Project data sphere to make cancer clinical trial data publicly available

The Costs of Anonymization: Case Study Using Clinical Data

BACKGROUND

Sharing data from clinical studies can accelerate scientific progress, improve transparency, and increase the potential for innovation and collaboration. However, privacy concerns remain a barrier to data sharing. Certain concerns, such as reidentification risk, can be addressed through the application of anonymization algorithms, whereby data are altered so that it is no longer reasonably related to a person. Yet, such alterations have the potential to influence the data set's statistical properties, such that the privacy-utility trade-off must be considered. This has been studied in theory, but evidence based on real-world individual-level clinical data is rare, and anonymization has not broadly been adopted in clinical practice.

OBJECTIVE

The goal of this study is to contribute to a better understanding of anonymization in the real world by comprehensively evaluating the privacy-utility trade-off of differently anonymized data using data and scientific results from the German Chronic Kidney Disease (GCKD) study.

METHODS

The GCKD data set extracted for this study consists of 5217 records and 70 variables. A 2-step procedure was followed to determine which variables constituted reidentification risks. To capture a large portion of the risk-utility space, we decided on risk thresholds ranging from 0.02 to 1. The data were then transformed via generalization and suppression, and the anonymization process was varied using a generic and a use case-specific configuration. To assess the utility of the anonymized GCKD data, general-purpose metrics (ie, data granularity and entropy), as well as use case-specific metrics (ie, reproducibility), were applied. Reproducibility was assessed by measuring the overlap of the 95% CI lengths between anonymized and original results.

RESULTS

Reproducibility measured by 95% CI overlap was higher than utility obtained from general-purpose metrics. For example, granularity varied between 68.2% and 87.6%, and entropy varied between 25.5% and 46.2%, whereas the average 95% CI overlap was above 90% for all risk thresholds applied. A nonoverlapping 95% CI was detected in 6 estimates across all analyses, but the overwhelming majority of estimates exhibited an overlap over 50%. The use case-specific configuration outperformed the generic one in terms of actual utility (ie, reproducibility) at the same level of privacy.

CONCLUSIONS

Our results illustrate the challenges that anonymization faces when aiming to support multiple likely and possibly competing uses, while use case-specific anonymization can provide greater utility. This aspect should be taken into account when evaluating the associated costs of anonymized data and attempting to maintain sufficiently high levels of privacy for anonymized data.

TRIAL REGISTRATION

German Clinical Trials Register DRKS00003971; https://drks.de/search/en/trial/DRKS00003971.

INTERNATIONAL REGISTERED REPORT IDENTIFIER (IRRID)

RR2-10.1093/ndt/gfr456.

Quality of life outcomes for patients with metastatic castration-resistant prostate cancer and pretreatment prognostic score

BACKGROUND

A prognostic risk score (Halabi score) in metastatic castration-resistant prostate cancer (mCRPC) accurately predicts overall survival, but its association with quality of life (QOL) has not been defined. We hypothesize that a higher pretreatment Halabi score is associated with worse QOL outcomes over time in mCRPC patients.

METHODS

Patient-level data from the docetaxel plus prednisone control arm of Mainsail, a Phase 3 clinical trial in mCRPC were accessed via ProjectDataSphere. Pretreatment Halabi score included disease-related factors: metastatic site, opioid use, Eastern Cooperative Oncology Group performance status (ECOG-PS), alkaline phosphatase, albumin, hemoglobin, lactic acid dehydrogenase, and PSA, with higher score indicating worse survival. Three QOL scales were created: Functional Assessment of Cancer Therapy-Prostate (FACT-P, higher score = better QOL), Brief Pain Inventory-Short Form Severity score (BPI-SFSS, higher score = higher pain severity), and BPI-SF Interference score (BPI-SFIS, higher score = greater pain interference). Mixed linear model was used to estimate the associations between Halabi score and QOL scores assessed at different time points (baseline, 2 months, and 6 months).

RESULTS

This analysis included 412 mCRPC patients (median age = 68 years, 82% white, 5% Black, median log PSA = 4.4 ng/mL). After multivariable adjustment, Halabi score was significantly associated with QOL scores at all time points. At 6 months, multivariable adjusted FACT-P decreased by 10.0 points (worsening), BPI-SFSS increased by 0.8 points (worsening), and BPI-SFIS increased by 0.9 points (worsening) for each unit increase in Halabi risk score. In multivariable analysis of individual components, ECOG-PS, site of metastasis, and opioid use were significantly associated with worse QOL scores at baseline.

CONCLUSIONS

Chemotherapy-naïve mCRPC patients with poorer Halabi prognostic risk scores have poorer QOL and greater pain intensity and interference at baseline and during follow-up.

Cancer patient survival can be parametrized to improve trial precision and reveal time-dependent therapeutic effects

Individual participant data (IPD) from oncology clinical trials is invaluable for identifying factors that influence trial success and failure, improving trial design and interpretation, and comparing pre-clinical studies to clinical outcomes. However, the IPD used to generate published survival curves are not generally publicly available. We impute survival IPD from ~500 arms of Phase 3 oncology trials (representing ~220,000 events) and find that they are well fit by a two-parameter Weibull distribution. Use of Weibull functions with overall survival significantly increases the precision of small arms typical of early phase trials: analysis of a 50-patient trial arm using parametric forms is as precise as traditional, non-parametric analysis of a 90-patient arm. We also show that frequent deviations from the Cox proportional hazards assumption, particularly in trials of immune checkpoint inhibitors, arise from time-dependent therapeutic effects. Trial duration therefore has an underappreciated impact on the likelihood of success.

Analysis of more than 150 Phase 3 oncology clinical trials supports parametric statistical analysis, significantly increasing the precision of small early-phase trials and relating deviations from the Cox proportional hazards model to trial duration.

Trials number, funding support, and intervention type associated with IPDMA data retrieval: a cross-sectional study

OBJECTIVES

This study aimed to investigate the predictors for high data retrieval and the reporting of individual participant data meta-analyses (IPDMAs).

STUDY DESIGN AND SETTING

We searched EMBASE, MEDLINE, and the Cochrane Library for articles pertaining to IPDMA from 2011 to 2019. Only IPDMA assessing treatment effects, including randomized controlled trials (RCTs), were included. Adherence to the PRISMA-IPD guideline was checked.

RESULTS

A total of 210 IPDMA covering 18 diseases were sampled; 80 (38.1%) and 123 (58.6%) of the IPDMA retrieved IPD from all and ≥80% RCTs, respectively. Non-Cochrane reviews, IPDMA on nonpharmacological interventions, analyses of smaller numbers of RCTs, and having funding supports were predictors of complete IPD retrieval. Owners of RCTs had an increased probability of obtaining IPD. Only 4.3% described the eligibility criteria covering all the PICO components, 11.0% reported the methods for assessing risk of bias across studies, 11.4% mentioned the IPD integrity, and 9.0% presented detailed results of syntheses.

CONCLUSION

IPD retrieval and reporting was not satisfactory among the published IPDMA. Encouraging RCT owners to conduct or join in the IPDMA is a potential strategy to maximize the IPD retrieval. IPDMA are suggested to adhere to the PRISMA-IPD guideline during reporting.

Artificial Intelligence and Mechanistic Modeling for Clinical Decision Making in Oncology

The amount of "big" data generated in clinical oncology, whether from molecular, imaging, pharmacological, or biological origin, brings novel challenges. To mine efficiently this source of information, mathematical models able to produce predictive algorithms and simulations are required, with applications for diagnosis, prognosis, drug development, or prediction of the response to therapy. Such mathematical and computational constructs can be subdivided into two broad classes: biologically agnostic, statistical models using artificial intelligence techniques, and physiologically based, mechanistic models. In this review, recent advances in the applications of such methods in clinical oncology are outlined. These include machine learning applied to big data (omics, imaging, or electronic health records), pharmacometrics and quantitative systems pharmacology, as well as tumor kinetics and metastasis modeling. Focus is set on studies with high potential of clinical translation, and particular attention is given to cancer immunotherapy. Perspectives are given in terms of combinations of the two approaches: "mechanistic learning."

Antibacterial Use Is Associated with an Increased Risk of Hematologic and Gastrointestinal Adverse Events in Patients Treated with Gemcitabine for Stage IV Pancreatic Cancer

BACKGROUND

Preclinical evidence has demonstrated that common intratumor bacteria metabolize the chemotherapeutic drug gemcitabine. The significance of this bacterial metabolism pathway, relative to the known metabolic pathways by host enzymes, is not known. We hypothesized that bacterial metabolism is clinically significant and that "knockdown" by antibacterial therapy has the unintended effect of increasing the effective dose of gemcitabine, thereby increasing the risk for gemcitabine-associated toxicities.

MATERIALS AND METHODS

We reanalyzed the comparator arm of the MPACT trial (NCT01442974), made available through Project Data Sphere, LLC (CEO Roundtable on Cancer's Life Sciences Consortium, Cary, NC; www.projectdatasphere.org). In this arm, 430 patients with metastatic pancreatic adenocarcinoma were treated with gemcitabine. We used the Anderson-Gill survival model to compare the risk of developing an adverse event after antibacterial prescription with time unexposed to antibacterials. Adverse events of grade 3 and greater were considered at three levels of granularity: all aggregated into one endpoint, aggregated by class, and taken individually. Antibiotic exposures were analyzed in aggregate as well as by class.

RESULTS

Antibacterial exposure was associated with an increased risk of adverse events (hazard ratio [HR]: 1.77; confidence interval [CI]: 1.46-2.14), any hematologic adverse event (HR: 1.64; CI: 1.26-2.13), and any gastrointestinal adverse event (HR: 2.14; CI: 1.12-4.10) but not a constitutional (HR: 1.33; CI: 0.611-2.90) or hepatologic adverse event (HR: 0.99; CI: 0.363-2.71). Among specific adverse events, antibacterial exposure was associated with an increased risk of anemia (HR: 3.16; CI: 1.59-6.27), thrombocytopenia (HR: 2.52; CI: 1.31-4.85), leukopenia (HR: 3.91; CI: 1.46-10.5), and neutropenia (HR: 1.53; CI: 1.07-2.17) but not any other specific adverse events.

CONCLUSION

Antibacterial exposure was associated with an increased risk of gemcitabine-associated, dose-limiting adverse events, including aggregate hematologic and gastrointestinal events, as well as four specific hematologic adverse events, suggesting that intratumor bacteria may be responsible for a clinically significant portion of gemcitabine metabolism. Alternative avenues of evidence will be necessary to confirm this preliminary finding and assess its generalizability. There is plentiful opportunity for similar analyses on other clinical trial data sets, where gemcitabine or other biomimetic small molecules were used.

IMPLICATIONS FOR PRACTICE

Patients treated with gemcitabine for metastatic pancreatic ductal adenocarcinoma have an increased rate of gemcitabine-associated toxicity during and after antibiotic therapy. This observation is consistent with preclinical evidence that intratumor bacteria metabolize gemcitabine to an inactive form. Further research is needed to determine whether this observation merits any changes in clinical practice.

Sharing of clinical trial data and results reporting practices among large pharmaceutical companies: cross sectional descriptive study and pilot of a tool to improve company practices

OBJECTIVES

To develop and pilot a tool to measure and improve pharmaceutical companies' clinical trial data sharing policies and practices.

DESIGN

Cross sectional descriptive analysis.

SETTING

Large pharmaceutical companies with novel drugs approved by the US Food and Drug Administration in 2015.

DATA SOURCES

Data sharing measures were adapted from 10 prominent data sharing guidelines from expert bodies and refined through a multi-stakeholder deliberative process engaging patients, industry, academics, regulators, and others. Data sharing practices and policies were assessed using data from ClinicalTrials.gov, Drugs@FDA, corporate websites, data sharing platforms and registries (eg, the Yale Open Data Access (YODA) Project and Clinical Study Data Request (CSDR)), and personal communication with drug companies.

MAIN OUTCOME MEASURES

Company level, multicomponent measure of accessibility of participant level clinical trial data (eg, analysis ready dataset and metadata); drug and trial level measures of registration, results reporting, and publication; company level overall transparency rankings; and feasibility of the measures and ranking tool to improve company data sharing policies and practices.

RESULTS

Only 25% of large pharmaceutical companies fully met the data sharing measure. The median company data sharing score was 63% (interquartile range 58-85%). Given feedback and a chance to improve their policies to meet this measure, three companies made amendments, raising the percentage of companies in full compliance to 33% and the median company data sharing score to 80% (73-100%). The most common reasons companies did not initially satisfy the data sharing measure were failure to share data by the specified deadline (75%) and failure to report the number and outcome of their data requests. Across new drug applications, a median of 100% (interquartile range 91-100%) of trials in patients were registered, 65% (36-96%) reported results, 45% (30-84%) were published, and 95% (69-100%) were publicly available in some form by six months after FDA drug approval. When examining results on the drug level, less than half (42%) of reviewed drugs had results for all their new drug applications trials in patients publicly available in some form by six months after FDA approval.

CONCLUSIONS

It was feasible to develop a tool to measure data sharing policies and practices among large companies and have an impact in improving company practices. Among large companies, 25% made participant level trial data accessible to external investigators for new drug approvals in accordance with the current study's measures; this proportion improved to 33% after applying the ranking tool. Other measures of trial transparency were higher. Some companies, however, have substantial room for improvement on transparency and data sharing of clinical trials.

Resistance models to EGFR inhibition and chemotherapy in non-small cell lung cancer via analysis of tumour size dynamics

Purpose

Imaging time-series data routinely collected in clinical trials are predominantly explored for covariates as covariates for survival analysis to support decision-making in oncology drug development. The key objective of this study was to assess if insights regarding two relapse resistance modes, de-novo (treatment selects out a pre-existing resistant clone) or acquired (resistant clone develops during treatment), could be inferred from such data.

Methods

Individual lesion size time-series data were collected from ten Phase III study arms where patients were treated with either first-generation EGFR inhibitors (erlotinib or gefitinib) or chemotherapy (paclitaxel/carboplatin combination or docetaxel). The data for each arm of each study were analysed via a competing models framework to determine which of the two mathematical models of resistance, de-novo or acquired, best-described the data.

Results

Within the first-line setting (treatment naive patients), we found that the de-novo model best-described the gefitinib data, whereas, for paclitaxel/carboplatin, the acquired model was preferred. In patients pre-treated with paclitaxel/carboplatin, the acquired model was again preferred for docetaxel (chemotherapy), but for patients receiving gefitinib or erlotinib, both the acquired and de-novo models described the tumour size dynamics equally well. Furthermore, in all studies where a single model was preferred, we found a degree of correlation in the dynamics of lesions within a patient, suggesting that there is a degree of homogeneity in pharmacological response.

Conclusions

This analysis highlights that tumour size dynamics differ between different treatments and across lines of treatment. The analysis further suggests that these differences could be a manifestation of differing resistance mechanisms.

Electronic supplementary material

The online version of this article (10.1007/s00280-019-03840-3) contains supplementary material, which is available to authorized users.

Development and Validation of a Nomogram Prognostic Model for Patients With Advanced Non-Small-Cell Lung Cancer

Importance

Nomogram prognostic models can facilitate cancer patient treatment plans and patient enrollment in clinical trials.

Objective

The primary objective is to provide an updated and accurate prognostic model for predicting the survival of advanced non-small-cell lung cancer (NSCLC) patients, and the secondary objective is to validate a published nomogram prognostic model for NSCLC using an independent patient cohort.

Design

1817 patients with advanced NSCLC from the control arms of 4 Phase III randomized clinical trials were included in this study. Data from 524 NSCLC patients from one of these trials were used to validate a previously published nomogram and then used to develop an updated nomogram. Patients from the other 3 trials were used as independent validation cohorts of the new nomogram. The prognostic performances were comprehensively evaluated using hazard ratios, integrated area under the curve (AUC), concordance index, and calibration plots.

Setting

General community.

Main outcome

A nomogram model was developed to predict overall survival in NSCLC patients.

Results

We demonstrated the prognostic power of the previously published model in an independent cohort. The updated prognostic model contains the following variables: sex, histology, performance status, liver metastasis, hemoglobin level, white blood cell counts, peritoneal metastasis, skin metastasis, and lymphocyte percentage. This model was validated using various evaluation criteria on the 3 independent cohorts with heterogeneous NSCLC populations. In the SUN1087 patient cohort, the continuous risk score output by the nomogram achieved an integrated area under the receiver operating characteristics (ROC) curve of 0.83, a log-rank P-value of 3.87e-11, and a concordance index of 0.717. In the SAVEONCO patient cohort, the integrated area under the ROC curve was 0.755, the log-rank P-value was 4.94e-6 and the concordance index was 0.678. In the VITAL patient cohort, the integrated area under the ROC curve was 0.723, the log-rank P-value was 1.36e-11, and the concordance index was 0.654. We implemented the proposed nomogram and several previously published prognostic models on an online Web server for easy user access.

Conclusions

This nomogram model based on basic clinical features and routine lab testing predicts individual survival probabilities for advanced NSCLC and exhibits cross-study robustness.

Effect of Breast Irradiation on Cardiac Disease in Women Enrolled in BCIRG-001 at 10-Year Follow-Up

PURPOSE

To investigate cardiac toxicity associated with breast radiation therapy (RT) at 10-year follow-up in BCIRG-001, a phase 3 trial comparing adjuvant anthracycline chemotherapy (fluorouracil, doxorubicin, and cyclophosphamide) with anthracycline-taxane chemotherapy (docetaxel, doxorubicin, and cyclophosphamide) in women with lymph node-positive early breast cancer.

METHODS AND MATERIALS

Prospective data from all 746 patients in the control arm (fluorouracil, doxorubicin, and cyclophosphamide) of BCIRG-001 at 10-year follow-up were obtained from Project Data Sphere. Cardiac toxicities examined included myocardial infarction (MI), heart failure (HF), arrhythmias, and relative and absolute left ventricular ejection fraction decrease of >20% from baseline. Toxicities were compared between patients who received RT versus no RT, left-sided RT versus no RT, and internal mammary nodal RT versus no RT.

RESULTS

Of the 746 patients, 559 (75%) received RT to a median dose of 50 Gy. Myocardial infarction occurred in 3 RT patients (0.5%) versus 6 no-RT patients (3%) (P=.01). Heart failure was seen in 15 RT patients (2.7%) versus 3 no-RT patients (1.6%) (P=.6). Among these, 35 RT patients (18%) had a left ventricular ejection fraction relative decrease of >20% baseline versus 7 (10%) who did not receive RT (P=.1). Arrhythmias were more common in RT patients (3.2%) versus no-RT patients (0%) (P=.01). On univariable and multivariable analysis HF was not significantly associated with RT, and MI was negatively associated with RT.

CONCLUSIONS

In this retrospective analysis of prospective toxicity outcomes, there is an increased risk of arrhythmias but no clear evidence of significantly increased risk of MI or HF at 10 years in lymph node-positive women treated with breast RT and uniform adjuvant doxorubicin-based chemotherapy. Given the low incidence of these outcomes, studies with larger numbers are needed to confirm our findings.

Three general concepts to improve risk prediction: good data, wisdom of the crowd, recalibration

In today's information age, the necessary means exist for clinical risk prediction to capitalize on a multitude of data sources, increasing the potential for greater accuracy and improved patient care. Towards this objective, the Prostate Cancer DREAM Challenge posted comprehensive information from three clinical trials recording survival for patients with metastatic castration-resistant prostate cancer treated with first-line docetaxel. A subset of an independent clinical trial was used for interim evaluation of model submissions, providing critical feedback to participating teams for tailoring their models to the desired target. Final submitted models were evaluated and ranked on the independent clinical trial. Our team, called "A Bavarian Dream", utilized many of the common statistical methods for data dimension reduction and summarization during the trial. Three general modeling principles emerged that were deemed helpful for building accurate risk prediction tools and ending up among the winning teams of both sub-challenges. These principles included: first, good data, encompassing the collection of important variables and imputation of missing data; second, wisdom of the crowd, extending beyond the usual model ensemble notion to the inclusion of experts on specific risk ranges; and third, recalibration, entailing transfer learning to the target source. In this study, we illustrate the application and impact of these principles applied to data from the Prostate Cancer DREAM Challenge.

A patient-level data meta-analysis of standard-of-care treatments from eight prostate cancer clinical trials

Open clinical trial data offer many opportunities for the scientific community to independently verify published results, evaluate new hypotheses and conduct meta-analyses. These data provide valuable opportunities for scientific advances in medical research. Herein we present the comparative meta-analysis of different standard of care treatments from newly available comparator arm data from several prostate cancer clinical trials. Comparison of survival rates following treatment with mitoxantrone or docetaxel in combination with prednisone as well as prednisone alone, validated the previously demonstrated superiority of treatment with docetaxel. Additionally, comparison of four testosterone suppression treatments in hormone-refractory prostate cancer revealed that subjects who had undergone surgical castration had significantly lower survival rates than those treated with LHRH, anti-androgen or LHRH plus anti-androgen, suggesting that this treatment option is less optimal. This study illustrates how the use of patient-level clinical trial data enables meta-analyses that can provide new insights into clinical outcomes of standard of care treatments and thus, once validated, has the potential to help optimize healthcare delivery.

Individual Patient Data Analysis of Randomized Clinical Trials: Impact of Black Race on Castration-resistant Prostate Cancer Outcomes

BACKGROUND

Population data suggest that black men have a higher risk of dying from prostate cancer (PCa) than other racial ethnicities.

OBJECTIVE

To examine the impact of black race on progression-free survival (PFS) and overall survival (OS) among men with metastatic castration-resistant PCa (mCRPC) enrolled in randomized controlled trials (RCTs).

DESIGN, SETTING, AND PARTICIPANTS

A pooled analysis was performed on individual patient data from five modern PCa RCTs available from Project Data Sphere.

OUTCOME MEASUREMENTS AND STATISTICAL ANALYSIS

Adjusted hazard ratios (HRs) were calculated to compare black and white race regarding PFS and OS. Subgroup analyses of mCRPC trials were performed based on the control arm treatments (mitoxantrone or docetaxel). Relevant covariates were used for adjustment in all analyses.

RESULTS AND LIMITATIONS

A total of 1613 patients were included; 77 were black (4.7%). No significant differences between black and white men's baseline characteristics were noted regarding age, performance status, or pretreatment prostate-specific antigen. The pooled HRs for black race for OS and PFS were 1.01 (95% confidence interval [CI], 0.73-1.35) and 1.29 (95% CI, 0.95-1.76), respectively. The median OS for black compared with white men was 254 versus 238 d (p=0.92), respectively, with mitoxantrone and 581 versus 546 d (p=0.53), respectively, with docetaxel. The primary limitation was the relatively small number of black men enrolled in mCRPC clinical trials.

CONCLUSIONS

In the context of RCTs, in which patients receive generally uniform treatment, a significant difference in OS for black men could not be detected in mCRPC. Black men continue to be dramatically underrepresented in RCTs, and efforts are needed to increase minority accrual to these trials.

PATIENT SUMMARY

We looked at the outcomes of men treated in randomized controlled trials to determine the impact of black race on survival. We found that in the context of modern clinical trials, there does not appear to be a significant difference in survival between black and white races; however, a trend for greater progression in black men was noted.

UK publicly funded Clinical Trials Units supported a controlled access approach to share individual participant data but highlighted concerns

OBJECTIVES

Evaluate current data sharing activities of UK publicly funded Clinical Trial Units (CTUs) and identify good practices and barriers.

STUDY DESIGN AND SETTING

Web-based survey of Directors of 45 UK Clinical Research Collaboration (UKCRC)-registered CTUs.

RESULTS

Twenty-three (51%) CTUs responded: Five (22%) of these had an established data sharing policy and eight (35%) specifically requested consent to use patient data beyond the scope of the original trial. Fifteen (65%) CTUs had received requests for data, and seven (30%) had made external requests for data in the previous 12 months. CTUs supported the need for increased data sharing activities although concerns were raised about patient identification, misuse of data, and financial burden. Custodianship of clinical trial data and requirements for a CTU to align its policy to their parent institutes were also raised. No CTUs supported the use of an open access model for data sharing.

CONCLUSION

There is support within the publicly funded UKCRC-registered CTUs for data sharing, but many perceived barriers remain. CTUs are currently using a variety of approaches and procedures for sharing data. This survey has informed further work, including development of guidance for publicly funded CTUs, to promote good practice and facilitate data sharing.

Opening clinical trial data: are the voluntary data-sharing portals enough?

Data generated by the numerous clinical trials conducted annually worldwide have the potential to be extremely beneficial to the scientific and patient communities. This potential is well recognized and efforts are being made to encourage the release of raw patient-level data from these trials to the public. The issue of sharing clinical trial data has recently gained attention, with many agreeing that this type of data should be made available for research in a timely manner. The availability of clinical trial data is most important for study reproducibility, meta-analyses, and improvement of study design. There is much discussion in the community over key data sharing issues, including the risks this practice holds. However, one aspect that remains to be adequately addressed is that of the accessibility, quality, and usability of the data being shared. Herein, experiences with the two current major platforms used to store and disseminate clinical trial data are described, discussing the issues encountered and suggesting possible solutions.

Views of Ethical Best Practices in Sharing Individual-Level Data From Medical and Public Health Research: A Systematic Scoping Review

There is increasing support for sharing individual-level data generated by medical and public health research. This scoping review of empirical research and conceptual literature examined stakeholders' perspectives of ethical best practices in data sharing, particularly in low- and middle-income settings. Sixty-nine empirical and conceptual articles were reviewed, of which, only five were empirical studies and eight were conceptual articles focusing on low- and middle-income settings. We conclude that support for sharing individual-level data is contingent on the development and implementation of international and local policies and processes to support ethical best practices. Further conceptual and empirical research is needed to ensure data sharing policies and processes in low- and middle-income settings are appropriately informed by stakeholders' perspectives.

The project data sphere initiative: accelerating cancer research by sharing data

BACKGROUND

In this paper, we provide background and context regarding the potential for a new data-sharing platform, the Project Data Sphere (PDS) initiative, funded by financial and in-kind contributions from the CEO Roundtable on Cancer, to transform cancer research and improve patient outcomes. Given the relatively modest decline in cancer death rates over the past several years, a new research paradigm is needed to accelerate therapeutic approaches for oncologic diseases. Phase III clinical trials generate large volumes of potentially usable information, often on hundreds of patients, including patients treated with standard of care therapies (i.e., controls). Both nationally and internationally, a variety of stakeholders have pursued data-sharing efforts to make individual patient-level clinical trial data available to the scientific research community.

POTENTIAL BENEFITS AND RISKS OF DATA SHARING

For researchers, shared data have the potential to foster a more collaborative environment, to answer research questions in a shorter time frame than traditional randomized control trials, to reduce duplication of effort, and to improve efficiency. For industry participants, use of trial data to answer additional clinical questions could increase research and development efficiency and guide future projects through validation of surrogate end points, development of prognostic or predictive models, selection of patients for phase II trials, stratification in phase III studies, and identification of patient subgroups for development of novel therapies. Data transparency also helps promote a public image of collaboration and altruism among industry participants. For patient participants, data sharing maximizes their contribution to public health and increases access to information that may be used to develop better treatments. Concerns about data-sharing efforts include protection of patient privacy and confidentiality. To alleviate these concerns, data sets are deidentified to maintain anonymity. To address industry concerns about protection of intellectual property and competitiveness, we illustrate several models for data sharing with varying levels of access to the data and varying relationships between trial sponsors and data access sponsors.

THE PROJECT DATA SPHERE INITIATIVE

PDS is an independent initiative of the CEO Roundtable on Cancer Life Sciences Consortium, built to voluntarily share, integrate, and analyze comparator arms of historical cancer clinical trial data sets to advance future cancer research. The aim is to provide a neutral, broad-access platform for industry and academia to share raw, deidentified data from late-phase oncology clinical trials using comparator-arm data sets. These data are likely to be hypothesis generating or hypothesis confirming but, notably, do not take the place of performing a well-designed trial to address a specific hypothesis. Prospective providers of data to PDS complete and sign a data sharing agreement that includes a description of the data they propose to upload, and then they follow easy instructions on the website for uploading their deidentified data. The SAS Institute has also collaborated with the initiative to provide intrinsic analytic tools accessible within the website itself. As of October 2014, the PDS website has available data from 14 cancer clinical trials covering 9,000 subjects, with hopes to further expand the database to include more than 25,000 subject accruals within the next year. PDS differentiates itself from other data-sharing initiatives by its degree of openness, requiring submission of only a brief application with background information of the individual requesting access and agreement to terms of use. Data from several different sponsors may be pooled to develop a comprehensive cohort for analysis. In order to protect patient privacy, data providers in the U.S. are responsible for deidentifying data according to standards set forth by the Privacy Rule of the U.S. Health Insurance Portability and Accountability Act of 1996. USING DATA SHARING TO IMPROVE OUTCOMES IN CANCER THE "PROSTATE CANCER CHALLENGE": Control-arm data of several studies among patients with metastatic castration-resistant prostate cancer (mCRPC) are currently available through PDS. These data sets have multiple potential uses. The "Prostate Cancer Challenge" will ask the cancer research community to use clinical trial data deposited in the PDS website to address key research questions regarding mCRPC. General themes that could be explored by the cancer community are described in this article: prognostic models evaluating the influence of pretreatment factors on survival and patient-reported outcomes; comparative effectiveness research evaluating the efficacy of standard of care therapies, as illustrated in our companion article comparing mitoxantrone plus prednisone with prednisone alone; effects of practice variation in dose, frequency, and duration of therapy; level of patient adherence to elements of trial protocols to inform the design of future clinical trials; and age of subjects, regional differences in health care, and other confounding factors that might affect outcomes.

POTENTIAL LIMITATIONS AND METHODOLOGICAL CHALLENGES

The number of data sets available and the lack of experimental-arm data limit the potential scope of research using the current PDS. The number of trials is expected to grow exponentially over the next year and may include multiple cancer settings, such as breast, colorectal, lung, hematologic malignancy, and bone marrow transplantation. Other potential limitations include the retrospective nature of the data analyses performed using PDS and its generalizability, given that clinical trials are often conducted among younger, healthier, and less racially diverse patient populations. Methodological challenges exist when combining individual patient data from multiple clinical trials; however, advancements in statistical methods for secondary database analysis offer many tools for reanalyzing data arising from disparate trials, such as propensity score matching. Despite these concerns, few if any comparable data sets include this level of detail across multiple clinical trials and populations.

CONCLUSION

Access to large, late-phase, cancer-trial data sets has the potential to transform cancer research by optimizing research efficiency and accelerating progress toward meaningful improvements in cancer care. This type of platform provides opportunities for unique research projects that can examine relatively neglected areas and that can construct models necessitating large amounts of detailed data. The full potential of PDS will be realized only when multiple tumor types and larger numbers of data sets are available through the website.

Big Data in Pharmaceutical R&D: Creating a Sustainable R&D Engine

Over the last 20 years, productivity in the pharmaceutical industry has been diminishing because of constantly increasing costs while output has overall been stagnant. Despite many efforts, productivity remains a challenge within the industry. At the same time, healthcare providers quite rightly require better value for money and clear evidence that new drugs are better than the current standard of care, making a complex situation even more complex. With the implementation of ‘Big Data’ initiatives trying to integrate data from disparate data sources and disciplines that are available in life science, the industry has identified a new frontier that might provide the insights needed to turn the ship around and allow the industry to return to sustainable growth.

Anonymising and sharing individual patient data

There is a strong movement to share individual patient data for secondary purposes, particularly for research. A major obstacle to broad data sharing has been the concern for patient privacy. One of the methods for protecting the privacy of patients in accordance with privacy laws and regulations is to anonymise the data before it is shared. This article describes the key concepts and principles for anonymising health data while ensuring it remains suitable for meaningful analysis.