Michael Glick (University of Pennsylvania, Philadelphia, PA, USA), Alonso Carrasco-Labra (University of Pennsylvania, Philadelphia, PA, Olivia Urquhart (University of Pennsylvania, Philadelphia, PA, USA)

Statistics for Dental Clinicians

• Broschiertes Buch

Produktbeschreibung

STATISTICS FOR DENTAL CLINICIANS

Enables clinicians to understand how biostatistics relate and apply to dental clinical practice

Statistics for Dental Clinicians helps dental practitioners to understand and interpret the scientific literature and apply the concepts to their clinical practice. Written using clear, accessible language, the book breaks down complex statistical and study design principles and demonstrates how statistics can inform clinical practice.

Chapters cover the basic building blocks of statistics, including clinical study designs, descriptive and inferential statistical concepts, and interpretation of study results, including differentiating between clinical and statistical significance. An extensive glossary of statistical terms, as well as graphs, figures, tables, and illustrations are included throughout to improve reader comprehension. Select readings accompany each chapter.

Statistics for Dental Clinicians includes information on:
_ How to understand and interpret the scientific language used in the biomedical literature and statistical concepts that underlie evidence-based dentistry
_ What is statistics and why do we need it, and how to effectively apply study results to clinical practice
_ Understanding and interpreting standard deviations, standard errors, p-values, confidence intervals, sample sizes, correlations, survival analyses, probabilistic-based diagnosis, regression modeling, and patient-reported outcome measures
_ Understanding and interpreting absolute risks, relative risks and odds ratios, as well as randomized controlled trials, cohort studies, case-control studies, cross-sectional studies, meta-analysis, bias and confounding

With comprehensive coverage of a broad topic, written using accessible language and shining light on statistical complexity often found in writings related to clinical topics, Statistics for Dental Clinicians is an essential guide for any dental practitioner wishing to improve their understanding of the biomedical literature.

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Produktdetails

• Verlag: Wiley & Sons / Wiley-Blackwell
• Artikelnr. des Verlages: 1A119810160
• 1. Auflage
• Seitenzahl: 240
• Erscheinungstermin: 28. August 2023
• Englisch
• Abmessung: 251mm x 176mm x 14mm
• Gewicht: 516g
• ISBN-13: 9781119810162
• ISBN-10: 1119810167
• Artikelnr.: 62152622

Autorenporträt

Michael Glick, DMD, FDS RCSEd, is Professor of Oral Medicine and Executive Director, Center for Integrative Global Oral Health in the School of Dental Medicine at the University of Pennsylvania in Philadelphia, Pennsylvania, USA. Alonso Carrasco-Labra, DDS, MSc, PhD, is an Associate Professor in the Department of Preventive & Restorative Sciences and Director, Cochrane Oral Health Collaborating Center at Penn Dental Medicine, Center for Integrative Global Oral Health in the School of Dental Medicine at the University of Pennsylvania in Philadelphia, Pennsylvania, USA. Olivia Urquhart, MPH, is an Instructor in the Department of Preventive & Restorative Sciences, Center for Integrative Global Oral Health in the School of Dental Medicine at the University of Pennsylvania in Philadelphia, Pennsylvania, USA.

Inhaltsangabe

Preamble xi

1 What is statistics and why do we need it? 1

Selected readings 7

2 Understanding and interpreting measures of association 8

Effect and effect size 8

Dichotomous outcome variables 9

Data presentation and interpretation 9

Absolute risk 9

Absolute risk difference 10

Relative risk or risk ratio 10

Odds ratio 11

Mean difference 11

A few additional notes 12

Selected readings 16

3 Understanding and interpreting a standard deviation and normal distribution 17

A few additional notes 22

Selected readings 23

4 Understanding and interpreting standard error 24

Interpretation of the standard error of the mean 25

Implication of the standard error of the mean 26

A few additional notes 26

Selected readings 29

5 Understanding and interpreting hypothesis testing and p-values 30

Descriptive and inferential statistics 30

Sampling error 31

Hypothesis testing or null hypothesis significance testing 31

Null and alternative hypotheses 32

Significance 33

P-value 34

A few additional notes 36

Selected readings 37

6 Understanding and interpreting a confidence interval 38

Understanding the confidence interval 39

How to interpret a confidence interval 39

A few additional notes 41

Selected readings 44

7 Understanding and interpreting power analysis and sample size 45

Sample size: Why is it important? 45

Components of a sample size calculation 46

Size of the effect 46

Significance level and type I error 47

Power and type II error 49

A few additional notes 50

Selected readings 53

8 Understanding and interpreting a survival analysis 54

Kaplan-Meier or survival curve 55

Comparing two Kaplan-Meier (survival) curves 57

Cox proportional hazard model 58

A few additional notes 59

Selected readings 60

9 Understanding and interpreting a probabilistic-based diagnosis 61

Sensitivity 61

Specificity 63

Positive predictive value 63

Negative predictive value 63

Likelihood ratios 65

Selective readings 68

10 Understanding and interpreting a correlation 69

Pearson product-moment correlation 69

Interpretation of Pearson correlation coefficients and coefficient of determination 71

Misinterpretation of correlations 73

A few additional notes 74

Selected readings 74

11 Understanding and interpreting a regression analysis 76

Estimation 76

Prediction 77

Linear regression 77

Multiple (or multivariable) linear (MLR) regression 79

Logistic regression 79

Predicting risk and odds 80

A hypothetical example: predicting risk and odds of an outcome 80

Estimating odds ratios 81

A hypothetical example--estimating an odds ratio 81

Nonindependence of observations 82

Building a regression model 82

A few additional notes 82

Selected readings 85

12 Understanding and interpreting confounding and effect modification 86

Counterfactual framework and causal reasoning 86

Causal inference and confounding bias 87

Strategies to deal with confounding in the study design phase 89

1. Randomization 90

2. Specification 90

3. Matching 90
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