Health Services Research Using SAS

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9781490582603: Health Services Research Using SAS
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I have read hundreds of SAS® books and manuals in my career as a researcher and teacher. Most provide important rules of the road or “Sasoids”, a phrase coined by Andy Ravenna an instructor at the New York SAS® Training Center. Since1980, they include manuals for all of the software releases from Version 4 in the1980s through Version 9 in 2008. Each serves as a resource to answer a coding question. In addition, SAS® provides on line help that codifies all of the coding questions with examples and syntax. These can be found on your help screen of you current SAS® release or at www.support.sas.com. I must confess that the book that I have had on the side of my computer since 1996 for quick answers The Little Book SAS®, by Delwiche DL and Slaughter, SJ, today in its Fifth Edition. In addition, I use Google as an invaluable resource for SAS syntax. Health Services Research Using SAS® also contains SAS rules and tools in Chapters 1 and 2. However, Chapter 3, 4 and 5 have real health care data from public sources such as the National Center for Health Statistics, National Hospital Discharge Survey, www.cdc.gov/nchs, the Organ Procurement and Transplantation Network, www.optn.transplat.hersa.gov and the California Office of Statewide Health Planning and Development (OSPHD) www.oshpd.ca.gov. Within each of these chapters is the history of the data sets, the organizational structure of the respective agencies, their mission and goals, and their published findings. Following the historical perspective chapters, SAS® coding is introduced for the analysis of the above data sets. All of the code uses best practices, learned from the faculty at New York SAS® Training Center. In addition, the scope and sequencing used in the courses includes exploratory data analysis (EDA), creating libraries, formatting, labels, indicator variables, truth logic, multivariate analysis, linear and logistic, and survival analysis. Each of these chapters selects an area of interest. For example, using the National Discharges Survey, you can identify the nations most common discharge diagnoses and procedures and compare the most common discharge diagnoses and procedures between the uninsured and insured. You can use the OPTN/UNOS data for the 88,636 liver transplants occurring in the U.S., between 1987 and 2008, present the demographic and clinical measurements for these transplants, identifying the factors that influence survival time. For the homeless who use California emergency departments (ED), you can show the most common discharge diagnoses and measure if age of the California homeless is influenced by gender, race, ethnicity, payer, disposition and county. All SAS® output is included which allows findings and results to be discussed. In addition each chapter has exercises and their corresponding answers. Before buying this book, your skills should include: (1) a strong understanding of statistical methods obtained in an undergraduate or graduate course. (2) familiarity with clinical coding methods such as the ICD-9-CM diagnosis and procedures and (3) function as an intermediate SAS® coder, and/or with experience in STATA, R, IBM SPSS or any of the numerous statistical packages listed on https//en.wikipedia.org/wiki/List_of statistical_packages.

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Dr. Raymond Arons earned his BS degree (Mechanical Engineering) in 1962 from Fairleigh Dickinson University, his MPH (Health Administration) in 1976, and his DrPH (Health Administration) in 1983 from Columbia University. Dr. Arons is a national expert in the secondary health care data sets and their use in answering wide ranging public health questions. He taught, "The Use of Large Scale Health Data Sets" to students from all departments at the Columbia School of Public Health (CSPH) and a variety of schools within at Columbia University including the school of Nursing, Social Work, Teachers College, Physician and Surgeons, and Dentistry. He was the director of the CSPH National Large Scale Health Data Center, a repository which contains over a billion patient records acquired from providers, governmental and academic surveys, making them available to both faculty and students for study. A large portion of the repository was provided from the National Center for Health Statistics (NHDS), The New York Statewide Research and Planning Cooperative (SPARCS), the California Office of Statewide Planning (OSPHD), The New Jersey Department of Health, and other federal state and non-governmental organization. He is one of the nation’s few authors of health services research who spent half of his life in a medical center. His 36 year career of hospitals and academic medicine is unprecedented. Prior to employment at Columbia Presbyterian Medical Center, Dr. Arons was a propulsion engineer with Grumman Aircraft Engineering Corporation and a member of the design and testing team of the Lunar Module rocket, descent and ascent, engines for the NASA Apollo Space Program. In 1969, Dr. Arons joined the staff at Presbyterian Hospital and was a member of its administrative staff for 26 years. He worked in a number of capacities including deputy to the chief medical officer, and director of the Office of Case Mix Studies. In 1997, Dr. Arons was appointed to the Faculty of the School of Public Health as an Assistant Professor. Dr. Arons is an Adjunct Full Professor in the Management Department of the Zicklin School of Business where he began in 1999. In 2011 he was appointed Adjunct Professor at the University of Denver Medical School and Graduate School of Arts and Science, Aurora Colorado. He is also Director of the Melody Arons Center for Applied Preschool Research and Education.

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Book Description Createspace, United States, 2013. Paperback. Condition: New. Original. Language: English . Brand New Book ***** Print on Demand *****. I have read hundreds of SAS(r) books and manuals in my career as a researcher and teacher. Most provide important rules of the road or Sasoids, a phrase coined by Andy Ravenna an instructor at the New York SAS(r) Training Center. Since1980, they include manuals for all of the software releases from Version 4 in the1980s through Version 9 in 2008. Each serves as a resource to answer a coding question. In addition, SAS(r) provides on line help that codifies all of the coding questions with examples and syntax. These can be found on your help screen of you current SAS(r) release or at I must confess that the book that I have had on the side of my computer since 1996 for quick answers The Little Book SAS(r), by Delwiche DL and Slaughter, SJ, today in its Fifth Edition. In addition, I use Google as an invaluable resource for SAS syntax. Health Services Research Using SAS(r) also contains SAS rules and tools in Chapters 1 and 2. However, Chapter 3, 4 and 5 have real health care data from public sources such as the National Center for Health Statistics, National Hospital Discharge Survey, the Organ Procurement and Transplantation Network, and the California Office of Statewide Health Planning and Development (OSPHD) Within each of these chapters is the history of the data sets, the organizational structure of the respective agencies, their mission and goals, and their published findings. Following the historical perspective chapters, SAS(r) coding is introduced for the analysis of the above data sets. All of the code uses best practices, learned from the faculty at New York SAS(r) Training Center. In addition, the scope and sequencing used in the courses includes exploratory data analysis (EDA), creating libraries, formatting, labels, indicator variables, truth logic, multivariate analysis, linear and logistic, and survival analysis. Each of these chapters selects an area of interest. For example, using the National Discharges Survey, you can identify the nations most common discharge diagnoses and procedures and compare the most common discharge diagnoses and procedures between the uninsured and insured. You can use the OPTN/UNOS data for the 88,636 liver transplants occurring in the U.S., between 1987 and 2008, present the demographic and clinical measurements for these transplants, identifying the factors that influence survival time. For the homeless who use California emergency departments (ED), you can show the most common discharge diagnoses and measure if age of the California homeless is influenced by gender, race, ethnicity, payer, disposition and county. All SAS(r) output is included which allows findings and results to be discussed. In addition each chapter has exercises and their corresponding answers. Before buying this book, your skills should include: (1) a strong understanding of statistical methods obtained in an undergraduate or graduate course. (2) familiarity with clinical coding methods such as the ICD-9-CM diagnosis and procedures and (3) function as an intermediate SAS(r) coder, and/or with experience in STATA, R, IBM SPSS or any of the numerous statistical packages listed on of statistical. Seller Inventory # APC9781490582603

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Book Description Createspace, United States, 2013. Paperback. Condition: New. Original. Language: English . Brand New Book ***** Print on Demand *****.I have read hundreds of SAS(r) books and manuals in my career as a researcher and teacher. Most provide important rules of the road or Sasoids, a phrase coined by Andy Ravenna an instructor at the New York SAS(r) Training Center. Since1980, they include manuals for all of the software releases from Version 4 in the1980s through Version 9 in 2008. Each serves as a resource to answer a coding question. In addition, SAS(r) provides on line help that codifies all of the coding questions with examples and syntax. These can be found on your help screen of you current SAS(r) release or at I must confess that the book that I have had on the side of my computer since 1996 for quick answers The Little Book SAS(r), by Delwiche DL and Slaughter, SJ, today in its Fifth Edition. In addition, I use Google as an invaluable resource for SAS syntax. Health Services Research Using SAS(r) also contains SAS rules and tools in Chapters 1 and 2. However, Chapter 3, 4 and 5 have real health care data from public sources such as the National Center for Health Statistics, National Hospital Discharge Survey, the Organ Procurement and Transplantation Network, and the California Office of Statewide Health Planning and Development (OSPHD) Within each of these chapters is the history of the data sets, the organizational structure of the respective agencies, their mission and goals, and their published findings. Following the historical perspective chapters, SAS(r) coding is introduced for the analysis of the above data sets. All of the code uses best practices, learned from the faculty at New York SAS(r) Training Center. In addition, the scope and sequencing used in the courses includes exploratory data analysis (EDA), creating libraries, formatting, labels, indicator variables, truth logic, multivariate analysis, linear and logistic, and survival analysis. Each of these chapters selects an area of interest. For example, using the National Discharges Survey, you can identify the nations most common discharge diagnoses and procedures and compare the most common discharge diagnoses and procedures between the uninsured and insured. You can use the OPTN/UNOS data for the 88,636 liver transplants occurring in the U.S., between 1987 and 2008, present the demographic and clinical measurements for these transplants, identifying the factors that influence survival time. For the homeless who use California emergency departments (ED), you can show the most common discharge diagnoses and measure if age of the California homeless is influenced by gender, race, ethnicity, payer, disposition and county. All SAS(r) output is included which allows findings and results to be discussed. In addition each chapter has exercises and their corresponding answers. Before buying this book, your skills should include: (1) a strong understanding of statistical methods obtained in an undergraduate or graduate course. (2) familiarity with clinical coding methods such as the ICD-9-CM diagnosis and procedures and (3) function as an intermediate SAS(r) coder, and/or with experience in STATA, R, IBM SPSS or any of the numerous statistical packages listed on of statistical. Seller Inventory # APC9781490582603

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