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Statistical Methods for Hospital Monitoring with R
Statistics in Practice
Morton, Anthony/Mengersen, Kerrie L/Playford, Geoffrey et al
ISBN/EAN: 9781118596302
Umbreit-Nr.: 5123943
Sprache:
Englisch
Umfang: XXI, 404 S.
Format in cm: 2.3 x 25.1 x 17.7
Einband:
gebundenes Buch
Erschienen am 23.08.2013
Auflage: 1/2013
- Zusatztext
- InhaltsangabeR Libraries x R Functions xi Preface xvi Introduction 1 0.1 Overview and rationale for this book 1 0.1.1 Motivation for the book 1 0.1.2 Why R? 2 0.1.3 Other reading for R 2 0.2 What methods are covered in the book? 3 0.3 Structure of the book 4 0.4 Using R 5 0.4.1 Entering data 6 0.4.2 Dates 8 0.4.3 Exporting data 10 0.5 Further notes 11 0.6 A brief introduction to rprogs charts and figures 11 0.6.1 What if there is no date column? 18 0.7 Appendix menus 20 0.7.1 IMenu() 20 0.7.2 CCMenu() 21 1 Introduction to analysis of binary and proportion data 24 1.1 Single proportion, samples and population 24 1.1.1 Calculating the confidence interval 26 1.1.2 Comparison with an expected rate 27 1.2 Likelihood ratio (Bayes factor) & supported range 29 1.3 Confidence intervals for a series of proportions 30 1.4 Difference between two proportions 33 1.4.1 Confidence intervals 33 1.4.2 Hypothesis test 35 1.4.3 The twoproportions function 37 1.5 Introducing a Bayesian approach 39 1.6 When the data are not just one or two independent samples 39 1.6.1 More than two independent proportions 40 1.6.2 Example 1, yearly data 40 1.6.3 Example 2, hospital data 43 1.6.4 Prop test and small samples 47 1.7 Summarising stratified proportion data 48 1.8 Stratified proportion data, differences between rates 50 1.8.1 Yearly data 52 1.8.2 Hospital data 54 1.9 MantelHaenszel, homogeneity and trend tests 54 1.9.1 Yearly data 56 1.9.2 Data stratified by hospital 59 1.10 Stratified rates and overdispersion 63 2 The analysis of aggregated binary data 67 2.1 Riskadjustment 68 2.1.1 Using stratification 68 2.1.2 Using logistic regression 70 2.2 Discrimination and calibration 71 2.3 Using 2005-06 data 76 2.3.1 Displaying and analysing data from multiple institutions 77 2.3.2 Tabulations 78 2.3.3 Funnel plot and plot of multiple confidence intervals 83 2.4 When the Es are not fixed 99 2.5 Complex Surgical Site Infections 102 2.5.1 Funnel plot analysis 102 2.5.2 Shrinkage analysis 104 2.6 Complex SSI risk-adjustment discrimination 106 2.7 Appendix 1 - Further tabulation methods 106 2.8 Appendix 2 - SMR CIs and tests, further scripts. Hospital expected values from other hospitals in group 109 3 Sequential binary data 116 3.1 CUSUM and related charts for binary data 117 3.2 Cumulative Observed-Expected (O-E) chart and combined CUSUM and O-E chart 120 3.3 Cumulative funnel plot and combined CUSUM and funnel plot 120 3.4 Example 121 3.5 Including risk adjustment 124 3.6 CUSUM chart 125 3.7 Cumulative observed minus expected (O-E) chart 125 3.8 Funnel plot 127 3.9 Discrimination and calibration of risk adjustment 128 3.10 Shewhart P chart and EWMA chart 132 3.11 Note on the Run-sum chart 135 3.12 The EWMA chart 135 3.13 Plotting the expected values 138 3.14 Using a spline or generalised additive model (GAM) chart 139 3.15 When there are few time periods 141 3.16 Charts for quarterly data and data without a first date column 143 3.17 Charts for composite measures 146 3.18 Additional tabulations 146 3.19 The issue of under-reporting 151 3.20 New CUSUM and EWMA charts, low-rate data 151 3.20.1 The risk-adjusted Bernoulli CUSUM 153 3.20.2 The EWMA 156 3.20.3 Quarterly rates 157 3.21 Intervals between uncommon binary adverse events 159 3.22 Appendix, proposed EWMA for low rate data 164 4 Introduction to analysis of count and rate data 168 4.1 Introduction 168 4.2 Rate and count data 169 4.3 Single count or rate 169 4.3.1 Confidence interval 170 4.3.2 Significance test 171 4.4 Confidence limits for columns of counts and rates 173 4.5 Two independent rates 175 4.5.1 Confidence interval 175 4.5.2 Hypothesis test 176 4.5.3 Bayesian approach 177 4.6 Chisquared and trend tests for count and rate
- Autorenportrait
- InhaltsangabeR Libraries x R Functions xi Preface xvi Introduction 1 0.1 Overview and rationale for this book 1 0.1.1 Motivation for the book 1 0.1.2 Why R? 2 0.1.3 Other reading for R 2 0.2 What methods are covered in the book? 3 0.3 Structure of the book 4 0.4 Using R 5 0.4.1 Entering data 6 0.4.2 Dates 8 0.4.3 Exporting data 10 0.5 Further notes 11 0.6 A brief introduction to rprogs charts and figures 11 0.6.1 What if there is no date column? 18 0.7 Appendix menus 20 0.7.1 IMenu() 20 0.7.2 CCMenu() 21 1 Introduction to analysis of binary and proportion data 24 1.1 Single proportion, samples and population 24 1.1.1 Calculating the confidence interval 26 1.1.2 Comparison with an expected rate 27 1.2 Likelihood ratio (Bayes factor) & supported range 29 1.3 Confidence intervals for a series of proportions 30 1.4 Difference between two proportions 33 1.4.1 Confidence intervals 33 1.4.2 Hypothesis test 35 1.4.3 The twoproportions function 37 1.5 Introducing a Bayesian approach 39 1.6 When the data are not just one or two independent samples 39 1.6.1 More than two independent proportions 40 1.6.2 Example 1, yearly data 40 1.6.3 Example 2, hospital data 43 1.6.4 Prop test and small samples 47 1.7 Summarising stratified proportion data 48 1.8 Stratified proportion data, differences between rates 50 1.8.1 Yearly data 52 1.8.2 Hospital data 54 1.9 MantelHaenszel, homogeneity and trend tests 54 1.9.1 Yearly data 56 1.9.2 Data stratified by hospital 59 1.10 Stratified rates and overdispersion 63 2 The analysis of aggregated binary data 67 2.1 Riskadjustment 68 2.1.1 Using stratification 68 2.1.2 Using logistic regression 70 2.2 Discrimination and calibration 71 2.3 Using 2005-06 data 76 2.3.1 Displaying and analysing data from multiple institutions 77 2.3.2 Tabulations 78 2.3.3 Funnel plot and plot of multiple confidence intervals 83 2.4 When the Es are not fixed 99 2.5 Complex Surgical Site Infections 102 2.5.1 Funnel plot analysis 102 2.5.2 Shrinkage analysis 104 2.6 Complex SSI risk-adjustment discrimination 106 2.7 Appendix 1 - Further tabulation methods 106 2.8 Appendix 2 - SMR CIs and tests, further scripts. Hospital expected values from other hospitals in group 109 3 Sequential binary data 116 3.1 CUSUM and related charts for binary data 117 3.2 Cumulative Observed-Expected (O-E) chart and combined CUSUM and O-E chart 120 3.3 Cumulative funnel plot and combined CUSUM and funnel plot 120 3.4 Example 121 3.5 Including risk adjustment 124 3.6 CUSUM chart 125 3.7 Cumulative observed minus expected (O-E) chart 125 3.8 Funnel plot 127 3.9 Discrimination and calibration of risk adjustment 128 3.10 Shewhart P chart and EWMA chart 132 3.11 Note on the Run-sum chart 135 3.12 The EWMA chart 135 3.13 Plotting the expected values 138 3.14 Using a spline or generalised additive model (GAM) chart 139 3.15 When there are few time periods 141 3.16 Charts for quarterly data and data without a first date column 143 3.17 Charts for composite measures 146 3.18 Additional tabulations 146 3.19 The issue of under-reporting 151 3.20 New CUSUM and EWMA charts, low-rate data 151 3.20.1 The risk-adjusted Bernoulli CUSUM 153 3.20.2 The EWMA 156 3.20.3 Quarterly rates 157 3.21 Intervals between uncommon binary adverse events 159 3.22 Appendix, proposed EWMA for low rate data 164 4 Introduction to analysis of count and rate data 168 4.1 Introduction 168 4.2 Rate and count data 169 4.3 Single count or rate 169 4.3.1 Confidence interval 170 4.3.2 Significance test 171 4.4 Confidence limits for columns of counts and rates 173 4.5 Two independent rates 175 4.5.1 Confidence interval 175 4.5.2 Hypothesis test 176 4.5.3 Bayesian approach 177 4.6 Chisquared and trend tests for count and rate