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Analysis of Biomarker Data: A Practical Guide [Hardback]

(Georgia Regents University, USA), (Baylor College of Medicine, USA)
  • Formāts: Hardback, 432 pages, height x width x depth: 241x160x28 mm, weight: 685 g, Graphs: 50 B&W, 0 Color
  • Izdošanas datums: 24-Apr-2015
  • Izdevniecība: John Wiley & Sons Inc
  • ISBN-10: 1118027558
  • ISBN-13: 9781118027554
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Analysis of Biomarker Data: A Practical GuidePalielināt
  • Formāts: Hardback, 432 pages, height x width x depth: 241x160x28 mm, weight: 685 g, Graphs: 50 B&W, 0 Color
  • Izdošanas datums: 24-Apr-2015
  • Izdevniecība: John Wiley & Sons Inc
  • ISBN-10: 1118027558
  • ISBN-13: 9781118027554
Citas grāmatas par šo tēmu:
Permanent link: https://www.kriso.lv/db/9781118027554.html
Keywords:

A “how to” guide for applying statistical methods to biomarker data analysis

Presenting a solid foundation for the statistical methods that are used to analyze biomarker data,Analysis of Biomarker Data: A Practical Guide features preferred techniques for biomarker validation. The authors provide descriptions of select elementary statistical methods that are traditionally used to analyze biomarker data with a focus on the proper application of each method, including necessary assumptions, software recommendations, and proper interpretation of computer output. In addition, the book discusses frequently encountered challenges in analyzing biomarker data and how to deal with them, methods for the quality assessment of biomarkers, and biomarker study designs.

Covering a broad range of statistical methods that have been used to analyze biomarker data in published research studies,Analysis of Biomarker Data: A Practical Guide also features:

  • A greater emphasis on the application of methods as opposed to the underlying statistical and mathematical theory
  • The use of SAS®, R, and other software throughout to illustrate the presented calculations for each example
  • Numerous exercises based on real-world data as well as solutions to the problems to aid in reader comprehension
  • The principles of good research study design and the methods for assessing the quality of a newly proposed biomarker
  • A companion website that includes a software appendix with multiple types of software and complete data sets from the book’s examples
Analysis of Biomarker Data: A Practical Guide is an ideal upper-undergraduate and graduate-level textbook for courses in the biological or environmental sciences. An excellent reference for statisticians who routinely analyze and interpret biomarker data, the book is also useful for researchers who wish to perform their own analyses of biomarker data, such as toxicologists, pharmacologists, epidemiologists, environmental and clinical laboratory scientists, and other professionals in the health and environmental sciences.

Preface xiii
Acknowledgements xvii
1 Introduction 1(4)
1.1 What is a Biomarker?,
1(1)
1.2 Biomarkers Versus Surrogate Endpoints,
2(1)
1.3 Organization of This Book,
3(2)
2 Designing Biomarker Studies 5(16)
2.1 Introduction,
5(1)
2.2 Designing the Study,
6(7)
2.2.1 The Exposure-Disease Association,
6(1)
2.2.2 Cross-sectional Studies,
7(1)
2.2.3 Case-Control Studies,
7(2)
2.2.4 Retrospective Cohort Studies,
9(1)
2.2.5 Prospective Cohort Studies,
9(1)
2.2.6 Observational Studies,
10(1)
2.2.7 Randomized Controlled Trials,
11(2)
2.3 Designing the Analysis,
13(5)
2.3.1 Choosing the Appropriate Measure of Association,
15(1)
2.3.1.1 Odds Ratio versus Risk Ratio,
15(1)
2.3.1.2 Consequences of Not Choosing the Appropriate Measure of Association,
16(1)
2.3.2 Choosing the Appropriate Statistical Analysis,
16(1)
2.3.3 Choosing the Appropriate Sample Size,
17(1)
2.4 Presenting Statistical Results,
18(2)
Problems,
20(1)
3 Elementary Statistical Methods for Analyzing Biomarker Data 21(51)
3.1 Introduction,
21(1)
3.2 Graphical and Tabular Summaries,
21(5)
3.3 Descriptive Statistics,
26(5)
3.4 Describing the Shape of Distributions,
31(2)
3.5 Sampling Distributions,
33(1)
3.6 Introduction to Statistical Inference,
34(9)
3.6.1 Point Estimation and Confidence Interval Estimation,
34(4)
3.6.2 Hypothesis Testing,
38(5)
3.7 Comparing Means Across Groups,
43(7)
3.7.1 Two Group Comparisons,
44(1)
3.7.2 Multiple-Group Comparisons,
45(5)
3.8 Correlation Analysis,
50(2)
3.9 Regression Analysis,
52(9)
3.9.1 Simple Linear Regression,
52(3)
3.9.2 Multiple Regression,
55(3)
3.9.3 Analysis of Covariance,
58(3)
3.10 Analyzing Cross-Classified Data,
61(8)
3.10.1 Testing for Independence,
61(4)
3.10.2 Comparison of Proportions,
65(4)
Problems,
69(3)
4 Frequently Encountered Challenges in Analyzing Biomarker Data and How to Deal with Them 72(183)
4.1 Introduction,
72(1)
4.2 Non-Normally Distributed Data,
73(40)
4.2.1 The Effects of Non-Normality,
73(1)
4.2.2 Testing Distributional Assumptions,
74(12)
4.2.2.1 Graphical Methods for Assessing Normality,
74(7)
4.2.2.2 Measures of Skewness and Kurtosis,
81(2)
4.2.2.3 Formal Hypothesis Tests of the Normality Assumption,
83(3)
4.2.3 Remedial Measures for Violation of a Distributional Assumption,
86(27)
4.2.3.1 Choosing a Transformation,
86(6)
4.2.3.2 Using a Robust Statistical Procedure,
92(1)
4.2.3.3 Distribution-Free Alternatives,
93(20)
4.3 Heterogeneity of Variance,
113(9)
4.3.1 The Effects of Heterogeneity,
113(1)
4.3.2 The Importance of Heterogeneity in the Comparison of Means,
113(9)
4.3.2.1 Comparisons of Two Groups,
113(3)
4.3.2.2 Comparisons of More Than Two Groups,
116(2)
4.3.2.3 Multiple Comparisons,
118(4)
4.4 Dependent Groups,
122(22)
4.4.1 The Consequences of Ignoring Dependence Among Groups,
122(2)
4.4.2 Comparing Two Dependent Means,
124(10)
4.4.2.1 Paired t-test,
124(3)
4.4.2.2 Wilcoxon Signed Ranks Test,
127(1)
4.4.2.3 Sign Test,
128(6)
4.4.3 Tests of Dependent Proportions,
134(10)
4.4.3.1 McNemar's Test,
134(4)
4.4.3.2 Cochran's Q test,
138(4)
4.4.3.3 Sample Size and Power Considerations,
142(2)
4.5 Correlated Outcomes,
144(40)
4.5.1 Choosing the Appropriate Measure of Association,
144(4)
4.5.1.1 Spearman's rho,
144(2)
4.5.1.2 Kendall's tau-b,
146(2)
4.5.2 Recommended Methods of Statistical Analysis for Correlation Coefficients,
148(8)
4.5.3 Recommended Methods for Interpreting Correlation Coefficient Results,
156(1)
4.5.4 Sample Size Issues in Correlation Analysis,
157(14)
4.5.5 Comparison of Correlation Coefficients,
171(10)
4.5.5.1 Comparison of Independent Correlation Coefficients,
172(2)
4.5.5.2 Comparison of Dependent Correlation Coefficients,
174(7)
4.5.6 Sample Size Issues When Comparing Two Correlation Coefficients,
181(18)
4.5.6.1 Sample Size Issues When Comparing Independent Correlation Coefficients,
181(2)
4.5.6.2 Sample Size Issues When Comparing Dependent Correlation Coefficients,
183(1)
4.6 Clustered Data,
184(15)
4.7 Outliers,
199(9)
4.7.1 The Effects of Outliers,
199(1)
4.7.2 Detection of Outliers,
199(8)
4.7.3 Methods for Accommodating Outliers,
207(1)
4.8 Limits of Detection and Non-Detected Observations,
208(13)
4.8.1 Statistical Inference When NDs Are Present,
210(1)
4.8.2 Maximum Likelihood Estimation of a Correlation Coefficient When Both X and Y Are Subject to Non-Detects,
210(2)
4.8.3 Comparison of Confidence Interval Methods for Correlation Coefficients When Both Variables Are Subject to Limits of Detection,
212(9)
4.9 The Analysis of Cross-Classified Categorical Data,
221(25)
4.9.1 Choosing the Appropriate Measure of Association,
221(4)
4.9.1.1 The Odds Ratio,
221(2)
4.9.1.2 Risk Ratio,
223(1)
4.9.1.3 Risk Difference,
224(1)
4.9.1.4 Odds Ratio for Paired Data,
225(1)
4.9.2 Choosing the Appropriate Statistical Analysis,
225(1)
4.9.3 Choosing the Appropriate Sample Size,
226(1)
4.9.4 Choosing a Statistical Method When Both the Predictor and the Outcome Are Dichotomous,
226(11)
4.9.4.1 Comparing Two Independent Groups in Terms of a Binomial Proportion,
226(4)
4.9.4.2 Exact Test for Independence of Rows and Columns in a 2 x 2 Table,
230(2)
4.9.4.3 Exact Inference for Odds Ratios,
232(2)
4.9.4.4 Inference for the Odds Ratio for Paired Data,
234(3)
4.9.5 Choice of a Statistical Method When the Predictor Is Ordinal and the Outcome is Dichotomous,
237(3)
4.9.5.1 Tests for a Significant Trend in Proportions,
237(3)
4.9.6 Choice of a Statistical Method When Both the Predictor and the Outcome are Ordinal,
240(3)
4.9.6.1 Test for Linear-by-Linear Association,
240(3)
4.9.7 Choice of a Statistical Method When Both the Predictor and the Outcome are Nominal,
243(14)
4.9.7.1 Fisher-Freeman-Halton Test,
243(3)
Problems,
246(9)
5 Validation of Biomarkers 255(77)
5.1 Overview of Methods for Assessing Characteristics of Biomarkers,
255(2)
5.2 General Description of Measures of Agreement,
257(30)
5.2.1 Discrete Variables,
257(18)
5.2.1.1 Cohen's Kappa,
257(8)
5.2.1.2 Extensions of Coefficient Kappa,
265(8)
5.2.1.3 Weighted Kappa,
273(2)
5.2.2 Continuous Variables,
275(12)
5.2.2.1 Pearson's Correlation Coefficient,
275(2)
5.2.2.2 Alternatives to Pearson's Correlation Coefficient,
277(10)
5.3 Assessing Reliability of a Biomarker,
287(7)
5.3.1 General Considerations,
287(1)
5.3.2 Assessing Reliability of a Dichotomous Biomarker,
287(4)
5.3.2.1 Dichotomous Biomarker, More Than Two Raters,
289(2)
5.3.3 Assessing Reliability of a Continuous Biomarker,
291(1)
5.3.4 Assessing Inter-Subject, Intra-Subject, and Analytical Measurement Variability,
292(2)
5.4 Assessing Validity,
294(35)
5.4.1 General Considerations,
294(1)
5.4.2 Assessing Validity When a Gold Standard Is Available,
295(19)
5.4.2.1 Dichotomous Biomarkers,
295(7)
5.4.2.2 Comparing Several Dichotomous Biomarkers,
302(2)
5.4.2.3 Continuous Biomarkers,
304(10)
5.4.3 Assessing Validity When a Gold Standard Is Not Available,
314(14)
5.4.3.1 Dichotomous Biomarkers,
315(4)
5.4.3.2 Continuous Biomarkers,
319(9)
5.4.4 Assessing Criterion Validity in Method Comparison Studies,
328(1)
5.4.5 Assessing Construct Validity in Method Comparison Studies,
329(1)
Problems,
329(3)
References 332(16)
Solutions to Problems 348(43)
Index 391
STEPHEN W. LOONEY, PHD, is Professor in the Department of Biostatistics and Epidemiology at Georgia Regents University, USA. He is a Fellow of the American Statistical Association and the Royal Statistical Society, an elected member of the International Statistical Institute, and a member of the International Biometric Society.

JOSEPH L. HAGAN, SCD, is Research Statistician at Texas Children's Hospital and Assistant Professor at the Baylor College of Medicine, USA. He is a member of the American Statistical Association.