Statistical analysis of data sets with missing values is a pervasive problem for which standard methods are of limited value. The first edition of Statistical Analysis with Missing Data has been a standard reference on missing-data methods. Now, reflecting extensive developments in Bayesian methods for simulating posterior distributions, this Second Edition by two acknowledged experts on the subject offers a thoroughly up-to-date, reorganized survey of current methodology for handling missing-data problems.

Statistical Analysis with Missing Data

International consensus report on the investigation and management of primary immune thrombocytopenia.

European LeukemiaNet recommendations for the management of chronic myeloid leukemia: 2013

Summary Type 1 diabetes is a chronic autoimmune disease characterised by insulin deficiency and resultant hyperglycaemia. Knowledge of type 1 diabetes has rapidly increased over the past 25 years, resulting in a broad understanding about many aspects of the disease, including its genetics, epidemiology, immune and β-cell phenotypes, and disease burden. Interventions to preserve β cells have been tested, and several methods to improve clinical disease management have been assessed. However, wide gaps still exist in our understanding of type 1 diabetes and our ability to standardise clinical care and decrease disease-associated complications and burden. This Seminar gives an overview of the current understanding of the disease and potential future directions for research and care.

Type 1 diabetes

In the United States, approximately 14 million people have had cancer and more than 1.6 million new cases are diagnosed each year. However, more than a decade after the Institute of Medicine (IOM) first studied the quality of cancer care, the barriers to achieving excellent care for all cancer patients remain daunting. Care often is not patient-centered, many patients do not receive palliative care to manage their symptoms and side effects from treatment, and decisions about care often are not based on the latest scientific evidence. The cost of cancer care also is rising faster than many sectors of medicine--having increased to $125 billion in 2010 from $72 billion in 2004--and is projected to reach $173 billion by 2020. Rising costs are making cancer care less affordable for patients and their families and are creating disparities in patients' access to high-quality cancer care. There also are growing shortages of health professionals skilled in providing cancer care, and the number of adults age 65 and older--the group most susceptible to cancer--is expected to double by 2030, contributing to a 45 percent increase in the number of people developing cancer. The current care delivery system is poorly prepared to address the care needs of this population, which are complex due to altered physiology, functional and cognitive impairment, multiple coexisting diseases, increased side effects from treatment, and greater need for social support. Delivering High-Quality Cancer Care: Charting a New Course for a System in Crisis presents a conceptual framework for improving the quality of cancer care. This study proposes improvements to six interconnected components of care: (1) engaged patients; (2) an adequately staffed, trained, and coordinated workforce; (3) evidence-based care; (4) learning health care information technology (IT); (5) translation of evidence into clinical practice, quality measurement and performance improvement; and (6) accessible and affordable care. This report recommends changes across the board in these areas to improve the quality of care. Delivering High-Quality Cancer Care: Charting a New Course for a System in Crisis provides information for cancer care teams, patients and their families, researchers, quality metrics developers, and payers, as well as HHS, other federal agencies, and industry to reevaluate their current roles and responsibilities in cancer care and work together to develop a higher quality care delivery system. By working toward this shared goal, the cancer care community can improve the quality of life and outcomes for people facing a cancer diagnosis.

/pdf/delivering-high-quality-cancer-care-charting-a-new-course-2u1fq5ml3k.pdf

Delivering High-Quality Cancer Care: Charting a New Course for a System in Crisis

/pdf/childhood-t-cell-acute-lymphoblastic-leukemia-in-a-single-4a9iojlip4.pdf

Childhood T-cell acute lymphoblastic leukemia in a single Latin American center: impact of improved treatment scheme and support therapy on survival.

Economic Challenges in Hematopoietic Cell Transplantation: How Will New and Established Programs Face the Growing Costs?

Introduction: Pregnancy-associated aplastic anemia (pAA) occurs when aplastic anemia (AA) is diagnosed for the first time during pregnancy and it is a rare but serious condition leading to severe maternal and fetal complications. It is unknown whether pregnancy triggers bone marrow failure or if this state is unrelated to the pathogenesis of pAA.Areas covered: In this review, three new cases of pAA are presented and its controversial etiologic relationship with pregnancy, its atypical presentation, and management are also discussed. Furthermore, a literature review of pAA cases between 1975 and 2020 was performed in PubMed, accessed via the National Library of Medicine PubMed interface. Keywords included 'aplastic anemia' AND 'pregnancy'. We found 54 cases reported in the literature with a clear diagnosis of pAA.Expert opinion: The diagnosis of pAA is challenging since pregnancy is associated with physiologic hematological changes in the complete blood count which can mask the disease. Meticulous monitoring and adequate support therapy given by a trained multidisciplinary team have the potential to improve outcomes for women and their neonates. All women should receive frequent assessments to optimize their care during pregnancy and after delivery, definitive treatment should be offered.

Pregnancy-associated aplastic anemia: a case-based review

Errar es humano

Background
Advances in automated cell separators have improved the efficiency of plateletpheresis and the possibility of obtaining double products (DP). We assessed cell processor accuracy of predicted platelet (PLT) yields with the goal of a better prediction of DP collections.

Study design and methods
This retrospective proof-of-concept study included 302 plateletpheresis procedures performed on a Trima Accel v6.0 at the apheresis unit of a hematology department. Donor variables, software predicted yield and actual PLT yield were statistically evaluated. Software prediction was optimized by linear regression analysis and its optimal cut-off to obtain a DP assessed by receiver operating characteristic curve (ROC) modeling.

Results
Three hundred and two plateletpheresis procedures were performed; in 271 (89.7%) occasions, donors were men and in 31 (10.3%) women. Pre-donation PLT count had the best direct correlation with actual PLT yield (r = 0.486. P < .001). Means of software machine-derived values differed significantly from actual PLT yield, 4.72 × 1011 vs.6.12 × 1011, respectively, (P < .001). The following equation was developed to adjust these values: actual PLT yield= 0.221 + (1.254 × theoretical platelet yield). ROC curve model showed an optimal apheresis device software prediction cut-off of 4.65 × 1011 to obtain a DP, with a sensitivity of 82.2%, specificity of 93.3%, and an area under the curve (AUC) of 0.909.

Conclusion
Trima Accel v6.0 software consistently underestimated PLT yields. Simple correction derived from linear regression analysis accurately corrected this underestimation and ROC analysis identified a precise cut-off to reliably predict a DP.

Plateletpheresis efficiency and mathematical correction of software-derived platelet yield prediction: A linear regression and ROC modeling approach

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