Validation of algorithms to select patients with multiple myeloma and patients initiating myeloma treatment in the national Veterans Affairs Healthcare System
暂无分享,去创建一个
J. Gaziano | M. Brophy | J. Driver | N. Fillmore | M. Abdallah | N. Do | C. Dumontier | C. Yildirim | J. La | Hamza Hassan | C. Edwards | K. Verma | J. Corrigan | Grace Ferri | Mayuri Dharne | Nikhil C. Munshi
[1] N. Munshi,et al. Contemporary Analysis of Electronic Frailty Measurement in Older Adults with Multiple Myeloma Treated in the National US Veterans Affairs Healthcare System , 2021, Cancers.
[2] Shaji K. Kumar,et al. Paving the way to precision medicine in multiple myeloma , 2021, Expert review of hematology.
[3] Sheila Ochylski,et al. Big Data in the Veterans Health Administration: A Nursing Informatics Perspective. , 2021, Journal of nursing scholarship : an official publication of Sigma Theta Tau International Honor Society of Nursing.
[4] G. Abel,et al. Defining multimorbidity and its impact in older united states veterans newly treated for multiple myeloma. , 2021, Journal of the National Cancer Institute.
[5] H. Krumholz,et al. Prevalence of Missing Data in the National Cancer Database and Association With Overall Survival , 2020, JAMA network open.
[6] S. Rajkumar,et al. Multiple myeloma: 2020 update on diagnosis, risk‐stratification and management , 2020, American journal of hematology.
[7] N. Aggarwal. Ramifications of the VA MISSION Act of 2018 on Mental Health: Potential Implementation Challenges and Solutions. , 2019, JAMA psychiatry.
[8] B. Gage,et al. Predicting venous thromboembolism in multiple myeloma: development and validation of the IMPEDE VTE score , 2019, American journal of hematology.
[9] G. Abel,et al. Multimorbidity patterns and their association with survival in a large national cohort of older veterans with multiple myeloma. , 2019, Journal of Clinical Oncology.
[10] A. Figueiras,et al. Bias in pharmacoepidemiologic studies using secondary health care databases: a scoping review , 2019, BMC Medical Research Methodology.
[11] S. Oliveria,et al. Validating an algorithm for multiple myeloma based on administrative data using a SEER tumor registry and medical record review , 2019, Pharmacoepidemiology and drug safety.
[12] M. Dimopoulos,et al. Interpreting clinical trial data in multiple myeloma: translating findings to the real-world setting , 2018, Blood Cancer Journal.
[13] R. Vij,et al. Development of an Algorithm to Distinguish Smoldering Versus Symptomatic Multiple Myeloma in Claims-Based Data Sets. , 2017, JCO clinical cancer informatics.
[14] Robert Platt,et al. Specifying a target trial prevents immortal time bias and other self-inflicted injuries in observational analyses. , 2016, Journal of clinical epidemiology.
[15] S. Rajkumar,et al. Daratumumab in multiple myeloma , 2016, The Lancet.
[16] K. Shea,et al. The Veterans Affairs's Corporate Data Warehouse: Uses and Implications for Nursing Research and Practice , 2015, Nursing administration quarterly.
[17] Medicaid Services. Strategies and Priorities for Information Technology at the Centers for Medicare and Medicaid Services , 2011 .
[18] R. Kyle,et al. Management of monoclonal gammopathy of undetermined significance (MGUS) and smoldering multiple myeloma (SMM). , 2011, Oncology.
[19] R. Kyle,et al. From Myeloma Precursor Disease to Multiple Myeloma: New Diagnostic Concepts and Opportunities for Early Intervention , 2011, Clinical Cancer Research.
[20] Jasvinder A Singh,et al. Accuracy of Veterans Administration databases for a diagnosis of rheumatoid arthritis. , 2004, Arthritis and rheumatism.
[21] M. Kelley,et al. Summary of Veterans Health Administration Cancer Data Sources. , 2019, Journal of registry management.
[22] Processes,et al. COMMITTEE ON FUTURE INFORMATION ARCHITECTURES, PROCESSES, AND STRATEGIES FOR THE CENTERS FOR MEDICARE AND MEDICAID SERVICES , 2010 .