Managing your patients' data in the neonatal and pediatric ICU : an introduction to databases and statistical analysis /
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| Format: | eBook |
| Language: | English |
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Malden, Mass. :
BMJ Books/Blackwell Pub.,
2006.
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| Online Access: | Connect to the full text of this electronic book Contributor biographical information Publisher description Table of contents |
Table of Contents:
- Paper-based patient records
- Computer-based patient records
- Aims of a patient data management process
- Data, information, and knowledge
- Single tables and their limitations
- Multiple tables: where to put the data, relationships among tables, and creating a database
- Relational database management systems: normalization (Codd's rules)
- From data model to database software
- Integrity: anticipating and preventing data accuracy problems
- Queries, forms, and reports
- Programming for greater software control
- Turning ideas into a useful tool: eNICU, point of care database software for the NICU
- Making eNICU serve your own needs
- Single versus multiple users
- Backup and recovery: assuring your data persists
- Security: controlling access and protecting patient confidentiality
- Asking questions of a data set: crafting a conceptual framework and testable hypothesis
- Stata: a software tool to analyze data and produce graphical displays
- Preparing to analyze data
- Variable types
- Measurement values vary: describing their distribution and summarizing them quantitatively
- Data from all versus some: populations and samples
- Estimating population parameters: confidence intervals
- Comparing two sample means and testing a hypothesis
- Type I and type II error in a hypothesis test, power, and sample size
- Comparing proportions: introduction to rates and odds
- Stratifying the analysis of dichotomous outcomes: confounders and effect modifiers; the Mantel-Haenszel method
- Ways to measure and compare the frequency of outcomes, and standardization to compare rates
- Comparing the means of more than two samples
- Assuming little about the data: nonparametric methods of hypothesis testing
- Correlation: measuring the relationship between two continuous variables
- Predicting continuous outcomes: univariate and multivariate linear regression
- Predicting dichotomous outcomes: logistic regression, and receiver operating characteristic
- Predicting outcomes over time: survival analysis
- Choosing variables and hypotheses: practical considerations.