Americans are bombarded with statistical data each and every day, and healthcare professionals are no exception. All segments of healthcare rely on data provided by insurance companies, consultants, research firms, and the federal government to help them make a host of decisions regarding the delivery of medical services. But while these health professionals rely on data, do they really make the best use of the information? Not if they fail to understand whether the assumptions behind the formulas generating the numbers make sense. Not if they dont understand that the world of healthcare is flooded with inaccurate, misleading, and even dangerous statistics.
Statistical Analysis for Decision Makers in Healthcare: Understanding and Evaluating Critical Information in a Competitive Market, Second Edition explains the fundamental concepts of statistics, as well as their common uses and misuses. Without jargon or mathematical formulas, nationally renowned healthcare expert and author, Jeff Bauer, presents a clear verbal and visual explanation of what statistics really do. He provides a practical discussion of scientific methods and data to show why statistics should never be allowed to compensate for bad science or bad data.
Relying on real-world examples, Dr. Bauer stresses a conceptual understanding that empowers readers to apply a scientifically rigorous approach to the evaluation of data. With the tools he supplies, you will learn how to dismantle statistical evidence that goes against common sense. Easy to understand, practical, and even entertaining, this is the book you wish you had when you took statistics in college and the one you are now glad to have to defend yourself against the abundance of bad studies and misinformation that might otherwise corrupt your decisions.
| Introduction |
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SECTION I The Scientific Foundations of Statistical Analysis |
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Scientific Method: The Language of Statistical Studies |
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3 | (20) |
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Science as a Universal Language |
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5 | (3) |
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The Attributes of Scientific Method |
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8 | (6) |
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8 | (1) |
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Science Is Free of Values |
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9 | (1) |
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10 | (1) |
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11 | (2) |
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13 | (1) |
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The Attributes of a Scientific Report |
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14 | (9) |
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15 | (1) |
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Introduction and Problem Statement |
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16 | (1) |
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17 | (1) |
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18 | (1) |
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19 | (1) |
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20 | (3) |
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Experimentation: The Foundation of Scientific Studies |
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23 | (16) |
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24 | (10) |
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26 | (2) |
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28 | (1) |
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Randomly Select the Study Sample from the Population |
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29 | (2) |
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Randomly Assign the Study Sample to Experimental and Control Groups |
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31 | (1) |
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Establish Pretest (Baseline) Measures for Experimental and Control Groups |
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32 | (1) |
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Administer an Experimental Effect to an Experimental Group Only |
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33 | (1) |
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Establish Posttest Measures for Experimental and Control Groups |
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33 | (1) |
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Analyze Experimental Data for Statistical Significance |
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33 | (1) |
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34 | (5) |
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SECTION II The Fundamental Importance of Data |
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Numbers Good and Bad: How to Judge the Quality of Data |
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39 | (18) |
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Letting the Numbers Speak for Themselves |
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40 | (2) |
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42 | (10) |
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43 | (2) |
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45 | (1) |
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46 | (1) |
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47 | (1) |
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47 | (2) |
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49 | (1) |
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49 | (1) |
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49 | (1) |
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50 | (2) |
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Types and Levels of Measurement |
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52 | (5) |
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53 | (1) |
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54 | (3) |
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Samples and Surveys: How Numbers Should Be Collected |
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57 | (20) |
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57 | (1) |
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58 | (4) |
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62 | (2) |
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64 | (1) |
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65 | (12) |
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66 | (1) |
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67 | (2) |
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69 | (2) |
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71 | (6) |
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SECTION III The Different Types of Statistics |
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Descriptive Statistics: The Foundation of Comparisons |
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77 | (20) |
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The First Step: What Have We Here? |
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78 | (5) |
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78 | (2) |
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80 | (3) |
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Measures of Central Tendency |
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83 | (6) |
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84 | (1) |
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84 | (3) |
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87 | (2) |
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89 | (8) |
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89 | (2) |
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91 | (6) |
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Inferential Statistics: Studies of Differences |
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97 | (28) |
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98 | (5) |
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The Central Limit Theorem |
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103 | (1) |
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Standard Error (of the Mean) |
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104 | (2) |
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106 | (1) |
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106 | (8) |
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Accepting the Null Hypothesis |
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111 | (1) |
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Rejecting the Null Hypothesis |
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111 | (3) |
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114 | (9) |
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116 | (1) |
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F Test (Analysis of Variance) |
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116 | (3) |
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119 | (4) |
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123 | (2) |
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Relational Statistics: Studies of Relationships |
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125 | (12) |
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Direction of Relationships |
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126 | (1) |
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Strength of Relationships |
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127 | (3) |
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130 | (2) |
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130 | (2) |
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Nonparametric Correlation |
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132 | (1) |
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Linear versus Nonlinear Relationships |
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132 | (2) |
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Correlation and Causality |
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134 | (3) |
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Explanatory Statistics: Studies of Causality |
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137 | (16) |
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138 | (2) |
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The Basic Equation of Multivariate Linear Regression |
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140 | (1) |
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141 | (3) |
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Evaluating a Multivariate Linear Regression Model |
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144 | (6) |
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Refining Regression Models |
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150 | (1) |
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Applications of Explanatory Statistics |
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150 | (3) |
| Postscript: Statistics in Perspective |
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153 | (2) |
| Index |
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155 | (2) |
| About the Author |
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157 | |
Dr. Bauer is an internationally recognized health futurist and medical economist. As an independent industry thought leader, he forecasts the evolution of health care and develops practical approaches to improving the medical sector of the American economy. He is widely known for his specific proposals to create efficient, effective health care through multi-stakeholder partnerships and other initiatives focused in the private sector.
Dr. Bauer has more than 275 publications on health care delivery. His latest books are a 25th anniversary update of his best seller, Not What the Doctor Ordered: Liberating Caregivers and Empowering Consumers for Successful Health Reform (2019), Paradox and Imperatives in Health Care: Redirecting Reform for Efficiency and Effectiveness (2015), and Upgrading Leaderships Crystal Ball: Five Reasons Why Forecasting Must Replace Predicting and How to Make the Strategic Change in Business and Public Policy (2014). Previous books include Statistical Analysis for Health Care Decision-Makers (CRC Press, 2009) and Telemedicine and the Reinvention of Health Care: The Seventh Revolution in Medicine (McGraw-Hill, 1999).
As a consultant, he has assisted hundreds of provider, purchaser, and payer organizations with strategic planning and performance improvement initiatives. He served as Vice President for Health Care Forecasting and Strategy for ACS, a Xerox Company, from 1999 to 2010. His own consulting firm, The Bauer Group, specialized in consumer-focused strategic planning and clinical affiliation agreements for multi-hospital networks from 1984 to 1992.
In addition, Dr. Bauer was a full-time teacher and administrator at the University of Colorado Health Sciences Center in Denver from 1973 to 1984, where he held appointments as associate professor and Assistant Chancellor for Planning and Program Development. He also served for four years as Health Policy Adviser to Colorado Governor Richard D. Lamm. From 1992 to 1999, Dr. Bauer was a visiting professor in Administrative Medicine at the Medical School of the University of Wisconsin-Madison.
He received his Ph.D. in economics from the University of Colorado-Boulder. He graduated from Colorado College in Colorado Springs with a B.A. in economics and completed a certificate in political studies at the University of Paris (France). During his academic career, he was a Boettcher Scholar, a Ford Foundation Independent Scholar, a Fulbright Scholar (Switzerland), and a Kellogg Foundation National Fellow. He is an honorary Fellow in the American Academy of Nurse Practitioners. Dr. Bauer lives in Madison, Wisconsin, where he spends his spare time painting (conceptual art in acrylics) and playing the viola da gamba (precursor to the cello). He is an active member of the Board of Directors of the Madison Symphony Orchestra.
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