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Note: Supplemental materials are not guaranteed with Rental or Used book purchases.
Purchase Benefits
What is included with this book?
Hospitals monitoring is becoming more complex and is increasing both because staff want their data analysed and because of increasing mandated surveillance. This book provides a suite of functions in R, enabling scientists and data analysts working in infection management and quality improvement departments in hospitals, to analyse their often non-independent data which is frequently in the form of trended, over-dispersed and sometimes auto-correlated time series; this is often difficult to analyse using standard office software.
This book provides much-needed guidance on data analysis using R for the growing number of scientists in hospital departments who are responsible for producing reports, and who may have limited statistical expertise.
This book explores data analysis using R and is aimed at scientists in hospital departments who are responsible for producing reports, and who are involved in improving safety. Professionals working in the healthcare quality and safety community will also find this book of interest
Statistical Methods for Hospital Monitoring with R:
Anthony Morton and Geoffrey Playford, Princess Alexandra Hospital, Brisbane, Australia
Kerrie Mengersen, Science and Engineering Faculty, Queensland University of Technology, Australia
Michael Whitby, Greenslopes Specialist Centre, Queensland, Australia
Introduction.
Overview and rationale for this book
Motivation for this book
Why R?
Other reading for R
What methods are covered in this book?
Structure of his book
Using R
Entering data
Dates
Dates, important note and chron()
Exporting data
Further notes
Charts introduction
When there is no date column
Control Chart Menu
Chapter 1.
Proportion
Confidence interval for
Significance test for
Likelihood ratio
Confidence interval for series of proportions
Difference between two proportions
Confidence intervals for
More than two proportions
Summary average of a series of proportions, Newcombe’s method
Stratified proportion data, differences between rates, Newcombe’s method
Mantel-Haenszel analysis
DerSimonian-Laird analysis
Chapter 2.
Risk adjustment
Stratification
Logistic regression
Discrimination
Calibration
Re-calibration
Displaying and analyzing data from multiple institutions
Tabulations
Tables in wide format
Z-scores
Multiple confidence intervals
Funnel plot
Indirect standardisation
False discovery
SMR
Random effects, shrinkage
Openbugs Gamma-Poisson Hierarchical Model
Empirical Bayes
Bootstrap
Variation in predicted values
Complex surgical site infections (low rate data)
Funnel plot analysis of low rate data
Shrinkage analysis of low rate data with Openbugs Gamma-Poisson Hierarchical Model
Appendix 1, further tabulations
Appendix 2, risk scores for a hospital derived from data with its data removed
Chapter 3.
CUSUM and related charts for binary data
Cumulative Observed-Expected (O-E) chart and combined CUSUM and O-E chart
Cumulative Funnel plot and combined CUSUM and Funnel plot
Example
Including Risk Adjustment
CUSUM chart
Cumulative observed minus expected (O-E) chart
Discrimination and Calibration of Risk Adjustment
Shewhart P chart and EWMA chart
Run-Sum chart
The EWMA chart
Expected values
Spline or generalized additive model (GAM) chart
Few time periods
Quarterly data and data without a first date column
Composite measures
Additional tabulations
Underreporting
New CUSUM and EWMA charts, predicting the current value for low-rate data
Risk-adjusted Bernoulli CUSUM
Single observation data and EWMA charts
Quarterly rates for current rate values in low rate data
Intervals between uncommon binary adverse events
Appendix single observation EWMA limits, a proposal
Chapter 4.
Introduction
Rate and count data
Single count or rate
Confidence Interval for single count or rate
Significance Test for single count or rate
Confidence limits for columns of counts and rates
Two independent rates
Confidence interval for two independent rates
Hypothesis test for two independent rates
Bayesian approach
Chi-squared and trend tests for count and rate data
Stratified count and rate data
Summary rate
Stratified count and rate data two sets of rates
Direct standardisation
Mantel-Haenszel, Homogeneity and Trend Tests
Count data variation
Complex Systems, Networks and Variation
Chapter 5
Introduction, data, limitations of aggregated count data analysis
Confidence intervals for the hospitals’ Staphylococcus aureus data
Funnel plots for the Staphylococcus aureus data
Tabulations and Z-scores
Overdispersion, false discovery, very small expected
Proposal for false discovery modified funnel plot
Bayesian shrinkage
Openbugs gamma-Poisson hierarchical model
Further tabulations, device-related and MRSA bacteraemias
Rearranging hospital levels for MRSA
Bacteraemia Risk Adjustment demonstration
Chapter 6.
Arranging data by weeks, months, quarters
Means and variances
Predictability
Denominators
Shewhart, EWMA and GAM control charts without denominators
Shewhart/EWMA charts
Shewhart, EWMA and GAM control charts with denominators
Overdispersion
Charts for quarterly data and data without a first date column
When there are few time periods
Cross-tabulation in wide format
Uncommon count data AEs.
Additional scripts for tabulations and charts
Intervals between uncommon count data events
Negative binomial parameters for control charts when denominators vary
Weighted variance
Linear approximation (Bissell)
Comparisons of simple weighted variance and linear approximation
Chapter 7
Multiple antibiotic-resistant organism (MRO) Prevalence
Antibiotic Usage
Spurious proportions
RIDIT charts
Numerical data
Length of stay (LOS) data
Change point
Assessing agreement
Numerical data agreement
Making Decisions (Decision Analysis)
Investigating Outbreaks, analysis of stratified data
Reviewing stratified data analysis
Outbreak investigation example
Chapter 8.
Overview of Hospital Quality Improvement
Five pillars
Customer
Practitioner
Evidence based system
Manifestations of poor systems
Malpractice
Criminal activity
Substandard performance
Medical error
Bundles and checklists
Discipline and accountability
Analyzing & implementing evidence-based systems
Change management
The Feedback loop
Implementation of the Quality Improvement Process
Obtaining data
Hospital as a network
The New copy of this book will include any supplemental materials advertised. Please check the title of the book to determine if it should include any access cards, study guides, lab manuals, CDs, etc.
The Used, Rental and eBook copies of this book are not guaranteed to include any supplemental materials. Typically, only the book itself is included. This is true even if the title states it includes any access cards, study guides, lab manuals, CDs, etc.