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Produktbild: Clinical Epidemiology and Biostatistics

Clinical Epidemiology and Biostatistics A Primer for Clinical Investigators and Decision-Makers

Fr. 181.00

inkl. gesetzl. MwSt., Versandkostenfrei

Beschreibung

Produktdetails

Einband

Taschenbuch

Erscheinungsdatum

08.10.2011

Verlag

Springer Berlin

Seitenzahl

286

Maße (L/B/H)

24.2/17/1.7 cm

Gewicht

524 g

Auflage

Softcover reprint of the original 1st ed. 1988

Sprache

Englisch

ISBN

978-3-642-64814-4

Beschreibung

Produktdetails

Einband

Taschenbuch

Erscheinungsdatum

08.10.2011

Verlag

Springer Berlin

Seitenzahl

286

Maße (L/B/H)

24.2/17/1.7 cm

Gewicht

524 g

Auflage

Softcover reprint of the original 1st ed. 1988

Sprache

Englisch

ISBN

978-3-642-64814-4

Herstelleradresse

Springer-Verlag KG
Sachsenplatz 4-6
1201 Wien
AT

Email: ProductSafety@springernature.com

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  • Produktbild: Clinical Epidemiology and Biostatistics
  • I Epidemiologic Research Design.- 1: Introduction.- 1.1 The Compatibility of the Clinical and Epidemiologic Approaches.- 1.2 Clinical Epidemiology: Main Areas of Interest.- 1.3 Historical Roots.- 1.4 Current and Future Relevance: Controversial Questions and Unproven Hypotheses.- 2: Measurement.- 2.1 Types of Variables and Measurement Scales.- 2.2 Sources of Variation in a Measurement.- 2.3 Properties of Measurement.- 2.4 “Hard” vs “Soft” Data.- 2.5 Consequences of Erroneous Measurement.- 2.6 Sources of Data.- 3: Rates.- 3.1 What is a Rate?.- 3.2 Prevalence and Incidence Rates.- 3.3 Stratification and Adjustment of Rates.- 3.4 Concluding Remarks.- 4: Epidemiologic Research Design: an Overview.- 4.1 The Research Objective: Descriptive vs Analytic Studies.- 4.2 Exposure and Outcome.- 4.3 The Three Axes of Epidemiologic Research Design.- 4.4 Concluding Remarks.- 5: Analytic Bias.- 5.1 Validity and Reproducibility of Exposure-Outcome Associations.- 5.2 Internal and External Validity.- 5.3 Sample Distortion Bias.- 5.4 Information Bias.- 5.5 Confounding Bias.- 5.6 Reverse Causality (“Cart-vs-Horse”) Bias.- 5.7 Concluding Remarks.- 6: Observational Cohort Studies.- 6.1 Research Design Components.- 6.2 Analysis of Results.- 6.3 Bias Assessment and Control.- 6.4 Effect Modification and Synergism.- 6.5 Advantages and Disadvantages of Cohort Studies.- 7: Clinical Trials.- 7.1 Research Design Components.- 7.2 Assignment of Exposure (Treatment).- 7.3 Blinding in Clinical Trials.- 7.4 Analysis of Results.- 7.5 Interpretation of Results.- 7.6 Ethical Considerations.- 7.7 Advantages and Disadvantages of Clinical Trials.- 8: Case-Control Studies.- 8.1 Introduction.- 8.2 Research Design Components.- 8.3 Analysis of Results.- 8.4 Bias Assessment and Control.- 8.5 Advantages and Disadvantages of Case-Control Studies.- 9: Cross-Sectional Studies.- 9.1 Introduction.- 9.2 Research Design Components.- 9.3 Analysis of Results.- 9.4 Bias Assessment and Control.- 9.5 “Pseudo-Cohort” Cross-Sectional Studies.- 9.6 Advantages, Disadvantages, and Uses of Cross-Sectional Studies.- II Biostatistics.- 10: Introduction to Statistics.- 10.1 Variables.- 10.2 Populations, Samples, and Sampling Variation.- 10.3 Description vs Statistical Inference.- 10.4 Statistical vs Analytic Inference.- 11: Descriptive Statistics and Data Display.- 11.1 Continuous Variables.- 11.2 Categorical Variables.- 11.3 Concluding Remarks.- 12: Hypothesis Testing and P Values.- 12.1 Formulating and Testing a Research Hypothesis.- 12.2 The Testing of Ho.- 12.3 Type II Error and Statistical Power.- 12.4 Bayesian vs Frequentist Inference.- 13: Statistical Inference for Continuous Variables.- 13.1 Repetitive Sampling and the Central Limit Theorem.- 13.2 Statistical Inferences Using the t-Distribution.- 13.3 Calculation of Sample Sizes.- 13.4 Nonparametric Tests of Two Means.- 13.5 Comparing Three or More Means: Analysis of Variance.- 13.6 Control for Confounding Factors.- 14: Statistical Inference for Categorical Variables.- 14.1 Introduction to Categorical Data Analysis.- 14.2 Comparing Two Proportions.- 14.3 Statistical Inferences for a Single Proportion.- 14.4 Comparison of Three or More Proportions.- 14.5 Analysis of Larger (r × c) Contingency Tables.- 15: Linear Correlation and Regression.- 15.1 Linear Correlation.- 15.2 Linear Regression.- 15.3 Correlation vs Regression.- 15.4 Statistical Inference.- 15.5 Control for Confounding Factors.- 15.6 Rank (Nonparametric) Correlation.- III Special Topics.- 16: Diagnostic Tests.- 16.1 Introduction.- 16.2 Defining “Normal” and “Abnormal” Test Results.- 16.3 The Reproducibility and Validity of Diagnostic Tests.- 16.4 The Predictive Value of Diagnostic Tests.- 16.5 Bayes’ Theorem.- 16.6 The Uses of Diagnostic Tests.- 17: Decision Analysis.- 17.1 Strategies for Decision-Making.- 17.2 Constructing a Decision Tree.- 17.3 Probabilities and Utilities.- 17.4 Completing the Analysis.- 17.5 Cost-Benefit Analysis.- 17.6 Cost-Effectiveness Analysis.- 18: Life-Table (Survival) Analysis.- 18.1 Introduction.- 18.2 Alternative Methods of Analysis: an Example.- 18.3 The Actuarial Method.- 18.4 The Kaplan-Meier (Product-Limit) Method.- 18.5 Statistical Inference.- 19: Causality.- 19.1 What is a “Cause”?.- 19.2 Necessary, Sufficient, and Multiple Causes.- 19.3 Patterns of Cause.- 19.4 Probability and Uncertainty.- 19.5 Can Exposure Cause Outcome?.- 19.6 Is Exposure an Important Cause of Outcome?.- 19.7 Did Exposure Cause Outcome in a Specific Case?.- Appendix Tables.