Anders Källén

Understanding Biostatistics

• Gebundenes Buch

Produktbeschreibung

Understanding Biostatistics looks at the fundamentals of biostatistics, using elementary statistics and explores the nature of statistical tests, the tests that produces the p-values and basic aspects of clinical trials. The concept of distribution functions are discussed, including the Guassian distribution and its importance in biostatistics. The book also looks at problems of estimating parameters in statistical models and looks at the similarities between different models. The author does not set out to provide the definitive text on biostatistics, but rather to provide a guide to the concepts involved and their application in everyday work.

Produktdetails

• Verlag: John Wiley & Sons / Turner Publishing Company
• Seitenzahl: 392
• Erscheinungstermin: 18. April 2011
• Englisch
• Abmessung: 254mm x 178mm x 27mm
• Gewicht: 798g
• ISBN-13: 9780470666364
• ISBN-10: 0470666366
• Artikelnr.: 33257735

Autorenporträt

Anders Källén, Department of Biostatistics, AstraZeneca R&D, Sweden.

Inhaltsangabe

Preface. 1 Statistics and Medical Science. 1.1 Introduction. 1.2 On the
Nature of Science. 1.3 How the Scientific Method uses Statistics. 1.4
Finding an Outcome Variable to Assess your Hypothesis. 1.5 How We Draw
Medical Conclusions from Statistical Results. 1.6 A Few Words about
Probabilities. 1.7 The Need for Honesty - the Multiplicity Issue. 1.8
Pre-Specification and p-value History. 1.9 Adaptive Designs - Controlling
the Risks in an Experiment. 1.10 The Elusive Concept of Probability. 1.11
Comments and Further Reading. References. 2 Observational Studies and the
Need for Clinical Trials. 2.1 Introduction. 2.2 Investigations of Medical
Interventions and Risk Factors. 2.3 Observational Studies and Confounders.
2.4 The Experimental Study. 2.5 Population Risks and Individual Risks. 2.6
Confounders, Simpson's Paradox and Stratification. 2.7 About Incidence and
Prevalence in Epidemiology. 2.8 Comments and Further Reading. References. 3
Study Design and the Bias Issue. 3.1 Introduction. 3.2 What Bias is All
About. 3.3 The Need for a Representative Sample - About Selection Bias. 3.4
Group Comparability and Randomization. 3.5 Information Bias in a Cohort
Study. 3.6 The Study, or Placebo, Effect. 3.7 The Curse of Missing Values.
3.8 Approaches to Data Analysis - Avoiding Self-inflicted Bias. 3.9 On
Meta-analysis and Publication Bias. 3.10 Comments and Further Reading.
References. 4 The Anatomy of Statistical Tests. 4.1 Introduction. 4.2
Statistical Tests, Medical Diagnosis and Roman Law. 4.3 The Risks with
Medical Diagnosis. 4.3.1 Medical Diagnosis based on a Single Test. 4.3.2
Bayes' Theorem and the Use and Misuse of Screening Tests. 4.4 The Law: a
Non-Quantitative Analogue. 4.5 Risks in Statistical Testing. 4.5.1 Does
Tonsillectomy Increase the Risk for Hodgkin's Lymphoma? 4.5.2 General
Discussion about Statistical Tests. 4.6 Making Statements about a Binomial
Parameter. 4.6.1 The Frequentist Approach. 4.6.2 The Bayesian Approach. 4.7
The Bell-Shaped Error Distribution. 4.8 Comments and Further Reading.
References. 4.A Appendix: The Evolution of the Central Limit Theorem. 5
Learning About Parameters, and Some Notes on Planning. 5.1 Introduction.
5.2 Test Statistics Described by Parameters. 5.3 How We Describe Our
Knowledge about a Parameter from an Experiment. 5.4 Statistical Analysis of
Two Proportions. 5.4.1 Some ways to compare two proportions. 5.4.2 Analysis
of the group difference. 5.5 Adjusting for Confounders in the Analysis. 5.6
The Power Curve of an Experiment. 5.7 Some Confusing Aspects of Power
Calculations. 5.8 Comments and Further Reading. References. 5.A Some
Technical Comments. 6 Empirical Distribution Functions. 6.1 Introduction.
6.2 How to Describe the Distribution of a Sample. 6.3 Describing the Sample
- Descriptive Statistics. 6.4 Population Distribution Parameters. 6.5
Confidence in the CDF and its Parameters. 6.6 Analysis of Paired Data. 6.7
Bootstrapping. 6.8 Meta-Analysis and Heterogeneity. 6.9 Comments and
Further Reading. References. 6.A Appendix: Some Technical Comments. 7
Correlation and Regression in Bivariate Distributions 7.1 Introduction. 7.2
Bivariate Distributions and Correlation. 7.3 About Baseline Corrections and
Other Covariates. 7.4 Bivariate Gaussian Distributions. 7.5 Regression to
the Mean. 7.6 Statistical Analysis of Bivariate Gaussian Data. 7.7
Simultaneous Analysis of Two Binomial Proportions. 7.8 Comments and Further
Reading. References. 7.A Appendix: Some Technical Comments. 8 How to
Compare the Outcome in Two Groups. 8.1 Introduction. 8.2 Simple Models that
Compare Two Distributions. 8.3 Comparison Done the Horizontal Way. 8.4
Analysis Done the Vertical Way. 8.5 Some Ways to Compute p-values. 8.6 The
Discrete Wilcoxon Test. 8.7 The Two-Period Crossover Trial. 8.8
Multivariate Analysis and Analysis of Covariance. 8.9 Comments and Further
Reading. References. 8.A Appendix: About U-statistics. 9 Least Squares,
Linear Models and Beyond. 9.1 Introduction. 9.2 The Purpose of Mathematical
Models. 9.3 Different Ways To Do Least Squares. 9.4 Logistic Regression,
with Variations. 9.5 The Two-step Modeling Approach. 9.6 The Effect of
Missing Covariates. 9.7 The Exponential Family of Distributions. 9.8
Generalized Linear Models. 9.9 Comments and Further Reading. References. 10
Analysis of Dose response. 10.1 Introduction. 10.2 Dose-Response
Relationship. 10.3 Relative Dose Potency and Therapeutic Ratio. 10.4
Subject-Specific and Population Averaged Dose-response. 10.5 Estimation of
the Population Averaged Dose-response Relationship. 10.6 Estimating
Subject-Specific Dose-responses. 10.7 Comments and Further Reading.
References. 11 Hazards and Censored Data. 11.1 Introduction. 11.2 Censored
observations: incomplete knowledge. 11.3 Hazard Models from a Population
Perspective. 11.4 The Impact of Competing Risks. 11.5 Heterogeneity in
Survival Analysis. 11.6 Recurrent Events and Frailty. 11.7 The Principles
Behind the Analysis of Censored Data. 11.8 The Kaplan-Meier Estimator of
the CDF. 11.9 Comments and Further Reading. References. 11.A Appendix: On
the Large-sample Approximations of Counting Processes. 12 From the Log-rank
Test to the Cox Proportional Hazards Model. 12.1 Introduction. 12.2
Comparing Hazards Between Two Groups. 12.3 Nonparametric Tests for Hazards.
12.4 Parameter Estimation in Hazards Models. 12.5 The Accelerated Failure
Time Model. 12.6 The Cox Proportional Hazards Model. 12.7 On Omitted
Covariates and Stratification in the Log-rank Test. 12.8 Comments and
Further Reading. References. 12.A Appendix: Comments on Interval-Censored
Data. 13 Remarks on Some Estimation Methods. 13.1 Introduction. 13.2
Estimating Equations and the Robust Variance Estimate. 13.3 From Maximum
Likelihood Theory to generalized estimating equations. 13.4 The Analysis of
Recurrent Events. 13.5 Defining and Estimating Mixed Effects Models. 13.6
Comments and Further Reading. References. 13.A Appendix: Formulas for
First-order Bias. Index.