Advanced life support accounts for 65% of emergency medical care among Medicare beneficiaries ( 1) and even more among patients with high-acuity conditions, such as stroke. The predominant response to out-of-hospital medical emergencies by ambulance providers in the United States is advanced life support (ALS) rather than basic life support (BLS). Results from instrumental variable analyses were broadly consistent with propensity score analyses for trauma and stroke, showed no survival differences between BLS and ALS for respiratory failure, and showed better survival at all time points with BLS than ALS for patients with AMI. Neurologic functioning favored BLS for all diagnoses. Patients with AMI did not exhibit differences in survival at 30 days but had better survival at 90 days with ALS (1.0 percentage point ). In propensity score analyses, survival to 90 days among patients with trauma, stroke, and respiratory failure was higher with BLS than ALS (6.1 percentage points for trauma 7.0 percentage points for stroke and 3.7 percentage points for respiratory failure).
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Exercises with SAS programming, statistical analyses, and their answer keys are provided for each chapter.Even coverage of both programming capabilities of the SAS system and its statistical procedures (most books cover one or the other).An expansive use of visuals, including screen shots, figures, and graphics, to illustrate statistical concepts and show results from SAS output.Statistical techniques and concepts are presented from the perspective of solving problems, to better engage students in the data analysis process.A disciplined focus on statistical topics that are always presented in beginning- to intermediate-level statistics courses, allowing for greater depth of understanding rather than breadth.A student study at replete with a multitude of computer programs, their output, specific details on how to check assumptions, as well as all data sets used in the book.ĭata Analysis Using SAS is a complete resource for Data Analysis I and II, Statistics I and II, Quantitative Reasoning, and SAS Programming courses across the social and behavioral sciences and health - especially those that carry a lab component.
The second half of the text goes into great depth on the most common statistical techniques and concepts - descriptive statistics, correlation, analysis of variance, and regression - used to analyze data in the social, behavioral, and health sciences using SAS commands. The coverage of the text is more evenly balanced among statistical analysis, SAS programming, and data/file management than any available text on the market. It provides students with a hands-on, exercise-heavy method for learning basic to intermediate SAS commands while understanding how to apply statistics and reasoning to real-world problems.ĭesigned to be used in order of teaching preference by instructor, the book is comprised of two primary sections: the first half of the text instructs students in techniques for data and file managements such as concatenating and merging files, conditional or repetitive processing of variables, and observations. Data Analysis Using SAS offers a comprehensive core text focused on key concepts and techniques in quantitative data analysis using the most current SAS commands and programming language.