory complexity, communication and logistical barriers, limited funding and resources, disparate experience and infrastructure across sites, recruitment holds because of changes in local laws, patient attrition, and disruptions caused by the COVID-19 pandemic. Over 100 contracts and 49 ethics board reviews were required, contributing to a lengthy prestudy preparation time of 2 years and start-up times of approximately 6 months per site. Some of the mitigating actions included engaging local cooperative groups (eg, the Australasian Gastro-Intestinal Trials Group in Australia and New Zealand) and seven contract research organizations to manage sites, regular communication with the central team, transition to electronic data management, and a centralized drug-dispensing system.

CONCLUSION

To ensure an efficient and patient-centered clinical trials environment in the APAC region and sustained growth, we suggest coordinated approaches to harmonize regulatory processes, APAC academic oncology trials consortia to streamline processes and provide governance, and ongoing commitment from governments, funding agents, and industry.

Ten simple rules for providing bioinformatics support within a hospital

Bioinformatics has become a key aspect of the biomedical research programmes of many hospitals' scientific centres, and the establishment of bioinformatics facilities within hospitals has become a common practice worldwide. Bioinformaticians working in these facilities provide computational biology support to medical doctors and principal investigators who are daily dealing with data of patients to analyze. These bioinformatics analysts, although pivotal, usually do not receive formal training for this job. We therefore propose these ten simple rules to guide these bioinformaticians in their work: ten pieces of advice on how to provide bioinformatics support to medical doctors in hospitals. We believe these simple rules can help bioinformatics facility analysts in producing better scientific results and work in a serene and fruitful environment.

Clinical trial data-sharing policies among journals, funding agencies, foundations, and other professional organizations: a scoping review

BACKGROUND AND OBJECTIVES

To identify the similarities and differences in data-sharing policies for clinical trial data that are endorsed by biomedical journals, funding agencies, and other professional organizations. Additionally, to determine the beliefs, and opinions regarding data-sharing policies for clinical trials discussed in articles published in biomedical journals.

METHODS

Two searches were conducted, a bibliographic search for published articles that present beliefs, opinions, similarities, and differences regarding policies governing the sharing of clinical trial data. The second search analyzed the gray literature (non-peer-reviewed publications) to identify important data-sharing policies in selected biomedical journals, foundations, funding agencies, and other professional organizations.

RESULTS

A total of 471 articles were included after database search and screening, with 45 from the bibliographic search and 426 from the gray literature search. A total of 424 data-sharing policies were included. Fourteen of the 45 published articles from the bibliographic search (31.1%) discussed only advantages specific to data-sharing policies, 27 (27/45; 60%) discussed both advantages and disadvantages, and 4 (4/45; 8.9%) discussed only disadvantages specific. A total of 216 journals (of 270; 80%) specified a data-sharing policy provided by the journal itself. One hundred industry data-sharing policies were included, and 32 (32%) referenced a data-sharing policy on their website. One hundred and thirty-six (42%) organizations (of 327) specified a data-sharing policy.

CONCLUSION

We found many similarities listed as advantages to data-sharing and fewer disadvantages were discussed within the literature. Additionally, we found a wide variety of commonalities and differences-such as the lack of standardization between policies, and inadequately addressed details regarding the accessibility of research data-that exist in data-sharing policies endorsed by biomedical journals, funding agencies, and other professional organizations. Our study may not include information on all data sharing policies and our data is limited to the entities' descriptions of each policy.

Global Oncology Medical Diplomacy Working Group Inaugural Meeting: Defining Worldwide Barriers to Germline Genomics in Cancer Prevention and Management

We convened an international working group to examine the issues that challenge equity and inclusion in genetic medicine. Specifically, 72 internationally known experts in oncology and cancer genetics from 34 countries (the Global Oncology Medical Diplomacy Working Group), gathered virtually on January 4-5, 2022, for the "Humanity Cancer Germline Convergence and Divergence Cancer Predispositions" conference hosted by Memorial Sloan Kettering Cancer Center, in collaboration with the United Arab Emirates Ministry of Health and the Al Jalila Foundation. The goal of the conference was to broaden transnational understanding of the current state of genetics in preventive and therapeutic cancer medicine, and to define barriers to increased uptake of germline genomics to decrease the international burden of cancer. Here, we highlight the overarching barriers that were defined through this effort. These global barriers to incorporating germline genomics into optimal cancer care can inform ongoing research, collaboration, and advocacy for equitable, cost-effective genomic medicine for populations worldwide.

The design and evaluation of hybrid controlled trials that leverage external data and randomization

Patient-level data from completed clinical studies or electronic health records can be used in the design and analysis of clinical trials. However, these external data can bias the evaluation of the experimental treatment when the statistical design does not appropriately account for potential confounders. In this work, we introduce a hybrid clinical trial design that combines the use of external control datasets and randomization to experimental and control arms, with the aim of producing efficient inference on the experimental treatment effects. Our analysis of the hybrid trial design includes scenarios where the distributions of measured and unmeasured prognostic patient characteristics differ across studies. Using simulations and datasets from clinical studies in extensive-stage small cell lung cancer and glioblastoma, we illustrate the potential advantages of hybrid trial designs compared to externally controlled trials and randomized trial designs.

Patient-level external control data from prior clinical studies or electronic health records can be used in the design and analysis of clinical trials. Here the authors report a hybrid trial design combining the use of external control data and randomization to test experimental treatments, using small cell lung cancer and glioblastoma datasets as examples.

Return to performance following severe ankle, knee, and hip injuries in National Basketball Association players

The purpose of this study was to compare basketball performance markers 1 y prior to initial severe lower extremity injury, including ankle, knee, and hip injuries, to 1 and 2 y following injury during the regular National Basketball Association (NBA) season. Publicly available data were extracted through a reproducible extraction computed programmed process. Eligible participants were NBA players with at least three seasons played between 2008 and 2019, with a time-loss injury reported during the study period. Basketball performance was evaluated for season minutes, points, and rebounds. Prevalence of return to performance and linear regressions were calculated. A total of 285 athletes sustained a severe lower extremity injury. A total of 196 (69%) played for 1 y and 130 (45%) played for 2 y following the injury. A total of 58 (30%) players participated in a similar number of games and 57 (29%) scored similar points 1 y following injury. A total of 48 (37%) participated in a similar number of games and 55 (42%) scored a similar number of points 2 y following injury. Fewer than half of basketball players who suffered a severe lower extremity injury were participating at the NBA level 2 y following injury, with similar findings for groin/hip/thigh, knee, and ankle injuries. Fewer than half of players were performing at previous preinjury levels 2 y following injury. Suffering a severe lower extremity injury may be a prognostic factor that can assist sports medicine professionals to educate and set performance expectations for NBA players.

Anti-Androgen Therapy Overcomes the Time Delay in Initiation of Salvage Radiation Therapy and Rescues the Oncological Outcomes in Men with Recurrent Prostate Cancer After Radical Prostatectomy: A Post Hoc Analysis of the RTOG-9601 Trial Data

Background

It is unknown whether the addition of anti-androgen therapy (AAT) to late salvage radiation therapy (sRT) can lead to oncological outcomes equivalent to that of early sRT in men with recurrent prostate cancer (CaP) after surgery.

Methods

Data on 670 men who participated in the Radiation Therapy Oncology Group (RTOG)-9601 trial and who experienced biochemical recurrence were extracted using the National Clinical Trials Network (NCTN) data archive platform. Patients were stratified into four treatment groups: early sRT (pre-sRT prostate-specific antigen [PSA] < 0.7 ng/mL) and late sRT (pre-sRT PSA ≥ 0.7 ng/mL) with/without concomitant AAT, based on cut-offs reported in the original trial. Time-varying Cox proportional hazards and Fine–Gray competing-risk regression analyses assessed the adjusted hazards of overall mortality, CaP-specific mortality, and metastasis among the four treatment groups.

Results

At 15-years (median follow-up of 14.7 years), for patients treated with early sRT, early sRT with AAT, late sRT, and late sRT with AAT, the overall mortality, CaP-specific mortality, and metastasis rates were 22.9, 22.8, 40.1, and 22.9% (log-rank p = 0.0039), 12.1, 3.9, 22.7, and 8.0% (Gray’s p = 0.0004), and 18.8, 14.6, 35.9, and 19.5% (Gray’s p = 0.0004), respectively. Time-varying multivariable adjusted analysis demonstrated increased hazards of overall mortality in patients receiving delayed sRT versus early sRT (hazards ratio [HR] 1.49, 95% confidence interval [CI] 1.02–2.17); however, no difference remained after the addition of concomitant AAT to late sRT (HR 0.85, 95% CI 0.55–1.32, referent early sRT). Likewise, the hazards of cancer-specific mortality and metastatic progression were worse for late sRT when compared with early sRT, but were no different after the addition of AAT to late sRT.

Conclusions

Poorer outcomes associated with late sRT in men with recurrent CaP may be rescued by delivery of concomitant AAT.

Project 2025: Proposals for the Continued Success of Drug Development in Acute Myeloid Leukemia

The Food and Drug Administration Oncology Center of Excellence initiated Project 2025 to develop five-year goals in specific areas of oncology drug development. This meeting, in October 2020, brought together a panel of regulators and academic experts in acute myeloid leukemia (AML) to discuss opportunities to maximize the success that has recently occurred in AML drug development. The panel discussed challenges and opportunities in clinical trial design and novel endpoints, and outlined key considerations for drug development to facilitate continued growth in the field.

Leveraging external data in the design and analysis of clinical trials in neuro-oncology

Integration of external control data, with patient-level information, in clinical trials has the potential to accelerate the development of new treatments in neuro-oncology by contextualising single-arm studies and improving decision making (eg, early stopping decisions). Based on a series of presentations at the 2020 Clinical Trials Think Tank hosted by the Society of Neuro-Oncology, we provide an overview on the use of external control data representative of the standard of care in the design and analysis of clinical trials. High-quality patient-level records, rigorous methods, and validation analyses are necessary to effectively leverage external data. We review study designs, statistical methods, risks, and potential distortions in using external data from completed trials and real-world data, as well as data sources, data sharing models, ongoing work, and applications in glioblastoma.

Characterization of Rookie Season Injury and Illness and Career Longevity Among National Basketball Association Players

IMPORTANCE

There is limited research investigating injury and illness among professional basketball players during their rookie season. By improving the understanding of injury incidence and risk specific to rookie players, sports medicine clinicians may be able to further individualize injury mitigation programs that address the unique needs of rookie players.

OBJECTIVE

To compare incidence and rate ratio (RR) of injury and illness among professional National Basketball Association (NBA) players in their rookie season with veteran players and to explore the association of sustaining an injury rookie season with career longevity.

DESIGN, SETTING, AND PARTICIPANTS

This retrospective cohort study used an online data repository and extracted publicly available data about NBA players between the 2007 and 2008 season to the 2018 and 2019 season. Available data for initial injury and all subsequent injuries were extracted during this time frame.

EXPOSURES

Injury and illness based on injury status during the rookie season of professional NBA players.

MAIN OUTCOMES AND MEASURES

Injury and illness incidence and RR. Association of injury during the rookie season with career longevity was assessed via Poisson regressions.

RESULTS

Of the 12 basketball seasons analyzed, 904 NBA players were included (mean [SD] age, 24.6 [3.9] years; body mass index, 24.8 [1.8]). The injury and illness incidence for rookie players was 14.28 per 1000 athlete game exposures (AGEs). Among all body regions, ankle injuries had the greatest injury incidence among players injured during their rookie season (3.17 [95% CI, 3.15-3.19] per 1000 AGEs). Rookie athletes demonstrated higher RR compared with veterans across multiple regions of the body (ankle: 1.32; 95% CI, 1.12 to 1.52; foot/toe: 1.29; 95% CI, 0.97 to 1.61; shoulder/arm/elbow: 1.43; 95% CI, 1.10 to 1.77; head/neck: 1.21; 95% CI, 0.61 to 1.81; concussions: 2.39; 95% CI, 1.89 to 2.90; illness: 1.14; 95% CI, 0.87 to 1.40), and demonstrated a higher rate of initial injuries compared with veteran players (1.41; 95% CI, 1.29 to 1.53). Players who sustained an injury rookie season demonstrated an unadjusted decrease in total seasons played (-0.4 [95% CI, -0.5 to -0.3] log years; P < .001), but this decrease was not observed within adjusted analysis (0.1 [95% CI, -0.1 to 0.2] log years; P = .36).

CONCLUSIONS AND RELEVANCE

In this study, rookie athletes demonstrated the highest injury incidence at the ankle and increased RR across multiple regions. These findings may reflect differences in preseason conditioning or load variables impacting rookie athletes and warrant further investigation. Future research is needed to determine the association of cumulative injury burden vs a singular injury event on career longevity.

Implementing the FAIR Data Principles in precision oncology: review of supporting initiatives

Compelling research has recently shown that cancer is so heterogeneous that single research centres cannot produce enough data to fit prognostic and predictive models of sufficient accuracy. Data sharing in precision oncology is therefore of utmost importance. The Findable, Accessible, Interoperable and Reusable (FAIR) Data Principles have been developed to define good practices in data sharing. Motivated by the ambition of applying the FAIR Data Principles to our own clinical precision oncology implementations and research, we have performed a systematic literature review of potentially relevant initiatives. For clinical data, we suggest using the Genomic Data Commons model as a reference as it provides a field-tested and well-documented solution. Regarding classification of diagnosis, morphology and topography and drugs, we chose to follow the World Health Organization standards, i.e. ICD10, ICD-O-3 and Anatomical Therapeutic Chemical classifications, respectively. For the bioinformatics pipeline, the Genome Analysis ToolKit Best Practices using Docker containers offer a coherent solution and have therefore been selected. Regarding the naming of variants, we follow the Human Genome Variation Society's standard. For the IT infrastructure, we have built a centralized solution to participate in data sharing through federated solutions such as the Beacon Networks.

Status, use and impact of sharing individual participant data from clinical trials: a scoping review

OBJECTIVES

To explore the impact of data-sharing initiatives on the intent to share data, on actual data sharing, on the use of shared data and on research output and impact of shared data.

ELIGIBILITY CRITERIA

All studies investigating data-sharing practices for individual participant data (IPD) from clinical trials.

SOURCES OF EVIDENCE

We searched the Medline database, the Cochrane Library, the Science Citation Index Expanded and the Social Sciences Citation Index via Web of Science, and preprints and proceedings of the International Congress on Peer Review and Scientific Publication. In addition, we inspected major clinical trial data-sharing platforms, contacted major journals/publishers, editorial groups and some funders.

CHARTING METHODS

Two reviewers independently extracted information on methods and results from resources identified using a standardised questionnaire. A map of the extracted data was constructed and accompanied by a narrative summary for each outcome domain.

RESULTS

93 studies identified in the literature search (published between 2001 and 2020, median: 2018) and 5 from additional information sources were included in the scoping review. Most studies were descriptive and focused on early phases of the data-sharing process. While the willingness to share IPD from clinical trials is extremely high, actual data-sharing rates are suboptimal. A survey of journal data suggests poor to moderate enforcement of the policies by publishers. Metrics provided by platforms suggest that a large majority of data remains unrequested. When requested, the purpose of the reuse is more often secondary analyses and meta-analyses, rarely re-analyses. Finally, studies focused on the real impact of data-sharing were rare and used surrogates such as citation metrics.

CONCLUSIONS

There is currently a gap in the evidence base for the impact of IPD sharing, which entails uncertainties in the implementation of current data-sharing policies. High level evidence is needed to assess whether the value of medical research increases with data-sharing practices.

The Use of External Control Data for Predictions and Futility Interim Analyses in Clinical Trials

PURPOSE

External control data from completed clinical trials and electronic health records can be valuable for the design and analysis of future clinical trials. We discuss the use of external control data for early stopping decisions in randomized clinical trials (RCTs).

METHODS

We specify interim analyses (IAs) approaches for RCTs, which allow investigators to integrate external data into early futility stopping decisions. IAs utilize predictions based on early data from the RCT, possibly combined with external data. These predictions at IAs express the probability that the trial will generate significant evidence of positive treatment effects. The trial is discontinued if this predictive probability becomes smaller than a pre-specified threshold. We quantify efficiency gains and risks associated with the integration of external data into interim decisions. We then analyze a collection of glioblastoma (GBM) datasets, to investigate if the balance of efficiency gains and risks justify the integration of external data into the IAs of future GBM RCTs.

RESULTS

Our analyses illustrate the importance of accounting for potential differences between the distributions of prognostic variables in the RCT and in the external data to effectively leverage external data for interim decisions. Using GBM datasets, we estimate that the integration of external data increases the probability of early stopping of ineffective experimental treatments by up to 25% compared to IAs that don't leverage external data. Additionally, we observe a reduction of the probability of early discontinuation for effective experimental treatments, which improves the RCT power.

CONCLUSION

Leveraging external data for IAs in RCTs can support early stopping decisions and reduce the number of enrolled patients when the experimental treatment is ineffective.

Temporal Trends and Severity in Injury and Illness Incidence in the National Basketball Association Over 11 Seasons

BACKGROUND

There is a paucity of current data describing injuries in professional basketball players. Utilizing publicly available injury data will allow for greater transparency for stakeholders to use the data as a shared resource to create future basketball injury prevention programs.

PURPOSE

To describe injury and illness incidence, severity, and temporal trends in National Basketball Association (NBA) players. Among those who develop time-loss injury or illness, we estimated severity based on games missed because of injury or illness.

STUDY DESIGN

Descriptive epidemiology study.

METHODS

Publicly available NBA data were extracted through a reproducible computer-programmed process from the 2008 to 2019 seasons. Data were externally validated by 2 independent reviewers through other publicly available data sources. Injury and illness were calculated per 1000 athlete game-exposures (AGEs). Injury severity was calculated as games missed because of injury or illness. Injury and illness data were stratified by body part, position, severity (slight, minor, moderate, or severe), month, and year.

RESULTS

A total of 1369 players played a total of 302,018 player-games, with a total of 5375 injuries and illnesses. The overall injury and illness incidence was 17.80 per 1000 AGEs. The median injury severity was 3 games (interquartile range, 0-6 games) missed per injury. Overall, 33% of injuries were classified as slight; 26%, as minor; 26%, as moderate; and 15%, as severe. The ankle (2.57 injuries/1000 AGEs), knee (2.44 injuries/1000 AGEs), groin/hip/thigh (1.99 injuries/1000 AGEs), and illness (1.85 illnesses/1000 AGEs) had the greatest incidence of injury and illness. Neither injury or illness incidence nor severity was different among basketball playing positions. Injury incidence demonstrated increasing incremental trends with season progression. Injuries were similar throughout the 11-year reporting period, except for a substantial increase in the lockout-shortened 2012 season.

CONCLUSION

The ankle and knee had the greatest injury incidence. Injury incidence was similar among basketball positions. Injury incidence increased throughout the season, demonstrating the potential relationship between player load and injury incidence.

Time to get serious about the detection and monitoring of early lung disease in cystic fibrosis

Structural and functional defects within the lungs of children with cystic fibrosis (CF) are detectable soon after birth and progress throughout preschool years often without overt clinical signs or symptoms. By school age, most children have structural changes such as bronchiectasis or gas trapping/hypoperfusion and lung function abnormalities that persist into later life. Despite improved survival, gains in forced expiratory volume in one second (FEV₁) achieved across successive birth cohorts during childhood have plateaued, and rates of FEV₁ decline in adolescence and adulthood have not slowed. This suggests that interventions aimed at preventing lung disease should be targeted to mild disease and commence in early life. Spirometry-based classifications of 'normal' (FEV₁≥90% predicted) and 'mild lung disease' (FEV₁ 70%-89% predicted) are inappropriate, given the failure of spirometry to detect significant structural or functional abnormalities shown by more sensitive imaging and lung function techniques. The state and readiness of two imaging (CT and MRI) and two functional (multiple breath washout and oscillometry) tools for the detection and monitoring of early lung disease in children and adults with CF are discussed in this article.Prospective research programmes and technological advances in these techniques mean that well-designed interventional trials in early lung disease, particularly in young children and infants, are possible. Age appropriate, randomised controlled trials are critical to determine the safety, efficacy and best use of new therapies in young children. Regulatory bodies continue to approve medications in young children based on safety data alone and extrapolation of efficacy results from older age groups. Harnessing the complementary information from structural and functional tools, with measures of inflammation and infection, will significantly advance our understanding of early CF lung disease pathophysiology and responses to therapy. Defining clinical utility for these novel techniques will require effective collaboration across multiple disciplines to address important remaining research questions. Future impact on existing management burden for patients with CF and their family must be considered, assessed and minimised.To address the possible role of these techniques in early lung disease, a meeting of international leaders and experts in the field was convened in August 2019 at the Australiasian Cystic Fibrosis Conference. The meeting entitiled 'Shaping imaging and functional testing for early disease detection of lung disease in Cystic Fibrosis', was attended by representatives across the range of disciplines involved in modern CF care. This document summarises the proceedings, key priorities and important research questions highlighted.

The sharing of research data facing the COVID-19 pandemic

During the previous Ebola and Zika outbreaks, researchers shared their data, allowing many published epidemiological studies to be produced only from open research data, to speed up investigations and control of these infections. This study aims to evaluate the dissemination of the COVID-19 research data underlying scientific publications. Analysis of COVID-19 publications from December 1, 2019, to April 30, 2020, was conducted through the PubMed Central repository to evaluate the research data available through its publication as supplementary material or deposited in repositories. The PubMed Central search generated 5,905 records, of which 804 papers included complementary research data, especially as supplementary material (77.4%). The most productive journals were The New England Journal of Medicine, The Lancet and The Lancet Infectious Diseases, the most frequent keyword was pneumonia, and the most used repositories were GitHub and GenBank. An expected growth in the number of published articles following the course of the pandemics is confirmed in this work, while the underlying research data are only 13.6%. It can be deduced that data sharing is not a common practice, even in health emergencies, such as the present one. High-impact generalist journals have accounted for a large share of global publishing. The topics most often covered are related to epidemiological and public health concepts, genetics, virology and respiratory diseases, such as pneumonia. However, it is essential to interpret these data with caution following the evolution of publications and their funding in the coming months.

Transparency in surgical randomized clinical trials: cross-sectional observational study

BACKGROUND

RCTs provide the scientific basis upon which treatment decisions are made. To facilitate critical review, it is important that methods and results are reported transparently. The aim of this study was to explore transparency in surgical RCTs with respect to trial registration, disclosure of funding sources, declarations of investigator conflicts and data-sharing.

METHODS

This was a cross-sectional review of published surgical RCTs. Ten high-impact journals were searched systematically for RCTs published in years 2009, 2012, 2015 and 2018. Four domains of transparency were explored: trial registration, disclosure of funding, disclosure of investigator conflicts, and a statement relating to data-sharing.

RESULTS

Of 611 RCTs, 475 were eligible for analysis. Some 397 RCTs (83.6 per cent) were registered on a trial database, of which 190 (47·9 per cent) had been registered prospectively. Prospective registration increased over time (26 per cent in 2009, 33·0 per cent in 2012, 54 per cent in 2015, and 72·7 per cent in 2018). Funding disclosure was present in 55·0, 65·0, 69·4 and 75·4 per cent of manuscripts respectively. Conflict of interest disclosure was present in 49·5, 89·1, 94·6 and 98·3 per cent of manuscripts across the same time periods. Data-sharing statements were present in only 15 RCTs (3·2 per cent), 11 of which were published in 2018.

CONCLUSION

Trial registration, disclosure of funding and disclosure of investigator conflicts in surgical RCTs have improved markedly over the past 10 years. Disclosure of data-sharing plans is exceptionally low. This may contribute to research waste and represents a target for improvement.

Outcome measures in multimodal rectal cancer trials

There is a large variability regarding the definition and choice of primary endpoints in phase 2 and phase 3 multimodal rectal cancer trials, resulting in inconsistency and difficulty of data interpretation. Also, surrogate properties of early and intermediate endpoints have not been systematically assessed. We provide a comprehensive review of clinical and surrogate endpoints used in trials for non-metastatic rectal cancer. The applicability, advantages, and disadvantages of these endpoints are summarised, with recommendations on clinical endpoints for the different phase trials, including limited surgery or non-operative management for organ preservation. We discuss how early and intermediate endpoints, including patient-reported outcomes and involvement of patients in decision making, can be used to guide trial design and facilitate consistency in reporting trial results in rectal cancer.

Digitizing the Pharma Neurons - A Technological Operation in Progress!

BACKGROUND

Digitization and automation are the buzzwords in clinical research and pharma companies are investigating heavily here. Right from drug discovery to personalized medicine, digital patients and patient engagement, there is great consideration of technology at each step.

METHODS

The published data and online information available is reviewed to give an overview of digitization in pharma, across the drug development cycle, industry collaborations and innovations. The regulatory guidelines, innovative collaborations across industry, academics and thought leadership are presented. Also included are some ideas, suggestions, way forwards while digitizing the pharma neurons, the regulatory stand, benefits and challenges.

RESULTS

The innovations range from discovering personalized medicine to conducting virtual clinical trials, and maximizing data collection from the real-world experience. To address the increasing demand for the real-world data and the needs of tech-savvy patients, the innovations are shaping up accordingly. Pharma companies are collaborating with academics and they are co-innovating the technology for example Massachusetts Institute of Technology's program. This focuses on the modernization of clinical trials, strategic use of artificial intelligence and machine learning using real-world evidence, assess the risk-benefit ratio of deploying digital analytics in medicine, and proactively identifying the solutions.

CONCLUSION

With unfolding data on the impact of science and technology amalgamation, we need shared mindset between data scientists and medical professionals to maximize the utility of enormous health and medical data. To tackle this efficiently, there is a need of cross-collaboration and education, and align with ethical and regulatory requirements. A perfect blend of industry, regulatory, and academia will ensure successful digitization of pharma neurons.

Obtaining and managing data sets for individual participant data meta-analysis: scoping review and practical guide

BACKGROUND

Shifts in data sharing policy have increased researchers' access to individual participant data (IPD) from clinical studies. Simultaneously the number of IPD meta-analyses (IPDMAs) is increasing. However, rates of data retrieval have not improved. Our goal was to describe the challenges of retrieving IPD for an IPDMA and provide practical guidance on obtaining and managing datasets based on a review of the literature and practical examples and observations.

METHODS

We systematically searched MEDLINE, Embase, and the Cochrane Library, until January 2019, to identify publications focused on strategies to obtain IPD. In addition, we searched pharmaceutical websites and contacted industry organizations for supplemental information pertaining to recent advances in industry policy and practice. Finally, we documented setbacks and solutions encountered while completing a comprehensive IPDMA and drew on previous experiences related to seeking and using IPD.

RESULTS

Our scoping review identified 16 articles directly relevant for the conduct of IPDMAs. We present short descriptions of these articles alongside overviews of IPD sharing policies and procedures of pharmaceutical companies which display certification of Principles for Responsible Clinical Trial Data Sharing via Pharmaceutical Research and Manufacturers of America or European Federation of Pharmaceutical Industries and Associations websites. Advances in data sharing policy and practice affected the way in which data is requested, obtained, stored and analyzed. For our IPDMA it took 6.5 years to collect and analyze relevant IPD and navigate additional administrative barriers. Delays in obtaining data were largely due to challenges in communication with study sponsors, frequent changes in data sharing policies of study sponsors, and the requirement for a diverse skillset related to research, administrative, statistical and legal issues.

CONCLUSIONS

Knowledge of current data sharing practices and platforms as well as anticipation of necessary tasks and potential obstacles may reduce time and resources required for obtaining and managing data for an IPDMA. Sufficient project funding and timeline flexibility are pre-requisites for successful collection and analysis of IPD. IPDMA researchers must acknowledge the additional and unexpected responsibility they are placing on corresponding study authors or data sharing administrators and should offer assistance in readying data for sharing.

Is useful research data usually shared? An investigation of genome-wide association study summary statistics

Primary data collected during a research study is often shared and may be reused for new studies. To assess the extent of data sharing in favourable circumstances and whether data sharing checks can be automated, this article investigates summary statistics from primary human genome-wide association studies (GWAS). This type of data is highly suitable for sharing because it is a standard research output, is straightforward to use in future studies (e.g., for secondary analysis), and may be already stored in a standard format for internal sharing within multi-site research projects. Manual checks of 1799 articles from 2010 and 2017 matching a simple PubMed query for molecular epidemiology GWAS were used to identify 314 primary human GWAS papers. Of these, only 13% reported the location of a complete set of GWAS summary data, increasing from 3% in 2010 to 23% in 2017. Whilst information about whether data was shared was typically located clearly within a data availability statement, the exact nature of the shared data was usually unspecified. Thus, data sharing is the exception even in suitable research fields with relatively strong data sharing norms. Moreover, the lack of clear data descriptions within data sharing statements greatly complicates the task of automatically characterising shared data sets.

Reproducible research practices, openness and transparency in health economic evaluations: study protocol for a cross-sectional comparative analysis

INTRODUCTION

There has been a growing awareness of the need for rigorously and transparent reported health research, to ensure the reproducibility of studies by future researchers. Health economic evaluations, the comparative analysis of alternative interventions in terms of their costs and consequences, have been promoted as an important tool to inform decision-making. The objective of this study will be to investigate the extent to which articles of economic evaluations of healthcare interventions indexed in MEDLINE incorporate research practices that promote transparency, openness and reproducibility.

METHODS AND ANALYSIS

This is the study protocol for a cross-sectional comparative analysis. We registered the study protocol within the Open Science Framework (osf.io/gzaxr). We will evaluate a random sample of 600 cost-effectiveness analysis publications, a specific form of health economic evaluations, indexed in MEDLINE during 2012 (n=200), 2019 (n=200) and 2022 (n=200). We will include published papers written in English reporting an incremental cost-effectiveness ratio in terms of costs per life years gained, quality-adjusted life years and/or disability-adjusted life years. Screening and selection of articles will be conducted by at least two researchers. Reproducible research practices, openness and transparency in each article will be extracted using a standardised data extraction form by multiple researchers, with a 33% random sample (n=200) extracted in duplicate. Information on general, methodological and reproducibility items will be reported, stratified by year, citation of the Consolidated Health Economic Evaluation Reporting Standards (CHEERS) statement and journal. Risk ratios with 95% CIs will be calculated to represent changes in reporting between 2012-2019 and 2019-2022.

ETHICS AND DISSEMINATION

Due to the nature of the proposed study, no ethical approval will be required. All data will be deposited in a cross-disciplinary public repository. It is anticipated the study findings could be relevant to a variety of audiences. Study findings will be disseminated at scientific conferences and published in peer-reviewed journals.

Transparency in Decision Modelling: What, Why, Who and How?

Transparency in decision modelling is an evolving concept. Recently, discussion has moved from reporting standards to open-source implementation of decision analytic models. However, in the debate about the supposed advantages and disadvantages of greater transparency, there is a lack of definition. The purpose of this article is not to present a case for or against transparency, but rather to provide a more nuanced understanding of what transparency means in the context of decision modelling and how it could be addressed. To this end, we review and summarise the discourse to date, drawing on our collective experience. We outline a taxonomy of the different manifestations of transparency, including reporting standards, reference models, collaboration, model registration, peer review and open-source modelling. Further, we map out the role and incentives for the various stakeholders, including industry, research organisations, publishers and decision makers. We outline the anticipated advantages and disadvantages of greater transparency with respect to each manifestation, as well as the perceived barriers and facilitators to greater transparency. These are considered with respect to the different stakeholders and with reference to issues including intellectual property, legality, standards, quality assurance, code integrity, health technology assessment processes, incentives, funding, software, access and deployment options, data protection and stakeholder engagement. For each manifestation of transparency, we discuss the 'what', 'why', 'who' and 'how'. Specifically, their meaning, why the community might (or might not) wish to embrace them, whose engagement as stakeholders is required and how relevant objectives might be realised. We identify current initiatives aimed to improve transparency to exemplify efforts in current practice and for the future.

ePCR: an R-package for survival and time-to-event prediction in advanced prostate cancer, applied to real-world patient cohorts

Motivation

Prognostic models are widely used in clinical decision-making, such as risk stratification and tailoring treatment strategies, with the aim to improve patient outcomes while reducing overall healthcare costs. While prognostic models have been adopted into clinical use, benchmarking their performance has been difficult due to lack of open clinical datasets. The recent DREAM 9.5 Prostate Cancer Challenge carried out an extensive benchmarking of prognostic models for metastatic Castration-Resistant Prostate Cancer (mCRPC), based on multiple cohorts of open clinical trial data.

Results

We make available an open-source implementation of the top-performing model, ePCR, along with an extended toolbox for its further re-use and development, and demonstrate how to best apply the implemented model to real-world data cohorts of advanced prostate cancer patients.

Availability and implementation

The open-source R-package ePCR and its reference documentation are available at the Central R Archive Network (CRAN): https://CRAN.R-project.org/package=ePCR. R-vignette provides step-by-step examples for the ePCR usage.

Supplementary information

Supplementary data are available at Bioinformatics online.

Artificial intelligence and machine learning in clinical development: a translational perspective

Future of clinical development is on the verge of a major transformation due to convergence of large new digital data sources, computing power to identify clinically meaningful patterns in the data using efficient artificial intelligence and machine-learning algorithms, and regulators embracing this change through new collaborations. This perspective summarizes insights, recent developments, and recommendations for infusing actionable computational evidence into clinical development and health care from academy, biotechnology industry, nonprofit foundations, regulators, and technology corporations. Analysis and learning from publically available biomedical and clinical trial data sets, real-world evidence from sensors, and health records by machine-learning architectures are discussed. Strategies for modernizing the clinical development process by integration of AI- and ML-based digital methods and secure computing technologies through recently announced regulatory pathways at the United States Food and Drug Administration are outlined. We conclude by discussing applications and impact of digital algorithmic evidence to improve medical care for patients.

Design and Evaluation of an External Control Arm Using Prior Clinical Trials and Real-World Data

PURPOSE

We discuss designs and interpretable metrics of bias and statistical efficiency of "externally controlled" trials (ECT) and compare ECT performance to randomized and single-arm designs.

EXPERIMENTAL DESIGN

We specify an ECT design that leverages information from real-world data (RWD) and prior clinical trials to reduce bias associated with interstudy variations of the enrolled populations. We then used a collection of clinical studies in glioblastoma (GBM) and RWD from patients treated with the current standard of care to evaluate ECTs. Validation is based on a "leave one out" scheme, with iterative selection of a single-arm from one of the studies, for which we estimate treatment effects using the remaining studies as external control. This produces interpretable and robust estimates on ECT bias and type I errors.

RESULTS

We developed a model-free approach to evaluate ECTs based on collections of clinical trials and RWD. For GBM, we verified that inflated false positive error rates of standard single-arm trials can be considerably reduced (up to 30%) by using external control data.

CONCLUSIONS

The use of ECT designs in GBM, with adjustments for the clinical profiles of the enrolled patients, should be preferred to single-arm studies with fixed efficacy thresholds extracted from published results on the current standard of care.

Landscape of Cardiovascular Device Registries in the United States

Background Regulators increasingly rely on registries for decision making related to high-risk medical devices in the United States. However, the limited uniform standards for registries may create substantial variability in registry implementation and utility to regulators. We surveyed the current landscape of US cardiovascular device registries and chart the extent of inconsistency in goals, administration, enrollment procedures, and approach to data access. Methods and Results A systematic review using Preferred Reporting Items for Systematic Reviews and Meta-Analysis (PRISMA) guidelines identified studies (1995-2017) referencing cardiovascular device registries with a US-based institution. Registries were then evaluated by reviewing associated articles and websites. Extracted data included device type, primary scientific aim(s), funding, stewardship (eg, administration of registry procedures), enrollment procedures, informed consent process, and mechanisms to access data for research. The 138 cardiovascular device registries in the cohort covered devices addressing interventional cardiology (65.9%), arrhythmias (15.2%), heart failure (10.1%), and valvular disease (10.1%). While the majority (55.8%) were industry-funded, stewardship was predominantly overseen by academic centers (74.0%). Most registry participation was voluntary (77.5%), but a substantial minority (19.7%) were required as a condition of device implantation. Informed consent requirements varied widely, with written consent required in only 55.1% of registries. Registry data were primarily accessible only to stewards (84.1%), with 13.8% providing pathways for external applications. Conclusions The majority of cardiovascular device registries were funded privately under the auspices of academic institutions, which set the rules for data access. The substantial variation between cardiovascular device registries suggests a role for regulators to further strengthen guidelines to improve quality, consistency, and ethical standards.

Big data in IBD: a look into the future

Big data methodologies, made possible with the increasing generation and availability of digital data and enhanced analytical capabilities, have produced new insights to improve outcomes in many disciplines. Application of big data in the health-care sector is in its early stages, although the potential for leveraging underutilized data to gain a better understanding of disease and improve quality of care is enormous. Owing to the intrinsic characteristics of inflammatory bowel disease (IBD) and the management dilemmas that it imposes, the implementation of big data research strategies not only can complement current research efforts but also could represent the only way to disentangle the complexity of the disease. In this Review, we explore important potential applications of big data in IBD research, including predictive models of disease course and response to therapy, characterization of disease heterogeneity, drug safety and development, precision medicine and cost-effectiveness of care. We also discuss the strengths and limitations of potential data sources that big data analytics could draw from in the field of IBD, including electronic health records, clinical trial data, e-health applications and genomic, transcriptomic, proteomic, metabolomic and microbiomic data.

Dosage adjustments in pivotal clinical trials with oral targeted therapies in solid tumors conducted in Europe

PURPOSE

The aim of this study was to evaluate in what measure is dosage adjustment particularly prevalent in pivotal clinical trials of oral targeted therapy drugs approved by the European Medicine Agency as of July 31, 2018, for the treatment of solid tumors.

METHODS

We performed a search on the official EMA site on human medicines, using as Keyword Search the ATC Code L01X (other antineoplastic agents); from the list of drugs results, we subsequently excluded antineoplastic drugs for hematological diseases, as well as refused and withdrawn drugs. For all analyzed drugs, we recorded full dosages, dose adjustments with relative reduction percentage, reason for the adjustments, number of patients included in the trial, percentage of patients who reduced their dosage or temporarily discontinued therapy, cause of dose reduction, and presence or absence of reference to a clinical outcome in patients who reduced their dose or discontinued therapy.

RESULTS

We considered 74 pivotal trials on 29 target therapies, of which 56 (76%) provide information on dosage reduction, 41 (55%) on therapy suspension, and 29 (39%) on the dose taken by the sample. Trials that provide information on dosage adjustment include reduction and suspension data widely used to manage side effects; they concern, respectively, 32 and 44% of the samples considered. No trial results take account of the possible role of adjustment in clinical outcomes.

CONCLUSION

It would be advisable for pivotal clinical trials to give more relevance to dose management, which is a widely used tool for the management of adverse events in clinical practice. To date, such information is lacking.

Diagnostic Assessment of Assumptions for External Validity: An Example Using Data in Metastatic Colorectal Cancer

BACKGROUND

Methods developed to estimate intervention effects in external target populations assume that all important effect measure modifiers have been identified and appropriately modeled. Propensity score-based diagnostics can be used to assess the plausibility of these assumptions for weighting methods.

METHODS

We demonstrate the use of these diagnostics when assessing the transportability of treatment effects from the standard of care for metastatic colorectal cancer control arm in a phase III trial (HORIZON III) to a target population of 1,942 Medicare beneficiaries age 65+ years.

RESULTS

In an unadjusted comparison, control arm participants had lower mortality compared with target population patients treated with the standard of care therapy (trial vs. target hazard ratio [HR] = 0.72, 95% confidence interval [CI], 0.58, 0.89). Applying inverse odds of sampling weights attenuated the trial versus target HR (weighted HR = 0.96, 95% CI = 0.73, 1.26). However, whether unadjusted or weighted, hazards did not appear proportional. At 6 months of follow-up, mortality was lower in the weighted trial population than the target population (weighted trial vs. target risk difference [RD] = -0.07, 95% CI = -0.13, -0.01), but not at 12 months (weighted RD = 0.00, 95% CI = -0.09, 0.09).

CONCLUSION

These diagnostics suggest that direct transport of treatment effects from HORIZON III to the Medicare population is not valid. However, the proposed sampling model might allow valid transport of the treatment effects on longer-term mortality from HORIZON III to the Medicare population treated in clinical practice. See video abstract at, http://links.lww.com/EDE/B435.

Trends in the characteristics of human functional genomic data on the gene expression omnibus, 2001-2017

The gene expression omnibus (GEO) is the world's largest public repository of functional genomic data. Despite its broad use in secondary genomic analyses, the temporal trends in the characteristics of genomic data on GEO, including experimental procedures, geographic origin, funder(s), and related disease, have not been examined. We identified 75,376 Series deposited to the GEO during 2001-2017 and built a database of all human genomic data (39,076 Series, 51.8% of all Series). Using the associated publications, we obtained funding information and identified the related disease area. Of the Series with classified disease areas, the two most common were cancer (n = 12,688, 32.5%) and immunologic diseases (n = 2,393, 6.1%), while the percentages of all other disease areas were below 5%, including neurological diseases (n = 1733, 4.4%), infectious diseases (n = 1225, 3.1%), diabetes (n = 828, 2.1%), and cardiovascular diseases (n = 299, 0.8%). In recent years, there has been a significant increase in the use of high-throughput sequencing (HTS), protein array and multiple-platform technologies, as well as in the proportion of North American deposits. Compared to those from other regions, North American deposits appeared to lead the shift from array-based to HTS technologies (odds ratio [OR], 95% confidence intervals [CI] = 3.39, 3.23-3.55, P = 9.40E-323), and were less likely to focus on a major disease area (OR = 0.64, 95% CI: 0.61-0.67, P = 5.02E-107), suggesting a greater emphasis on basic science in North America. Furthermore, the Series utilizing HTS were less likely to be disease-classified compared to other technologies (OR = 0.39, 95% CI: 0.37-0.41, P = 1.00E-322), suggesting a preferential use or adoption of HTS in basic science settings. Finally, funding from the NHGRI, NCI, NIEHS, and NCCR resulted in a higher number of GEO Series per grant than other NIH institutes, demonstrating different preferences on genomic studies among awardees of NIH institutes. Our findings demonstrate geographic, technological, and funding disparities in the trends of GEO deposit characteristics.

Ten quick tips for sharing open genomic data

As sequencing prices drop, genomic data accumulates—seemingly at a steadily increasing pace. Most genomic data potentially have value beyond the initial purpose—but only if shared with the scientific community. This, of course, is often easier said than done. Some of the challenges in sharing genomic data include data volume (raw file sizes and number of files), complexities, formats, nomenclatures, metadata descriptions, and the choice of a repository. In this paper, we describe 10 quick tips for sharing open genomic data.

Research integrity and the law that never was

Evidence-based medicine is arguably among the most important innovations of the modern era, but publication bias and inadequate research transparency are serious issues affecting the very foundation of evidence-based practice. Despite this truth, these crucial issues have gone largely unaddressed or inadequately addressed for a distressingly long period of time. Regulatory efforts have thus far proven insufficient in eliminating these issues. Fortunately, the last 5 years in particular have seen developments that one hopes will contribute to the eradication of these issues and a future where we can look back on these issues as a sordid story of our past. However, much like the purported fixes of the past, time will be the final arbiter of the efficacy of remedial measures currently underway. This article chronicles the history of these issues, failed attempts to fix these issues, and what can be and is being done with the hope of bringing about true resolution.

Myocarditis Associated with Immune Checkpoint Inhibitors: An Expert Consensus on Data Gaps and a Call to Action

Immune checkpoint inhibitors (ICIs) have transformed the treatment landscape for cancer. Due to the mechanism of action of ICIs, inflammatory reactions against normal tissue were an anticipated side effect of these agents; these immune-related adverse events have been documented and are typically low grade and manageable. Myocarditis has emerged as an uncommon but potentially life-threatening adverse reaction in patients treated with ICIs. Assessment and characterization of ICI-associated myocarditis is challenging because of its low incidence and protean manifestations. Nevertheless, the seriousness of ICI-associated myocarditis justifies a coordinated effort to increase awareness of this syndrome, identify patients who may be at risk, and enable early diagnosis and appropriate treatment. The "Checkpoint Inhibitor Safety Working Group," a multidisciplinary committee of academic, industry, and regulatory partners, convened at a workshop hosted by Project Data Sphere, LLC, on December 15, 2017. This meeting aimed to evaluate the current information on ICI-associated myocarditis, determine methods to collect and share data on this adverse reaction, and establish task forces to close the identified knowledge gaps. In this report, we summarize the workshop findings and proposed steps to address the impact of ICI-associated myocarditis in patients with cancer.

Artificial intelligence in radiation oncology: A specialty-wide disruptive transformation?

Artificial intelligence (AI) is emerging as a technology with the power to transform established industries, and with applications from automated manufacturing to advertising and facial recognition to fully autonomous transportation. Advances in each of these domains have led some to call AI the "fourth" industrial revolution [1]. In healthcare, AI is emerging as both a productive and disruptive force across many disciplines. This is perhaps most evident in Diagnostic Radiology and Pathology, specialties largely built around the processing and complex interpretation of medical images, where the role of AI is increasingly seen as both a boon and a threat. In Radiation Oncology as well, AI seems poised to reshape the specialty in significant ways, though the impact of AI has been relatively limited at present, and may rightly seem more distant to many, given the predominantly interpersonal and complex interventional nature of the specialty. In this overview, we will explore the current state and anticipated future impact of AI on Radiation Oncology, in detail, focusing on key topics from multiple stakeholder perspectives, as well as the role our specialty may play in helping to shape the future of AI within the larger spectrum of medicine.