Biostatistics and Epidemiology A Primer for Health and Biomedical Professionals ContentsPreface To The Third Edition AcknowledgmentsChapter The Scientific Method The Logic of Scientific Reasoning Variability of Phenomena Requires Statistical Analysis Inductive Infer

  • Title: Biostatistics and Epidemiology: A Primer for Health and Biomedical Professionals
  • Author: Sylvia Wassertheil-Smoller
  • ISBN: 9780387402925
  • Page: 116
  • Format: Paperback
  • ContentsPreface To The Third Edition AcknowledgmentsChapter 1 The Scientific Method 1.1 The Logic of Scientific Reasoning 1.2 Variability of Phenomena Requires Statistical Analysis 1.3 Inductive Inference Statistics as the Technology of the Scientific Method 1.4 Design of Studies 1.5 How to Quantify Variables 1.6 The Null Hypothesis 1.7 Why Do We Test the Null HypothesisContentsPreface To The Third Edition AcknowledgmentsChapter 1 The Scientific Method 1.1 The Logic of Scientific Reasoning 1.2 Variability of Phenomena Requires Statistical Analysis 1.3 Inductive Inference Statistics as the Technology of the Scientific Method 1.4 Design of Studies 1.5 How to Quantify Variables 1.6 The Null Hypothesis 1.7 Why Do We Test the Null Hypothesis 1.8 Types of Errors 1.9 Significance Level and Types of Error 1.10 Consequences of Type I and Type II ErrorsChapter 2 A Little Bit Of Probability 2.1 What Is Probability 2.2 Combining Probabilities 2.3 Conditional Probability 2.4 Bayesian Probability 2.5 Odds and Probability 2.6 Likelihood Ratio 2.7 Summary of ProbabilityChapter 3 Mostly About Statistics 3.1 Chi Square for 2 x 2 Tables 3.2 McNemar Test 3.3 Kappa 3.4 Description of a Population Use of the Standard Deviation 3.5 Meaning of the Standard Deviation The Normal Distribution 3.6 The Difference Between Standard Deviation and Standard Error 3.7 Standard Error of the Difference Between Two Means 3.8 Z Scores and the Standardized Normal Distribution 3.9 The t Statistic 3.10 Sample Values and Population Values Revisited 3.11 A Question of Confidence 3.12 Confidence Limits and Confidence Intervals 3.13 Degrees of Freedom 3.14 Confidence Intervals for Proportions 3.15 Confidence Intervals Around the Difference Between Two Means 3.16 Comparisons Between Two Groups 3.17 Z Test for Comparing Two Proportions 3.18 t Test for the Difference Between Means of Two Independent Groups Principles 3.19 How to Do a t Test An Example 3.20 Matched Pair t Test 3.21 When Not to Do a Lot of t Tests The Problem of Multiple Tests of Significance 3.22 Analysis of Variance Comparison Among Several Groups 3.23 Principles 3.24 Bonferroni Procedure An Approach to Making Multiple Comparisons 3.25 Analysis of Variance When There Are Two Independent Variables The Two Factor ANOVA 3.26 Interaction Between Two Independent Variables 3.27 Example of a Two Way ANOVA 3.28 Kruskal Wallis Test to Compare Several Groups 3.29 Association and Causation The Correlation Coefficient 3.30 How High Is High 3.31 Causal Pathways 3.32 Regression 3.33 The Connection Between Linear Regression and the Correlation Coefficient 3.34 Multiple Linear Regression 3.35 Summary So FarChapter 4 Mostly About Epidemiology 4.1 The Uses of Epidemiology 4.2 Some Epidemiologic Concepts Mortality Rates 4.3 Age Adjusted Rates 4.4 Incidence and Prevalence Rates 4.5 Standardized Mortality Ratio 4.6 Person Years of Observation 4.7 Dependent and Independent Variables 4.8 Types of Studies 4.9 Cross Sectional Versus Longitudinal Looks at Data 4.10 Measures of Relative Risk Inferences From Prospective Studies the Framingham Study 4.11 Calculation of Relative Risk from Prospective Studies 4.12 Odds Ratio Estimate of Relative Risk from Case Control Studies 4.13 Attributable Risk 4.14 Response Bias 4.15 Confounding Variables 4.16 Matching 4.17 Multiple Logistic Regression 4.18 Confounding By Indication 4.19 Survival Analysis Life Table Methods 4.20 Cox Proportional Hazards Model 4.21 Selecting Variables For Multivariate Models 4.22 Interactions Additive and Multiplicative Models Summary Chapter 5 Mostly About Screening 5.1 Sensitivity, Specificity, and Related Concepts 5.2 Cutoff Point and Its Effects on Sensitivity and SpecificityChapter 6 Mostly About Clinical Trials 6.1 Features of Randomized Clinical Trials 6.2 Purposes of Randomization 6.3 How to Perform Randomized Assignment 6.4 Two Tailed Tests Versus One Tailed Test 6.5 Clinical Trial as Gold Standard 6.6 Regression Toward the Mean 6.7 Intention to Treat Analysis 6.8 How Large Should the Clinical Trial Be 6.9 What Is Involved in Sample Size Calculation 6.10 How to Calculate Sample Size for the Difference Between Two Proportions 6.11 How to Calculate Sample Size for Testing the Difference Between Two MeansChapter 7 Mostly About Quality Of Life 7.1 Scale Construction 7.2 Reliability 7.3 Validity 7.4 Responsiveness 7.5 Some Potential PitfallsChapter 8 Mostly About Genetic Epidemiology 8.1 A New Scientific Era 8.2 Overview of Genetic Epidemiology 8.3 Twin Studies 8.4 Linkage and Association Studies 8.5 LOD Score Linkage Statistic 8.6 Association Studies 8.7 Transmission Disequilibrium Tests TDT 8.8 Some Additional Concepts and Complexities of Genetic StudiesChapter 9 Research Ethics And Statistics 9.1 What does statistics have to do with it 9.2 Protection of Human Research Subjects 9.3 Informed Consent 9.4 Equipoise 9.5 Research Integrity 9.6 Authorship policies 9.7 Data and Safety Monitoring Boards 9.8 SummaryPostscript A Few Parting Comments On The Impact Of Epidemiology On Human Lives Appendix A Critical Values Of Chi square, Z, And T Appendix B Fisher S Exact Test Appendix C Kruskal wallis Nonparametric Test To Compare Several Groups Appendix D How To Calculate A Correlation Coefficient Appendix E Age adjustment Appendix F Confidence Limits On Odds Ratios Appendix G J Or U Shaped Relationship Between Two Variables Appendix H Determining Appropriateness Of Change Scores Appendix I Genetic Principles References Suggested Readings Index

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