Biostatistics for Population Health: A Primer 1st Edition by Lisa M. Sullivan, ISBN-13: 978-1284194265

Biostatistics for Population Health: A Primer 1st Edition by Lisa M. Sullivan, ISBN-13: 978-1284194265

[PDF eBook eTextbook]

• Publisher: ‎ Jones & Bartlett Learning; 1st edition (April 16, 2020)
• Language: ‎ English
• ISBN-10: ‎ 1284194264
• ISBN-13: ‎ 978-1284194265

Written for undergraduate and graduate students with little or no mathematical background, Biostatistics for Population Health: A Primer offers current and future health professionals a clear, and accessible approach to learning the basic tools and techniques necessary to conduct biostatistical analyses and the professional confidence to critically evaluate and interpret biostatistical findings. Each unit begins with a contemporary population health issue (e.g., the opioid crisis, physical inactivity among children, diabetes) and raises questions that require the use of techniques discussed in that unit. Each technique, in turn, is illustrated with realistic, contemporary examples (e.g. vaping) to pique student interest. By the end of the unit, students are encouraged to apply the techniques to address the questions that were raised.

Key Features:

• Contemporary, realistic examples and straightforward approach makes material accessible to students with minimal background.

• Statistical and mathematical notation is kept to a minimum with focus on application and interpretation.

• Key points summarized at the end of each unit and a comprehensive glossary provide helpful references for students

• Concise length makes this text an easy and affordable supplement for a variety of courses.

• The instructor guide offers helpful suggestions and resources to engage students and encourage active learning.

Table of Contents:

Cover
Title Page
Copyright Page
Contents
Acknowledgment
Introduction
Unit 1 Summarizing Data for Decision Making
A Population Health Issue—The Opioid Crisis in the United States
Populations and Samples
1.1 Data, Measurement, and Variables
Dichotomous Variables
Categorical Variables
Ordinal Variables
Continuous Variables
1.2 Descriptive Statistics
Descriptive Statistics for Dichotomous Variables
Descriptive Statistics for Categorical and Ordinal Variables
Descriptive Statistics for Continuous or Measurement Variables
1.3 Risks, Rates, and Ratios and Their Use in Population Health
Risks
Rates
Ratios
1.4 Graphical Displays of Data
Distributions
Comparing Groups
Summarizing Associations
Trends over Time
1.5 Summary
Key Points
References
Unit 2 Associations Between Two Variables
A Population Health Issue—Physical Inactivity Among Children Worldwide Is Raising Concerns
2.1 Concepts and Applications of Probability
2.2 Screening and Diagnostic Tests
Performance Measures of Screening Tests
Bayes’ Rule
2.3 Probability Models
The Normal Distribution
The Standard Normal Distribution
Using z Scores for Comparisons
2.4 Estimation
The Central Limit Theorem
Confidence Interval Estimates
Confidence Interval Estimates for a Population Mean or a Population Proportion
Confidence Intervals Comparing Means in Two Independent Groups
Confidence Intervals Comparing Means in Two Matched or Paired Groups
Confidence Intervals Comparing Proportions, Risks, and Rates in Two Independent Groups
Confidence Intervals Comparing Proportions and Risks in Two Matched or Paired Groups
2.5 Tests of Hypothesis for Means and Proportions
Errors in Statistical Tests
Hypothesis Tests for a Population Mean or a Population Proportion
Hypothesis Tests Comparing Means in Two Independent Groups
Hypothesis Tests Comparing Means in Two Matched or Paired Groups
Hypothesis Tests Comparing Means in More than Two Groups
Hypothesis Tests Comparing Proportions in Two or More Independent Groups
Hypothesis Tests Comparing Proportions in Two Matched or Paired Groups
2.6 Correlation Analysis
Estimating a Correlation
Confidence Interval Estimate for a Population Correlation
Hypothesis Test for a Population Correlation
2.7 Summary
Key Points
References
Unit 3 Multivariable Analysis
A Population Health Issue—Diabetes Prevalence Increasing Worldwide
3.1 Bias, Confounding, and Effect Modification
3.2 Approaches to Address Confounding
3.3 Data Considerations for Multivariable Modelling
3.4 Multivariable Linear Regression Analysis
Crude, Unadjusted Linear Regression Models
Multivariable, Adjusted Linear Regression Models
3.5 Multivariable Logistic Regression Analysis
Crude, Unadjusted Logistic Regression Models
Multivariable, Adjusted Logistic Regression Models
3.6 Survival Analysis
Estimating a Survival Function
Comparing Survival in Two or More Independent Groups
Cox Proportional Hazards Regression Analysis
3.7 Summary
Key Points
References
Glossary
Index

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