eNICU installation and administration instructions, |
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Acknowledgments, |
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Chapter 1 Introduction, |
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Part I Managing data and routine reporting |
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Section 1 The process of managing clinical data |
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Chapter 2 Paper-based patient records, |
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9 | (4) |
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Chapter 3 Computer-based patient records, |
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Chapter 4 Aims of a patient data management process, |
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Section 2 Modeling data: Accurately representing our work and storing the data so we may reliably retrieve them |
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Chapter 5 Data, information, and knowledge, |
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Chapter 6 Single tables and their limitations, |
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Chapter 7 Multiple tables: where to put the data, relationships among tables, and creating a database, |
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Chapter 8 Relational database management systems: normalization (Codd's rules), |
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42 | (6) |
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Section 3 Database software |
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Chapter 9 From data model to database software, |
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Chapter 10 Integrity: anticipating and preventing data accuracy problems, |
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60 | (7) |
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Chapter 11 Queries, forms, and reports, |
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Chapter 12 Programming for greater software control, |
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Chapter 13 Turning ideas into a useful tool: eNICU, point of care database software for the NICU, |
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Chapter 14 Making eNICU serve your own needs, |
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Section 4 Database administration |
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Chapter 15 Single versus multiple users, |
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146 | (5) |
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Chapter 16 Backup and recovery: assuring your data persists, |
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151 | (6) |
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Chapter 17 Security: controlling access and protecting patient confidentiality, |
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Conclusion Part I: Maintaining focus on a moving target, |
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Part II Learning from aggregate experience: exploring and analyzing data sets |
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Section 5 Interrogating data |
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Chapter 18 Asking questions of a data set: crafting a conceptual framework and testable hypothesis, |
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175 | (8) |
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Chapter 19 Stata: a software tool to analyze data and produce graphical displays, |
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183 | (2) |
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Chapter 20 Preparing to analyze data, |
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Section 6 Analytical concepts and methods |
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Chapter 21 Variable types, |
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195 | (3) |
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Chapter 22 Measurement values vary: describing their distribution and summarizing them quantitatively, |
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Chapter 23 Data from all versus some: populations and samples, |
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Chapter 24 Estimating population parameters: confidence intervals, 224 Chapter 25 Comparing two sample means and testing a hypothesis, |
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Chapter 26 Type I and type II error in a hypothesis test, power, and sample size, |
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Chapter 27 Comparing proportions: introduction to rates and odds, |
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Chapter 28 Stratifying the analysis of dichotomous outcomes: confounders and effect modifiers; the Mantel—Haenszel method, |
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Chapter 29 Ways to measure and compare the frequency of outcomes, and standardization to compare rates, |
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269 | (9) |
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Chapter 30 Comparing the means of more than two samples, |
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Chapter 31 Assuming little about the data: nonparametric methods of hypothesis testing, |
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287 | (3) |
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Chapter 32 Correlation: measuring the relationship between two continuous variables, |
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290 | (4) |
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Chapter 33 Predicting continuous outcomes: univariate and multivariate linear regression, |
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Chapter 34 Predicting dichotomous outcomes: logistic regression, and receiver operating characteristic, |
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Chapter 35 Predicting outcomes over time: survival analysis, |
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Chapter 36 Choosing variables and hypotheses: practical considerations, |
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346 | (5) |
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Conclusion The challenge of transforming data and information to shared knowledge: tools that make us smart, |
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351 | (4) |
References, |
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Index, |
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