Managing your patients' data in the neonatal and pediatric ICU: An introduction to databases and statistical analysis -- Contents -- eNICU installation and administration instructions -- Acknowledgments -- Chapter 1 Introduction -- Part I Managing data and routine reporting -- Section 1 The process of managing clinical data -- Chapter 2 Paper-based patient records -- Chapter 3 Computer-based patient records -- Chapter 4 Aims of a patient data management process -- Section 2 Modeling data: Accurately representing our work and storing the data so we may reliably retrieve them -- Chapter 5 Data, information, and knowledge -- Chapter 6 Single tables and their limitations -- Chapter 7 Multiple tables: Where to put the data, relationships among tables, and creating a database -- Chapter 8 Relational database management systems: normalization (Codd 's rules) -- Section 3 Database software -- Chapter 9 From data model to database software -- Chapter 10 Integrity: anticipating and preventing data accuracy problems -- Chapter 11 Queries, forms, and reports -- Chapter 12 Programming for greater software control -- Chapter 13 Turning ideas into a useful tool: eNICU, point of care database software for the NICU -- Chapter 14 Making eNICU serve your own needs -- Section 4 Database administration -- Chapter 15 Single versus multiple users -- Chapter 16 Backup and recovery: assuring your data persists -- Chapter 17 Security: controlling access and protecting patientficon dentiality -- Conclusion Part I: Maintaining focus on a moving target -- Part II Learning from aggregate experience: exploring and analyzing data sets -- Section 5 Interrogating data -- Chapter 18 Asking questions of a data set: crafting a conceptual framework and testable hypothesis -- Chapter 19 Stata: a software tool to analyze data and produce graphical displays.

Chapter 20 Preparing to analyze data -- Section 6 Analytical concepts and methods -- Chapter 21 Variable types -- Chapter 22 Measurement values vary: describing their distribution and summarizing them quantitatively -- Chapter 23 Data from all versus some: populations and samples -- Chapter 24 Estimating population parameters: confidence intervals -- Chapter 25 Comparing two sample means and testing a hypothesis -- Chapter 26 Type I and type II error in a hypothesis test, power, and sample size -- Chapter 27 Comparing proportions: introduction to rates and odds -- Chapter 28 Stratifying the analysis of dichotomous outcomes: confounders and effect modifiers -- the Mantel-Haenszel method -- Chapter 29 Ways to measure and compare the frequency of outcomes, and standardization to compare rates -- Chapter 30 Comparing the means of more than two samples -- Chapter 31 Assuming little about the data: nonparametric methods of hypothesis testing -- Chapter 32 Correlation: measuring the relationship between two continuous variables -- Chapter 33 Predicting continuous outcomes: univariate and multivariate linear regression -- Chapter 34 Predicting dichotomous outcomes: logistic regression, and receiver operating characteristic -- Chapter 35 Predicting outcomes over time: survival analysis -- Chapter 36 Choosing variables and hypotheses: practical considerations -- Conclusion The challenge of transforming data and information to shared knowledge: tools that make us smart -- References -- Index.