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El. knyga: Applied Biostatistical Principles and Concepts: Clinicians' Guide to Data Analysis and Interpretation

(Nemours Healthcare System, Wilmington, Delaware, USA)
  • Formatas: 322 pages
  • Išleidimo metai: 22-Nov-2017
  • Leidėjas: Routledge
  • Kalba: eng
  • ISBN-13: 9781315352213
Kitos knygos pagal šią temą:
Applied Biostatistical Principles and Concepts: Clinicians' Guide to Data Analysis and InterpretationDidesnis paveikslėlis
  • Formatas: 322 pages
  • Išleidimo metai: 22-Nov-2017
  • Leidėjas: Routledge
  • Kalba: eng
  • ISBN-13: 9781315352213
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The past three decades have witnessed modern advances in statistical modeling and evidence discovery in biomedical, clinical, and population-based research. With these advances come the challenges in accurate model stipulation and application of models in scientific evidence discovery

Applied Biostatistical Principles and Concepts provides practical knowledge using biological and biochemical specimen/samples in order to understand health and disease processes at cellular, clinical, and population levels. Concepts and techniques provided will help researchers design and conduct studies, then translate data from bench to clinics in attempt to improve the health of patients and populations.

This book is suitable for both clinicians and health or biological sciences students. It presents the reality in statistical modelling of health research data in a concise manner that will address the issue of "big data" type I error tolerance and probability value, effect size and confidence interval for precision, effect measure modification and interaction as well as confounders, thus allowing for more valid inferences and yielding results that are more reliable, valid and accurate.
Foreword xi
Preface xv
Acknowledgments xvii
Author xix
Introduction xxi
SECTION I Design process
1 Basics of biomedical and clinical research
3(24)
1.1 Introduction
3(2)
1.2 Why conduct clinical research?
5(1)
1.3 Study subjects
6(1)
1.4 Subject selection
6(1)
1.5 Sampling
7(1)
1.6 Generalization
7(1)
1.7 Sample size and power estimations
8(1)
1.8 Screening (detection) and diagnostic (confirmation) tests
8(13)
1.9 Balancing benefits and harmful effects in medicine
21(1)
1.10 Summary
22(5)
Questions for discussion
24(1)
References
25(2)
2 Research design: Experimental and nonexperimental studies
27(18)
2.1 Introduction
27(1)
2.2 Epidemiologic study designs
28(2)
2.3 Nonexperimental designs
30(2)
2.4 Experimental designs (clinical trials)
32(2)
2.5 Nonexperimental versus experimental design
34(2)
2.6 Measures of disease association or effect
36(1)
2.7 Precision, random error, and bias
37(2)
2.8 Confounding, covariates, effect measure modifier, interaction
39(2)
2.9 Summary
41(4)
Questions for discussion
42(1)
References
43(2)
3 Population, sample, probability, and biostatistical reasoning
45(32)
3.1 Introduction
45(1)
3.2 Populations
46(1)
3.3 Sample and sampling strategies
47(1)
3.4 Biostatistical reasoning
48(1)
3.5 Measures of central tendency and dispersion
49(19)
3.6 Standardized distribution---z score statistic
68(1)
3.7 Basic probability notion
69(1)
3.8 Simple and unconditional probability
69(1)
3.9 Conditional probability
70(1)
3.10 Independence and conditional probability
71(1)
3.11 Probability distribution
72(1)
3.12 Summary
72(5)
Questions for discussion
73(1)
References
74(3)
SECTION II Biostatistical modeling
4 Statistical considerations in clinical research
77(30)
4.1 Introduction
77(5)
4.2 Types of variables
82(1)
4.3 Variables and sources of variation (variability)
82(2)
4.4 Sampling, sample size, and power
84(3)
4.5 Research questions, hypothesis testing, and statistical inference
87(13)
4.6 Summary
100(7)
Questions for discussion
101(1)
References
102(5)
5 Study size and statistical power estimations
107(18)
5.1 Introduction
107(3)
5.2 Sample size characterization
110(1)
5.3 Purpose of sample size
110(1)
5.4 Sample size computation
110(4)
5.5 Sample size estimation for single- or one-sample proportion hypothesis testing
114(2)
5.6 One-sample estimation of sample size with outcome mean
116(1)
5.7 Two independent samples: Proportions
117(2)
5.8 Two independent group means
119(1)
5.9 Prospective cohort or two-group comparison in clinical trials
120(1)
5.10 Case-control study
121(1)
5.11 Summary
122(3)
Questions for discussion
123(1)
References
123(2)
6 Single sample statistical inference
125(26)
6.1 Introduction
125(5)
6.2 One-sample group design
130(1)
6.3 Hypothesis statement
130(1)
6.4 Test statistic
130(6)
6.5 Inference from a nonnormal population---One-sample t test
136(2)
6.6 Other types of t tests
138(7)
6.7 Summary
145(6)
Questions for discussion
148(1)
References
148(3)
7 Two independent samples statistical inference
151(20)
7.1 Introduction
151(1)
7.2 Independent (two-sample) t test and nonparametric alternative (Mann--Whitney u test)
152(10)
7.3 z Test for two independent proportions
162(2)
7.4 Chi-square test of proportions in two groups
164(4)
7.5 Summary
168(3)
Questions for discussion
169(1)
References
170(1)
8 Statistical inference in three or more samples
171(20)
8.1 Introduction
171(2)
8.2 Analysis of variance (ANOVA)?
173(9)
8.3 Other hypothesis tests based on ANOVA
182(6)
8.4 Summary
188(3)
Questions for discussion
189(1)
References
190(1)
9 Statistical inference involving relationships or associations
191(56)
9.1 Introduction
191(11)
9.2 Correlation and correlation coefficients
202(7)
9.3 Simple linear regression
209(8)
9.4 Multiple/multivariable linear regression
217(2)
9.5 Logistic regression technique
219(3)
9.6 Model building and interpretation
222(7)
9.7 Survival analysis: Time-to-event method
229(8)
9.8 Poisson regression
237(4)
9.9 Summary
241(6)
Questions for discussion
243(1)
References
243(4)
10 Special topics in evidence discovery
247(18)
70.7 Introduction
247(1)
10.2 Big data and implication in evidence discovery
248(3)
10.3 Reality in statistical modeling of translational and clinical science data
251(3)
10.4 Tabulation versus regression analysis: When and when not to use regression
254(8)
10.5 Summary
262(3)
Questions for discussion
262(1)
References
263(2)
Appendix 265(4)
Index 269
Laurens Holmes Jr. was trained in internal medicine, specializing in immunology and infectious diseases prior to his expertise in epidemiology (cancer)-with- biostatistics (survival analysis). Over the past two decades, Dr. Holmes had been working in cancer epidemiology, control & prevention. His involvement in biostatistical modeling of health research data includes signal amplification and stratification in risk modelling, evidence discovery through effect size and confidence interval (not p value) and evidence-based clinical and translational research through Quantitative Evidence Synthesis (QES).
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