| Description |
xxxii, 332 pages : color illustrations ; 28 cm |
| Series |
Public health professional |
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Public health professional.
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| Contents |
Contents note continued: Analysis of Covariance and Adjusted Means -- Cause Versus Association -- Diagnostics -- Variable Selection -- Forecasting or Prediction -- Covariate-Adjustment -- Planning a Multiple Regression Study -- Summary -- Key Terms -- Practice with Data -- Practice with Concepts -- ch. 4 Categorical Data: Comparisons and Associations -- Describing the Data -- Graphical Summaries: Bar Graphs and Pie Charts -- Numerical Summaries: Prevalence, Risk, and Odds -- One-Group Studies with a Dichotomous Outcome -- One-Sample Proportion: Hypothesis Test -- One-Sample Proportion: Confidence Intervals -- Planning a One-Sample Study -- Planning for Estimating a Proportion -- Planning for Making Comparisons with a Proportion -- One-Sample Proportion: Exact Methods -- Multiple-Group Studies -- Proportions in a Contingency Table -- Hypothesis Test for Two Dichotomous Variables (2X2 Contingency Table) -- Hypothesis Test for Two Nominal Variables (RXC Contingency Table) -- |
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Contents note continued: Concepts for Statistical Inference -- Estimation (Confidence Intervals) -- Hypothesis Testing (p-Values) -- Type 1 Error -- Type 2 Error -- Power -- Planning Studies -- Estimation Study -- Testing Study -- Summary -- Key Terms -- Practice with Concepts -- ch. 2 Continuous Data: Making Comparisons -- Describing the Data -- Numerical Summaries: Measures of Center and Spread -- Graphical Summaries for a Continuous Outcome -- Histograms -- Box-and-Whisker Plots -- Plotting Means -- One-Group Studies with a Continuous Outcome -- One-Sample Hypothesis Test: Historical Controls -- One-Sample Hypothesis Test: Within Subject Controls -- One-Sample Confidence Intervals for the Mean -- Planning a One-Sample Study -- Planning for Estimating a Mean -- Planning for Making Comparison with a Mean -- One-Sample Median: Nonparametric Hypothesis Test -- Two-Sample Studies with a Continuous Outcome -- Two Sample Means: Equal Variances -- Hypothesis Test -- |
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Contents note continued: Confidence Intervals -- Two Sample Means: Unequal Variances -- Hypothesis Test -- Confidence Interval -- Planning a Two-Sample Study -- Planning for Estimating a Mean -- Planning for Making a Comparison with a Mean -- Two Samples: Nonparametric Hypothesis Test -- Multiple Group Studies with a Continuous Outcome -- Partitioning the Variance: The ANOVA Table -- Multiple Sample Means: Overall Hypothesis Test -- Multiple Sample Means: Specific Hypothesis Tests -- Multiple Sample Means: Multiple Comparisons -- Planning a Multiple-Sample Study -- Multiple Samples: Nonparametric Hypothesis Tests -- Summary -- Key Terms -- Practice with Data -- Practice with Concepts -- ch. 3 Continuous Data: Correlation and Regression -- Describing the Data -- Graphical Summaries: Scatterplots -- Numerical Summaries: Correlation -- Inferences for the Correlation Coefficient -- Simple Linear Regression: One Response and One Explanatory Variable -- |
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Contents note continued: Defining the Linear Relationship -- The Model -- The ANOVA Table -- Overall Hypothesis Test -- Specific Hypothesis Test for Slope -- Specific Confidence Interval for Slope -- Prediction Equation -- Diagnostics -- Violations to Constant Variance -- Violations of Normality -- Violations of Linearity -- Polynomial Regression -- Implementing Regression Diagnostis -- Planning a Correlation Study -- Multiple Linear Regression: One Response and Multiple Regressors -- The Model (Two Regressors) -- Getting the Intercept and Slopes -- The ANOVA Table (Two Regressors) -- Overall Hypothesis Test -- Adjusted Sum of Squares and Partial Correlations (Two Regressors) -- Specific Hypothesis Tests for Slopes (Two Regressors) -- Specific Confidence Intervals for Slopes (Two Regressors) -- Prediction Equation -- More Than Two Regressors -- Regressors Do Not Have to Be Continuous -- Interpreting the Results of a Multiple Linear Regression -- Confounding -- |
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Contents note continued: Describing the Data -- Numerical and Graphical Summaries: Counts -- Numerical Summaries for Count Outcomes -- Person-Time and Event Rates -- Age-Adjusted Event Rates -- Graphical Summaries: Bar and Line Graphs -- One-Group Studies with a Count Outcome -- Statistical Inference for an Event Rate -- Confidence Interval for an Event Rate -- Event Rate in One-Sample: Exact Test -- Multiple Group Studies -- Rate Ratio: Two Groups -- Rate Ratios: Multiple Groups -- Count Dependent Variable with Multiple Regressors: Poisson Regression -- The Model -- Getting the Intercept and Slopes -- Hypothesis Tests -- Overall Hypothesis Test -- Hypothesis Test for Effects -- Specific Hypothesis Tests for the Slopes -- Adjusted Rate Ratios -- Interpreting Adjusted Rate Ratios for Nominal Regressors -- Interpreting the Adjusted Rate Ratio for a Continuous Regressor -- Interpreting the Results of a Poisson Regression -- Regressors as Explanatory Variables -- |
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Contents note continued: Evidence of Effect Modification -- Dichotomous Dependent Variable with Multiple Regressors: Logistic Regression -- The Model -- Getting the Slopes and Intercept -- Hypothesis Tests -- Overall Hypothesis Test -- Hypothesis Test for Effects -- Specific Hypothesis Tests for the Slopes -- Adjusted Odds Ratios -- Interpreting Adjusted Odds Ratios for Nominal Regressors -- Interpreting the Adjusted Odds Ratio for a Continuous Regressor -- Interpreting the Results of a Logistic Regression -- Regressors as Explanatory Variables -- Regressors as Confounders -- Interactions -- Predicting Probabilities -- Why the Odds Ratio? -- Diagnostics -- Pearson, Deviance, Hosmer-Lemeshow Hypothesis Tests -- Using ROC Curves in Logistic Regression -- Residuals -- Variable Selection -- Forecasting/Prediction -- Covariate-Adjustment -- Strategies for Performing the Regression -- Summary -- Key Terms -- Practice with Data -- Practice with Concepts -- ch. 6 Count Data -- |
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Contents note continued: Hypothesis Test for Effects -- Specific Hypothesis Tests -- Adjusted Hazard Ratios -- Interpreting the Results of a Proportional-Hazards Regression -- Diagnostics -- Variable Selection -- Forecasting/Prediction -- Covariate-Adjustment -- Strategies for Performing the Regression -- Summary -- Key Terms -- Practice with Data -- Practice with Concepts -- APPENDIX A Correlated Data: Longitudinal Studies -- Describing the Data -- The Assumption: Independence -- Impacts of Violating the Assumption -- Correcting for the Violation: Linear Mixed Models -- APPENDIX B Correlated Data: Clustering and Hierarchies -- More Examples of Cluster Trials -- The Assumption: Independence -- Impacts of Violating the Assumption -- Correcting for the Violation: GEEs and GLMMs |
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Contents note continued: Hypothesis Test for a Nominal and Ordinal Variable (RXC Contingency Table) -- Hypothesis Test for Two Ordinal Variables (RXC Contingency Table) -- Multiple-Group Proportions: Exact Tests -- Measures of Association for a Dichotomous Outcome -- Difference in Proportions, Risks, and Prevalence Rates -- Ratio of Proportions, Risks, and Prevalence Rates -- Odds Ratio -- Interpretation of Ratios: Beyond Two Groups -- Planning a Two-Sample Study with a Dichotomous Outcome -- Summary -- Key Terms -- Practice with Data -- Practice with Concepts -- ch. 5 A Dichotomous Outcome: Confounding & Regression -- Describing the Data -- Numerical Summaries: Sensitivity and Specificity -- Graphical Summaries: Receiver Operator Characteristics (ROC) Curves -- Confounding and Effect Modification -- Stratified Contingency Tables -- Identifying Effect Modification: The Breslow-Day Test -- No Evidence of Effect Modification: Mantel-Haenszel Odds Ratio -- |
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Contents note continued: Regressors as Confounders -- Diagnostics -- Hypothesis Tests for Model Fit -- Overdispersion -- Residuals -- What if Poisson Regression is not Appropriate? -- Variable Selection -- Forecasting/Prediction -- Covariate-Adjustment -- Strategies for Performing the Regression -- Summary -- Key Terms -- Practice with Data -- Practice with Concepts -- ch. 7 Time-to-Event Analysis -- Describing the Data -- Numerical and Graphical Summaries: Hazard Functions -- Numerical and Graphical Summaries: Survival Functions -- Censoring -- Censoring Defined -- Data Structure with Censored Observations -- The Kaplan-Meier Method -- Life-Table Method -- Group Comparisons -- Comparing Groups with Kaplan-Meier Curves -- Hazard Ratios -- The Log-Rank Test: Comparing Two Groups -- The Log-Rank Test: Comparing Multiple Groups -- Planning a Two-Sample Study -- Proportional Hazards Regression -- Model -- Getting the Slopes -- Hypothesis Tests -- Overall Hypothesis Test -- |
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Machine generated contents note: ch. 1 An Overview of Statistical Concepts -- What Subjects to Study: Populations and Samples -- Defining the Subjects and Population -- Samples: A Subset of the Population -- Strategies for Obtaining a Random Sample of the Population -- How to Study the Subjects: Study Designs -- What to Measure on the Subjects: Variable Types -- Categorical Variables -- Ordinal Variables -- Nominal Variables -- Dichotomous Variables -- Continuous Variables -- Count Variables -- How to Describe the Subjects: Numerical Summaries -- Categorical Variables -- Continuous Variables -- Notations for Parameters and Statistics -- How to Describe the Subjects: Graphical Summaries -- Categorical Variables -- Bernoulli and Binomial Distributions -- Continuous Variables -- The Normal Distribution -- Skewed Distributions -- Count Variables -- Percentiles -- Variability -- Variability Within and Between Subjects -- Variability Between Samples -- The Central Limit Theorem -- |
| Summary |
BIOSTATISTICS: AN APPLIED INTRODUCTION FOR THE PUBLIC HEALTH PRACTITIONER is designed to help public health researchers, practitioners, and students understand and apply essential biostatistics concepts. This innovative new text emphasizes real-world public health problems and the research questions they inspire. This text provides a unique introduction to statistical concepts and methods used by working professionals during investigations. Unlike other texts that assume a strong knowledge of mathematics or rely heavily on formulas, BIOSTATISTICS consistently emphasizes the public health context, making even complex material both accessible and relevant. The first chapter introduces common statistical terminology by explaining them in clear language, while subsequent chapters explore the most useful and versatile statistical methods for a variety of public health research questions |
| Bibliography |
Includes bibliographical references and index |
| Subject |
Biometry.
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Medical statistics.
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Medicine -- Research -- Statistical methods.
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Biometry -- methods.
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Biostatistics -- methods.
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Biomedical Research -- methods.
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Biometry -- methods.
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biometry |
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Public Health -- methods.
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statistics |
| Author |
Scutchfield, F. Douglas.
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| LC no. |
2011925054 |
| ISBN |
1111035148 |
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9781111035143 |
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