Atjaunināt sīkdatņu piekrišanu

Medical Statistics: A Textbook for the Health Sciences 5th edition [Mīkstie vāki]

4.00/5 (5 ratings by Goodreads)
(University of Sheffield, UK), (Medical Research Council Cancer Trials Office, Cambridge), (University of Southampton)
  • Formāts: Paperback / softback, 448 pages, height x width x depth: 254x175x28 mm, weight: 612 g
  • Izdošanas datums: 04-Feb-2021
  • Izdevniecība: Wiley-Blackwell
  • ISBN-10: 1119423643
  • ISBN-13: 9781119423645
Citas grāmatas par šo tēmu:
  • Mīkstie vāki
  • Cena: 52,05 €
  • Grāmatu piegādes laiks ir 3-4 nedēļas, ja grāmata ir uz vietas izdevniecības noliktavā. Ja izdevējam nepieciešams publicēt jaunu tirāžu, grāmatas piegāde var aizkavēties.
  • Daudzums:
  • Ielikt grozā
  • Piegādes laiks - 4-6 nedēļas
  • Pievienot vēlmju sarakstam
Medical Statistics: A Textbook for the Health Sciences 5th editionPalielināt
  • Formāts: Paperback / softback, 448 pages, height x width x depth: 254x175x28 mm, weight: 612 g
  • Izdošanas datums: 04-Feb-2021
  • Izdevniecība: Wiley-Blackwell
  • ISBN-10: 1119423643
  • ISBN-13: 9781119423645
Citas grāmatas par šo tēmu:
Permanent link: https://www.kriso.lv/db/9781119423645.html
Keywords:
"The fifth edition of this popular textbook provides students and practitioners with a clear, concise introduction to the statistics they will come across in their regular reading of clinical papers. Written by three experts with wide teaching and consulting experience, Medical Statistics: A Textbook for the Health Sciences, 5th Edition: Assumes no prior knowledge of statistics. Covers all essential statistical methods. Completely revised, updated and expanded. Includes numerous examples and exercises onthe interpretation of the statistics in papers published in medical journals. Includes a companion website with downloadable data, new separate chapters on topics such as logistic regression; diagnostic tests; assessing the quality of statistics/reading the statistics in a scientific paper and other statistical methods (such as meta-analysis; the bootstrap; imputation of missing data and summary measures for analysing longitudinal data) and additional sections on designs such as pilot/feasibility studies; cluster and stepped wedge randomised controlled trials"--

The 5th edition of this popular introduction to statistics for the medical and health sciences has undergone a significant revision, with several new chapters added and examples refreshed throughout the book. Yet it retains its central philosophy to explain medical statistics with as little technical detail as possible, making it accessible to a wide audience. 

 

Helpful multi-choice exercises are included at the end of each chapter, with answers provided at the end of the book.  Each analysis technique is carefully explained and the mathematics kept to minimum. Written in a style suitable for statisticians and clinicians alike, this edition features many real and original examples, taken from the authors' combined many years' experience of designing and analysing clinical trials and teaching statistics.

 

Students of the health sciences, such as medicine, nursing, dentistry, physiotherapy, occupational therapy, and radiography should find the book useful, with examples relevant to their disciplines. The aim of training courses in medical statistics pertinent to these areas is not to turn the students into medical statisticians but rather to help them interpret the published scientific literature and appreciate how to design studies and analyse data arising from their own projects.  However, the reader who is about to design their own study and collect, analyse and report on their own data will benefit from a clearly written book on the subject which provides practical guidance to such issues.

 

The practical guidance provided by this book will be of use to professionals working in and/or managing clinical trials, in academic, public health, government and industry settings, particularly medical statisticians, clinicians, trial co-ordinators. Its practical approach will appeal to applied statisticians and biomedical researchers, in particular those in the biopharmaceutical industry, medical and public health organisations.

Preface xi
1 Uses and Abuses of Medical Statistics 1(8)
1.1 Introduction
2(1)
1.2 Why Use Statistics?
2(1)
1.3 Statistics is About Common Sense and Good Design
3(2)
1.4 How a Statistician Can Help
5(4)
2 Displaying and Summarising Data 9(28)
2.1 Types of Data
10(3)
2.2 Summarising Categorical Data
13(2)
2.3 Displaying Categorical Data
15(2)
2.4 Summarising Continuous Data
17(7)
2.5 Displaying Continuous Data
24(4)
2.6 Within-Subject Variability
28(2)
2.7 Presentation
30(1)
2.8 Points When Reading the Literature
31(1)
2.9 Technical Details
32(1)
2.10 Exercises
33(4)
3 Summary Measures for Binary Data 37(12)
3.1 Summarising Binary and Categorical Data
38(8)
3.2 Points When Reading the Literature
46(1)
3.3 Exercises
46(3)
4 Probability and Distributions 49(22)
4.1 Types of Probability
50(4)
4.2 The Binomial Distribution
54(1)
4.3 The Poisson Distribution
55(2)
4.4 Probability for Continuous Outcomes
57(1)
4.5 The Normal Distribution
58(5)
4.6 Reference Ranges
63(1)
4.7 Other Distributions
64(2)
4.8 Points When Reading the Literature
66(1)
4.9 Technical Section
66(1)
4.10 Exercises
67(4)
5 Populations, Samples, Standard Errors and Confidence Intervals 71(20)
5.1 Populations
72(1)
5.2 Samples
73(1)
5.3 The Standard Error
74(1)
5.4 The Central Limit Theorem
75(2)
5.5 Standard Errors for Proportions and Rates
77(2)
5.6 Standard Error of Differences
79(1)
5.7 Confidence Intervals for an Estimate
80(3)
5.8 Confidence Intervals for Differences
83(1)
5.9 Points When Reading the Literature
84(1)
5.10 Technical Details
85(1)
5.11 Exercises
86(5)
6 Hypothesis Testing, P-values and Statistical Inference 91(20)
6.1 Introduction
92(1)
6.2 The Null Hypothesis
92(2)
6.3 The Main Steps in Hypothesis Testing
94(2)
6.4 Using Your P-value to Make a Decision About Whether to Reject, or Not Reject, Your Null Hypothesis
96(3)
6.5 Statistical Power
99(2)
6.6 One-sided and Two-sided Tests
101(1)
6.7 Confidence Intervals (CIs)
101(3)
6.8 Large Sample Tests for Two Independent Means or Proportions
104(3)
6.9 Issues with P-values
107(1)
6.10 Points When Reading the Literature
108(1)
6.11 Exercises
108(3)
7 Comparing Two or More Groups with Continuous Data 111(34)
7.1 Introduction
112(1)
7.2 Comparison of Two Groups of Paired Observations - Continuous Outcomes
113(6)
7.3 Comparison of Two Independent Groups - Continuous Outcomes
119(8)
7.4 Comparing More than Two Groups
127(3)
7.5 Non-Normal Distributions
130(1)
7.6 Degrees of Freedom
131(1)
7.7 Points When Reading the Literature
132(1)
7.8 Technical Details
132(8)
7.9 Exercises
140(5)
8 Comparing Groups of Binary and Categorical Data 145(18)
8.1 Introduction
146(1)
8.2 Comparison of Two Independent Groups - Binary Outcomes
146(5)
8.3 Comparing Risks
151(1)
8.4 Comparison of Two Groups of Paired Observations - Categorical Outcomes
152(1)
8.5 Degrees of Freedom
153(1)
8.6 Points When Reading the Literature
154(1)
8.7 Technical Details
154(6)
8.8 Exercises
160(3)
9 Correlation and Linear Regression 163(30)
9.1 Introduction
164(1)
9.2 Correlation
165(6)
9.3 Linear Regression
171(7)
9.4 Comparison of Assumptions Between Correlation and Regression
178(1)
9.5 Multiple Regression
179(2)
9.6 Correlation is not Causation
181(1)
9.7 Points When Reading the Literature
182(1)
9.8 Technical Details
182(8)
9.9 Exercises
190(3)
10 Logistic Regression 193(18)
10.1 Introduction
194(1)
10.2 Binary Outcome Variable
194(2)
10.3 The Multiple Logistic Regression Equation
196(4)
10.4 Conditional Logistic Regression
200(1)
10.5 Reporting the Results of a Logistic Regression
201(1)
10.6 Additional Points When Reading the Literature When Logistic Regression Has Been Used
202(1)
10.7 Technical Details
202(2)
10.8 The Wald Test
204(1)
10.9 Evaluating the Model and its Fit: The Hosmer-Lemeshow Test
204(1)
10.10 Assessing Predictive Efficiency (1): 2 x 2 Classification Table
205(1)
10.11 Assessing Predictive Efficiency (2): The ROC Curve
206(1)
10.12 Investigating Linearity
206(1)
10.13 Exercises
207(4)
11 Survival Analysis 211(22)
11.1 Time to Event Data
212(2)
11.2 Kaplan-Meier Survival Curve
214(3)
11.3 The Logrank Test
217(4)
11.4 The Hazard Ratio
221(2)
11.5 Modelling Time to Event Data
223(3)
11.6 Points When Reading Literature
226(3)
11.7 Exercises
229(4)
12 Reliability and Method Comparison Studies 233(16)
12.1 Introduction
234(1)
12.2 Repeatability
234(3)
12.3 Agreement
237(2)
12.4 Validity
239(1)
12.5 Method Comparison Studies
240(3)
12.6 Points When Reading the Literature
243(1)
12.7 Technical Details
243(2)
12.8 Exercises
245(4)
13 Evaluation of Diagnostic Tests 249(16)
13.1 Introduction
250(1)
13.2 Diagnostic Tests
250(1)
13.3 Prevalence, Overall Accuracy, Sensitivity, and Specificity
251(1)
13.4 Positive and Negative Predictive Values
252(1)
13.5 The Effect of Prevalence
253(1)
13.6 Confidence Intervals
254(1)
13.7 Functions of a Screening and Diagnostic Test
255(1)
13.8 Likelihood Ratio, Pre-test Odds and Post-test Odds
256(1)
13.9 Receiver Operating Characteristic (ROC) Curve
257(4)
13.10 Points When Reading the Literature About a Diagnostic Test
261(1)
13.11 Exercises
262(3)
14 Observational Studies 265(28)
14.1 Introduction
266(1)
14.2 Risk and Rates
266(6)
14.3 Taking a Random Sample
272(1)
14.4 Questionnaire and Form Design
273(1)
14.5 Cross-sectional Surveys
274(1)
14.6 Non-randomised Studies
275(3)
14.7 Cohort Studies
278(4)
14.8 Case-Control Studies
282(5)
14.9 Association and Causality
287(1)
14.10 Modern Causality Methods and Big Data
287(1)
14.11 Points When Reading the Literature
288(1)
14.12 Technical Details
288(2)
14.13 Exercises
290(3)
15 The Randomised Controlled Trial 293(20)
15.1 Introduction
294(1)
15.2 The Protocol
294(1)
15.3 Why Randomise?
295(1)
15.4 Methods of Randomisation
296(2)
15.5 Design Features
298(5)
15.6 Design Options
303(6)
15.7 Meta-analysis
309(1)
15.8 Checklists for Design, Analysis and Reporting
309(2)
15.9 CONSORT
311(1)
15.10 Points When Reading the Literature About a Trial
311(1)
15.11 Exercises
311(2)
16 Sample Size Issues 313(18)
16.1 Introduction
314(1)
16.2 Study Size
315(3)
16.3 Continuous Data
318(1)
16.4 Binary Data
319(2)
16.5 Prevalence
321(1)
16.6 Subject Withdrawals
322(1)
16.7 Other Aspects of Sample Size Calculations
323(2)
16.8 Points When Reading the Literature
325(1)
16.9 Technical Details
325(2)
16.10 Exercises
327(4)
17 Other Statistical Methods 331(24)
17.1 Analysing Serial or Longitudinal Data
332(9)
17.2 Poisson Regression
341(2)
17.3 Missing Data
343(7)
17.4 Bootstrap Methods
350(3)
17.5 Points When Reading the Literature
353(1)
17.6 Exercises
353(2)
18 Meta-analysis 355(14)
18.1 Introduction
356(1)
18.2 What is a Meta-analysis?
356(2)
18.3 Meta-analysis Methods
358(1)
18.4 Example: Mobile Phone Based Intervention for Smoking Cessation
359(4)
18.5 Discussion
363(1)
18.6 Technical Details
363(2)
18.7 Exercises
365(4)
19 Common Mistakes and Pitfalls 369(24)
19.1 Introduction
370(1)
19.2 Misleading Graphs and Tables
370(6)
19.3 Plotting Change Against Initial Value
376(4)
19.4 Within Group Versus Between Group Analyses
380(1)
19.5 Analysing Paired Data Ignoring the Matching
381(1)
19.6 Unit of Analysis
382(1)
19.7 Testing for Baseline Imbalances in an RCT
382(1)
19.8 Repeated Measures
383(4)
19.9 Clinical and Statistical Significance
387(1)
19.10 Post Hoc Power Calculations
387(1)
19.11 Predicting or Extrapolating Beyond the Observed Range of Data
388(2)
19.12 Exploratory Data Analysis
390(1)
19.13 Misuse of P-values
391(1)
19.14 Points When Reading the Literature
391(2)
Appendix: Statistical Tables 393(10)
Solutions to Multiple-Choice Exercises 403(10)
References 413(10)
Index 423
STEPHEN J. WALTERS is Professor of Medical Statistics and Clinical Trials in the School of Health and Related Research (ScHARR) at the University of Sheffield, UK. Stephen is a prolific researcher and writer, including the popular textbooks How to Display Data and How to Design, Analyse and Report Cluster Randomised Trials in Medicine and Health Related Research. He is a National Institute for Health Research (NIHR) Senior Investigator, and has developed several courses on teaching medical statistics to medical and health science students, clinicians and allied health professionals.

MICHAEL J. CAMPBELL is Emeritus Professor of Medical Statistics in the School of Health and Related Research (ScHARR) at the University of Sheffield, UK. Mike is a leading researcher in medical statistics and clinical trials with a national and international reputation. A prolific writer, Mike has written many best-selling textbooks on medical statistics and clinical trials including: Statistics at Square One, Statistics at Square Two, Sample Size Tables for Clinical Studies, and How to Design, Analyse and Report Cluster Randomised Trials in Medicine and Health Related Research.

DAVID MACHIN is Emeritus Professor of Medical Statistics in the School of Health and Related Research (ScHARR) at the University of Sheffield, UK. He was Foundation Director of the National Medical Research Council, Clinical Trials and Epidemiology Research Unit, Singapore, and Head of the MRC Cancer Trials Office, Cambridge, UK. He has published more than 250 peer reviewed articles, and several books on a wide variety of topics in statistics and medicine. His earlier experience included posts at the Universities of Wales, Leeds, Stirling, Southampton and Sheffield, a period with the European Organisation for Research and Treatment of Cancer in Brussels, Belgium, and at the World Health Organization in Geneva, Switzerland.