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Statistics for Clinicians: How Much Should a Doctor Know? 2023 ed. [Kietas viršelis]

  • Formatas: Hardback, 610 pages, aukštis x plotis: 254x178 mm, weight: 1633 g, 50 Illustrations, color; 81 Illustrations, black and white; XL, 610 p. 131 illus., 50 illus. in color., 1 Hardback
  • Išleidimo metai: 04-Mar-2023
  • Leidėjas: Springer International Publishing AG
  • ISBN-10: 3031207572
  • ISBN-13: 9783031207570
Kitos knygos pagal šią temą:
Statistics for Clinicians: How Much Should a Doctor Know? 2023 ed.Didesnis paveikslėlis
  • Formatas: Hardback, 610 pages, aukštis x plotis: 254x178 mm, weight: 1633 g, 50 Illustrations, color; 81 Illustrations, black and white; XL, 610 p. 131 illus., 50 illus. in color., 1 Hardback
  • Išleidimo metai: 04-Mar-2023
  • Leidėjas: Springer International Publishing AG
  • ISBN-10: 3031207572
  • ISBN-13: 9783031207570
Kitos knygos pagal šią temą:
Pastovi nuoroda: https://www.kriso.lt/db/9783031207570.html
Raktažodžiai:
How much statistics does a clinician, surgeon or nurse need to know?

This book provides an essential handbook to help appraise evidence in a scientific paper, to design and interpret the results of research correctly, to guide our students and to review the work of our colleagues. This title is written by a clinician exclusively for fellow clinicians, in their own language and not in statistical or epidemiological terms.

When clinicians discuss probability, it is focussed on how it applies to the management of patients in the flesh and how they are managed in a clinical setting. Statistics for Clinicians does not overlook the basis of statistics, but reviews techniques specific to medicine with an emphasis on their application. It ensures that readers have the correct tools to hand, including worked examples, guides and links to online calculators and free software, enabling readers to execute most statistical calculations. This book will therefore be enormously helpful for many working across all fields of medicine at any stage of their career.
1 Expressing and Analyzing Variability
1(86)
1.1 Introduction
1(1)
1.2 Variables: Types, Measurement and Role
2(1)
1.2.1 Variable Conversion
2(1)
1.3 Summarizing Data
3(7)
1.3.1 The Qualitative Variable
4(3)
1.3.2 The Quantitative Variable
7(1)
1.3.3 Measures of Central Tendency
7(1)
1.3.4 Measures of Dispersion
7(3)
1.4 The Normal Distribution
10(21)
1.4.1 The Standardized Normal Distribution and the Z Score
12(3)
1.4.2 The Confidence Interval (CI)
15(10)
1.4.3 Verifying Normality
25(5)
1.4.4 Normalization of Data
30(1)
1.5 The P-value
31(1)
1.5.1 The Primary Risk of Error (α)
31(1)
1.6 The Null and Alternative Hypothesis
32(2)
1.6.1 Statistical Significance and the Degree of Significance
34(1)
1.7 Testing Hypothesis
34(5)
1.7.1 A Simple Parametric Test
34(1)
1.7.2 Unilateral Study Design
34(2)
1.7.3 Bilateral Study Design
36(1)
1.7.4 The Secondary Risk of Error
37(2)
1.8 Common Indices of Clinical Outcomes
39(17)
1.8.1 The Risk and the Odds
39(1)
1.8.2 The Relative Risks and the Odds Ratio
39(4)
1.8.3 The Hazard Ratio
43(6)
1.8.4 Relative Risk Increase (RRI) and Relative Risk Reduction (RRR)
49(1)
1.8.5 Absolute Risk Increase (ARI) and Reduction (ARR)
50(1)
1.8.6 Number Needed to Treat Benefit (NNTB) and Number Needed to Harm (NNTH)
50(1)
1.8.7 Calculation of the 95% Confidence Interval and Testing Statistical Significance
51(5)
1.9 Diagnostic Accuracy
56(29)
1.9.1 The Discriminative Measures
57(6)
1.9.2 The Predictive Values
63(3)
1.9.3 The Likelihood Ratios
66(5)
1.9.4 Single Indicators of Test Performance
71(11)
1.9.5 Choosing the Appropriate Diagnostic Test
82(1)
1.9.6 Comparing Two Diagnostic Tests
83(2)
1.9.7 The Standards for Reporting Diagnostic Accuracy (STARD)
85(1)
References
85(2)
2 Bivariate Statistical Analysis
87(160)
2.1 Choosing a Statistical Test
88(2)
2.1.1 Independence of Data: Paired Versus Unpaired Tests
89(1)
2.1.2 Data Distribution: Parametric Versus Distribution-Free Tests
89(1)
2.2 Consulting Statistical Tables
90(7)
2.2.1 The Test Statistics
90(1)
2.2.2 The Degrees of Freedom (Df)
91(2)
2.2.3 Consulting Individual Tables
93(4)
2.3 Inferences on Two Qualitative Variables
97(17)
2.3.1 The Unpaired Tests
97(12)
2.3.2 The Paired Tests
109(5)
2.4 Inferences on Means and Variances of Normal Distribution: The Parametric Tests
114(29)
2.4.1 The Comparison of Two Means
114(9)
2.4.2 The Comparison of Two Variances
123(1)
2.4.3 The Comparison of Multiple Means
124(19)
2.5 Inference on Medians and Other Distributions Than Normal: Non-parametric Tests?
143(10)
2.5.1 The Comparison of Two-Groups
143(7)
2.5.2 The Comparison of Several Groups
150(3)
2.6 Inference on the Relation of Two Quantitative Variables
153(21)
2.6.1 Correlation
154(8)
2.6.2 Regression
162(12)
2.7 Inference on Survival Curves
174(17)
2.7.1 Introduction: Assumptions and Definitions
174(1)
2.7.2 Kaplan Meier Method
175(6)
2.7.3 Actuarial Method
181(2)
2.7.4 Comparison of Survival Curves
183(8)
2.8 Choosing the Appropriate Bivariate Statistical Test
191(4)
2.8.1 Introduction
191(2)
2.8.2 The Unpaired Statistical Tests
193(1)
2.8.3 The Paired Statistical Tests
194(1)
2.8.4 The Comparison of Survival Curves
194(1)
2.9 Adjusting Bivariate Analysis: Prognostic Studies
195(18)
2.9.1 Introduction
195(2)
2.9.2 Excluding a Qualitative (Reverse) Interaction
197(3)
2.9.3 Adjusting Two Proportions
200(3)
2.9.4 Adjusting Two Means
203(6)
2.9.5 Adjusting Two Quantitative Variables
209(4)
2.10 Measuring Agreement and Testing Reliability
213(31)
2.10.1 Introduction
213(2)
2.10.2 Plan of the Analysis
215(3)
2.10.3 The Qualitative Outcome
218(10)
2.10.4 The Quantitative Outcome
228(16)
References
244(3)
3 Multivariable Analysis
247(92)
3.1 Introduction
247(1)
3.2 The ANOVA Family
248(46)
3.2.1 Testing the Main Effects
249(2)
3.2.2 Testing Interaction Effects of Qualitative Variables
251(3)
3.2.3 Testing Interaction Effects of Quantitative Variables: Analysis of Covariance (ANCOVA)
254(17)
3.2.4 Multivariate ANOVA and ANCOVA (MANOVA and MANCOVA)
271(6)
3.2.5 Repeated Measures ANOVA (RMANOVA)
277(17)
3.3 General Outlines of Multivariable Models
294(5)
3.3.1 Indications
294(1)
3.3.2 Aim of the Model
294(1)
3.3.3 Selection of Predictors
295(1)
3.3.4 Model Selection
296(1)
3.3.5 The Equations and Estimation of Regression Coefficients
297(1)
3.3.6 Evaluation of the Model
298(1)
3.4 Multiple Regression Analysis
299(16)
3.4.1 Introduction: Simple Versus Multiple Linear Regression
300(1)
3.4.2 The Basic Assumptions
301(2)
3.4.3 The Example
303(2)
3.4.4 Designing the Model
305(1)
3.4.5 Verification of the Assumptions
306(4)
3.4.6 Model Evaluation
310(4)
3.4.7 What Should Be Reported
314(1)
3.4.8 The Case of Multiple Outcome Variables
315(1)
3.5 Binary Logistic Regression Analysis
315(14)
3.5.1 Introduction: The Linear Versus the Logistic Regression
315(7)
3.5.2 The Basic Assumptions
322(1)
3.5.3 The Example
323(1)
3.5.4 Designing the Model
324(1)
3.5.5 Verification of the Assumptions
324(1)
3.5.6 Model Evaluation
325(3)
3.5.7 What Should Be Reported
328(1)
3.6 Cox Regression Analysis
329(8)
3.6.1 Introduction: Life Tables Versus Cox Regression Analysis
329(1)
3.6.2 The Basic Assumptions
330(2)
3.6.3 The Example
332(1)
3.6.4 Designing the Model
333(1)
3.6.5 Verification of the Assumptions
333(2)
3.6.6 Model Evaluation
335(1)
3.6.7 What Should Be Reported
336(1)
References
337(2)
4 Sample Size Calculation
339(82)
4.1 Introduction
339(11)
4.1.1 Estimation of the Effect Size
340(1)
4.1.2 Choosing the Risks of Error
341(1)
4.1.3 The Direction of the Study
342(1)
4.1.4 Study Specific Factors
343(1)
4.1.5 Sample Size Calculation
344(6)
4.2 Comparison of Two Independent Quantitative Variables: Student and Mann & Whitney Tests
350(6)
4.2.1 Effect Size
350(2)
4.2.2 Sample Size
352(4)
4.3 Association of Two Independent Binary Variables: Chi-Square and Fisher's Exact Tests
356(6)
4.3.1 Effect Size
356(3)
4.3.2 Sample Size
359(3)
4.4 Categorical and Ordinal Variables: Chi-Square Tests of Independence and Goodness of Fit
362(4)
4.4.1 Effect Size
363(1)
4.4.2 Sample Size
364(2)
4.5 Paired Analysis
366(6)
4.5.1 Paired Student Test
366(2)
4.5.2 Paired Wilcoxon-Sign-Rank Test
368(2)
4.5.3 McNemar's Test
370(2)
4.6 Comparison of Multiple Means: One-Way ANOVA
372(5)
4.6.1 Effect Size
372(3)
4.6.2 Sample Size
375(2)
4.7 Simple Correlation: Pearson's Correlation Coefficient R
377(2)
4.7.1 Effect Size
378(1)
4.7.2 Sample Size
378(1)
4.8 Simple Linear Regression
379(2)
4.8.1 Effect Size
379(1)
4.8.2 Sample Size
379(2)
4.9 Time to Event
381(3)
4.9.1 Effect Size
381(1)
4.9.2 Sample Size
381(3)
4.10 Logistic Regression
384(3)
4.10.1 Effect Size: Log Odds Ratio
385(1)
4.10.2 Sample Size
385(2)
4.11 Multiple Regression
387(4)
4.11.1 Conditional Fixed Factors Model
387(2)
4.11.2 Unconditional Random Effect Model
389(2)
4.12 Repeated Measures
391(6)
4.12.1 Repeated Measures ANOVA (RMANOVA)
391(4)
4.12.2 Friedman Test
395(2)
4.13 Non-inferiority and Equivalence Studies
397(8)
4.13.1 Comparison of 2 Means
398(2)
4.13.2 Comparison of Two Proportions
400(3)
4.13.3 Time to Event Analysis
403(2)
4.14 Diagnostic Accuracy
405(5)
4.14.1 Sensitivity and Specificity
405(3)
4.14.2 ROC Analysis
408(2)
4.15 Measuring Agreement
410(3)
4.15.1 Qualitative Outcome
410(1)
4.15.2 Quantitative Outcome
411(2)
4.16 Survey Analysis
413(3)
4.16.1 Introduction
413(1)
4.16.2 Factors Regulating Sample Size Calculation
413(1)
4.16.3 Sample Size Calculation
414(2)
References
416(5)
5 The Protocol of a Comparative Clinical Study: Statistical Considerations
421(20)
5.1 Background and Rationale
421(1)
5.2 Objectives
421(1)
5.3 Study Design
422(2)
5.3.1 The Formulation of the Study
422(1)
5.3.2 Number of Study Groups and Number of Effectuated Comparisons
422(1)
5.3.3 The Classic Parallel Groups Versus Other Designs
423(1)
5.3.4 Design Framework: Superiority, Non-inferiority, Equivalence or Pilot Study
423(1)
5.3.5 Allocation Ratio
423(1)
5.4 Methods
424(4)
5.4.1 Study Endpoints
424(1)
5.4.2 Assignment of Interventions
424(4)
5.4.3 Blinding (Masking)
428(1)
5.5 Study Population and Samples
428(3)
5.5.1 Study Settings
428(1)
5.5.2 Inclusion Criteria
428(1)
5.5.3 Exclusion Criteria
429(1)
5.5.4 Study Timeline
430(1)
5.5.5 Follow-Up
430(1)
5.6 Treatments and Interventions
431(1)
5.6.1 Studied Treatments and Interventions
431(1)
5.6.2 Associated Treatments and Interventions
432(1)
5.7 Data Management
432(1)
5.7.1 Data Collection and Storage
432(1)
5.7.2 Data Monitoring and Auditing
432(1)
5.8 Statistical Methods
433(4)
5.8.1 Population for the Analysis
433(1)
5.8.2 Statistical Hypothesis
433(1)
5.8.3 Statistical Analysis
434(1)
5.8.4 Sample Size Determination
435(2)
5.9 Study Documentations
437(1)
5.10 Study Ethics
438(1)
5.11 Data Sharing and Publication
438(1)
5.12 Appendices
438(1)
References
438(3)
6 Introduction to Meta-Analysis
441(86)
6.1 Introduction
441(3)
6.1.1 From Narrative to Systematic Review
441(1)
6.1.2 Why Do We Need Meta-Analysis?
442(1)
6.1.3 Types of Meta-Analysis
443(1)
6.2 Stages of Meta-Analysis
444(75)
6.2.1 Formulation of the Problem
445(1)
6.2.2 Data Collection
446(1)
6.2.3 Assessment of Risk of Bias
447(1)
6.2.4 Choosing the Model
447(2)
6.2.5 Managing the Effect Size Estimates
449(3)
6.2.6 Estimation of a Mean Effect Size, Se, 95% CI and P Value
452(27)
6.2.7 Assessment of Heterogeneity
479(7)
6.2.8 Subgroup Analysis
486(12)
6.2.9 Meta Regression
498(5)
6.2.10 Assessment of Publication Bias and Small-Study Effects
503(8)
6.2.11 Sensitivity Analysis
511(2)
6.2.12 Reporting Meta-Analysis
513(6)
6.3 Psychometric Meta-Analysis (Hunter and Schmidt)
519(4)
6.3.1 The Basic Concept
519(1)
6.3.2 The Bare Bones Meta-Analysis
520(1)
6.3.3 Meta-Analysis Corrected for All Artifacts
521(2)
References
523(4)
7 Pitfalls and Common Errors
527(56)
7.1 Sample Size Calculation
527(14)
7.1.1 Empower the Study
527(1)
7.1.2 Sculpture the Primary Outcome
528(7)
7.1.3 Reduce Variability by Ameliorating the Study Design
535(2)
7.1.4 Prepare the Study to Receive the Selected Tests
537(3)
7.1.5 Account for the Effect of Covariates in Multivariable Analysis
540(1)
7.1.6 Manage the Secondary Outcomes
540(1)
7.2 Data Management
541(6)
7.2.1 Check on Errors and Data Consistency
541(1)
7.2.2 Verify Outliers
541(1)
7.2.3 Manage Missing Data
541(4)
7.2.4 Normalizing Data
545(2)
7.3 Tools of the Analysis
547(7)
7.3.1 The Statistical Software
547(4)
7.3.2 The Complementary Online Calculators
551(3)
7.4 Data Reporting in a Manuscript
554(22)
7.4.1 The Introduction
554(1)
7.4.2 The Material and Methods
555(5)
7.4.3 The Results
560(6)
7.4.4 The Discussion
566(2)
7.4.5 The Abstract
568(1)
7.4.6 Common Pitfalls, Misinterpretations, and Inadequate Reporting
569(7)
7.5 The Role of the Statistician
576(3)
7.5.1 Include the Statistician in the Research Team
576(1)
7.5.2 Begin from the Beginning
577(1)
7.5.3 The Protocol is not a One-Man Show
577(1)
7.5.4 Meeting Mid-Way
578(1)
7.5.5 Data Management
578(1)
7.5.6 Statistical Analysis
579(1)
7.5.7 Publication
579(1)
References
579(4)
List of Equations 583(26)
Index 609
Dr. Ahmed Hassouna, Professor Emeritus of Cardiothoracic Surgery, Ain Shams University, Egypt; is the founder, Editor-in-Chief of "The Cardiothoracic Surgeon" and "The Egyptian Cardiothoracic Surgeon", associate editor & statistical advisor of "the Egyptian Heart Journal". He is a visiting professor of Biostatistics at several Egyptian universities and a Biostatistics consultant for the Egyptian Ministry of Health and Population. He is a member of the Egyptian Supreme Council Committee for promoting professors, the founder, and CEO of TRUST research center".