Journal Publications

Project Data Sphere's success is ultimately measured by the practice-changing scientific insights discovered by the research community. We are proud to share the following list of peer-accepted publications, and we are grateful to the data providers, research scientists, and patients that make this possible.

Article Titlesort descending Journal
Temporal Trends in Clinical Evidence of 5-Year Survival Within Electronic Health Records Among Patients With Early-Stage Colon Cancer Managed With Laparoscopy-Assisted Colectomy vs Open Colectomy JAMA Network Open
The application of electronic medical records (EMRs) as a virtual comparator arm in a lung cancer clinical trial: A case study Journal of Clinical Oncology
The effect of post-mastectomy radiation in women with one to three positive nodes enrolled on the control arm of BCIRG-005 at ten year follow-up Radiotherapy and Oncology
The menopause after cancer study (MACS) - A multimodal technology assisted intervention for the management of menopausal symptoms after cancer – Trial protocol of a phase II study Contemporary Clinical Trials Communications
The Project Data Sphere Initiative: Accelerating Cancer Research by Sharing Data The Oncologist
The Prostate Cancer DREAM Challenge: A Community-Wide Effort to Use Open Clinical Trial Data for the Quantitative Prediction of Outcomes in Metastatic Prostate Cancer The Oncologist
The Use of External Control Data for Predictions and Futility Interim Analyses in Clinical Trials Neuro-Oncology
The VENUSS prognostic model to predict disease recurrence following surgery for non-metastatic papillary renal cell carcinoma: development and evaluation using the ASSURE prospective clinical trial cohort BMC Medicine
Towards a Grammar for Processing Clinical Trial Data R Journal Volume
Translation of Prognostic and Pharmacodynamic Biomarkers from Trial to Non-trial Patients with Metastatic Castration-resistant Prostate Cancer Treated with Docetaxel Clinical Oncology
Treatment Stratification of Patients with Metastatic Castration-Resistant Prostate Cancer by Machine Learning iScience
Two-step feature selection for predicting survival time of patients with metastatic castrate resistant prostate cancer F1000 Research
Using complex networks for refining survival prognosis in prostate cancer patient F1000 Research
Using horseshoe prior for incorporating multiple historical control data in randomized controlled trials Sage Journals

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