Bibliothèque de L'IMIST/CNRST

"Includes eNICU software for the neonatal intensive care unit, which may be modified for local use or other clinical settings."

Includes bibliographical references.

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; creating a database -- Relational database management systems: normalization; Codd's rules -- From data model to database software -- Integrity: anticipating and preventing problems with data accuracy -- 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 vs. multiple user -- Backup: assuring your data persists -- Security: controlling access and protecting patient confidentiality -- 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 or some: populations and samples -- Estimating population parameters; confidence intervals -- Comparing two sample means; statistical significance and clinical significance -- Type I and type II error in a hypothesis test; power; sample size -- Comparing proportions; introduction to rates and odds -- Stratifying the analysis of dichotomous outcomes; confounders and effect modifiers; multiple 2 x 2 tables: the Mantel-Haenszel method -- Ways to measure and compare the frequency of outcomes; standardization -- Comparing the means of more than two samples -- Assuming little about the data: non-parametric 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; receiver operating characteristic (ROC) -- Predicting outcomes over time: survival analysis -- Choosing variables and hypotheses: practical considerations.