Data Sharing, Year 1 — Access to Data from Industry-Sponsored Clinical Trials

Strom, Brian L.; Buyse, Marc; Hughes, John; Knoppers, Bartha Maria

doi:10.1056/nejmp1411794

Cited by 101 publications

(77 citation statements)

References 4 publications

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“…The latter issue has to date not turned out to be as big an issue as once feared [20,21]. Many of the other concerns cannot be totally eliminated, but they can be mitigated and managed.…”

Section: Issues With Data Access and Sharingmentioning

confidence: 99%

“…In spite of, or perhaps because of the debate, a number of major initiatives have been launched over the past 3-4 years to move the field of data sharing in the clinical research sector forward. Project Data Sphere [23,24], CSDR [21,25], Yale University Open Data Access (YODA) [26], Supporting Open Access for Researchers (SOAR) [27], to name a few. The last three are collections of individual patient data from trials receive from 13, 2, and 1 commercial sponsors, respectively.…”

Section: Data Sharing Systemsmentioning

confidence: 99%

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Statistical controversies in clinical research: data access and sharing—can we be more transparent about clinical research? Let’s do what’s right for patients

Rockhold

2017

Annals of Oncology

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Calls for greater transparency and 'open data access' in clinical research are widespread, from sources including the Executive Office of the President, which in 2013 called for increased access to the results of federally funded research. In 2015, The Institute of Medicine issued a report advocating for a multi-stakeholder effort to foster responsible data sharing, and there are many others. Open science is good for researchers, good for innovation, and good for patients. The question at the center of the open-science efforts for clinical trials should not be whether data should be shared, but rather how we can usher in responsible methods for doing so. Unfortunately, there remain numerous perceived barriers to complete transparency around clinical trial data. This paper reviews the current status of data disclosure, the barriers to achieving it and a suggestion for the future.

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“…The latter issue has to date not turned out to be as big an issue as once feared [20,21]. Many of the other concerns cannot be totally eliminated, but they can be mitigated and managed.…”

Section: Issues With Data Access and Sharingmentioning

confidence: 99%

Section: Data Sharing Systemsmentioning

confidence: 99%

Statistical controversies in clinical research: data access and sharing—can we be more transparent about clinical research? Let’s do what’s right for patients

Rockhold

2017

Annals of Oncology

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“…from RCTs (Bertagnolli et al, 2017;Strom et al, 2014;Taichman et al, 2016), methods for AD are important for a number of reasons. Firstly, it is uncertain whether IPD will ever be accessible for historic clinical trials, to which new journal requirements or voluntary disclosure commitments do not apply.…”

Section: Introductionmentioning

confidence: 99%

“…Firstly, it is uncertain whether IPD will ever be accessible for historic clinical trials, to which new journal requirements or voluntary disclosure commitments do not apply. Secondly, many sponsors offer access to IPD via a secure server, from which data cannot be downloaded (Strom et al, 2014) so that an analysis cannot be simultaneously performed across RCTs from different sponsors. Finally, competitors are excluded from access to individual patient data (IPD) by most pharmaceutical companies.…”

Section: Introductionmentioning

confidence: 99%

Meta-analysis of aggregate data on medical events

Holzhauer

2016

Statist. Med.

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A meta-analysis combines analysis results from multiple independent sets of data. Metaanalyses to assess whether a treatment affects the occurrence of a medical event are frequently based on published aggregate data from controlled clinical trials. In that setting, the number of patients with an event in each trial is often treated as a binomial random variable. This assumes that censoring times have the same distribution in all treatment groups of a trial, and are independent of event times.To allow for different drop-out time distributions across treatment groups, we derive a likelihood for commonly available aggregate data that assumes specific event and drop-out time distributions for a number of situations. These include exponentially or Weibull distributed event and drop-out times, event-driven trials, the situation when a patient may experience multiple potentially fatal events, and when individual patient data are available for some trials.The assumption that parameters of survival distributions are exchangeable between trials is more plausible than for the expected proportion of patients with an event.For this reason the proposed likelihood is more suitable than a binomial likelihood for use in hierarchical meta-analysis models and for incorporating prior information from historical control group data. Hierarchical models and prior information are useful in sparse data settings and to avoid parameter identifiability problems. We use simulations to compare hierarchical Bayesian models with the proposed trial-level likelihood against other meta-analysis methods and to compare methods for using historical control group data. We also demonstrate how conjugate priors may be used to analyze exponentially distributed failure times without the need for Markov chain Monte Carlo methods.

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“…The Institute of Medicine explicitly recommends data sharing, although with adoption of specific rules to guarantee a fair opportunity for researchers to publish results before secondary investigators gain access to the data, to protect the commercial interests of sponsors and to avoid privacy concerns and inappropriate use [8]. Some large pharmaceutical companies are already sharing clinical trial data [9], and the European Medicines Agency has started doing so this year [8]. Making the analyzable data set available to researchers avoids unnecessarily duplicating trials and allows meta-analyses, applications of innovative statistical methods, novel new analyses, cross-study comparisons, sample size estimation for new studies, and replication of results.…”

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confidence: 99%

Making advances in delirium research: coupling delirium outcomes research and data sharing

Salluh

Latronico

2015

Intensive Care Med

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Understanding and improving the current management of delirium in critically ill patients has become a major challenge for clinicians at the bedside and researchers. In the past decade significant advances have been made increasing our knowledge on the epidemiology, clinical phenotype, diagnosis, prevention, treatment, and risk assessment of delirium [1]. As clinicians in the intensive care unit (ICU), being able to predict the occurrence of delirium early in the course of critical illness may be extremely useful for the implementation of preventive measures or to design interventions for preemptive treatment of high-risk individuals. In a recent study Wassenaar et al. [2] demonstrated the ability to predict delirium with a relatively straightforward model for implementation at ICU admission.Before publishing the E-PRE-DELIRIC, the authors planned and realized a series of rigorous studies. In their first study, the authors developed the PREdiction of DELIRium for Intensive Care patients (PRE-DELIRIC) model for prediction of delirium based on variables readily available in the first 24 h after ICU admission. The authors then temporally validated it in a second prospective cohort in the same hospital and finally validated it externally in four other Dutch hospitals. Prevalence of delirium varied between 25.5 and 36.8 %. Risk factors were selected on the basis of a systematic review [3]. Of the 25 potential risk factors, ten were retained (Table 1). The area under receiver operating characteristics curve (AUROC) of pooled data varied for development (AUROC = 0.86), temporal validation (AUROC = 0.89), and external validation (AUROC = 0.84), resulting in an AUROC of 0.85.In a second study [4], the PRE-DELIRIC was recalibrated in eight ICUs from six countries; the median delirium incidence was 22.5 % (IQR 12.8-36.6 %). The incidence of the ten predictors differed significantly between centers, but the AUROC of the eight participating centers remained good at 0.77 [95 % confidence interval (CI) 0.74-0.79].In this third prospective cohort study in 13 ICUs from seven countries [2], the authors enrolled 2914 patients to develop and validate a predictive model for ICU delirium based on data available at ICU admission. Multiple logistic regression analysis was used to develop the model on data of the first two-thirds and validated on data of the last one-third of the patients from every participating ICU. The incidence of delirium was 24.5 % in the development cohort (1962 patients), 21.8 % in the validation cohort (952 patients), and 23.6 % in the entire study population. The AUROC was 0.76 (95 % CI

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Data Sharing, Year 1 — Access to Data from Industry-Sponsored Clinical Trials

Cited by 101 publications

References 4 publications

Statistical controversies in clinical research: data access and sharing—can we be more transparent about clinical research? Let’s do what’s right for patients

Statistical controversies in clinical research: data access and sharing—can we be more transparent about clinical research? Let’s do what’s right for patients

Meta-analysis of aggregate data on medical events

Making advances in delirium research: coupling delirium outcomes research and data sharing

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