Crash Course Evidence-Based Medicine: Reading and Writing Medical Papers Updated Print + eBook edition
- 1st Edition - January 8, 2015
- Latest edition
- Author: Amit Kaura
- Editor: Andrew Polmear
- Language: English
The (printed) ‘Updated Edition’ now comes with added value access to the complete, downloadable eBook version via Student Consult. Search, read and revise whilst on the move and u… Read more
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Description
Description
The (printed) ‘Updated Edition’ now comes with added value access to the complete, downloadable eBook version via Student Consult. Search, read and revise whilst on the move and use the interactive self-assessment to test your understanding. Crash Course - a more flexible, practical learning package than ever before.
Crash Course Evidence-Based Medicine: Reading and Writing Medical Papers – your effective everyday study companion PLUS the perfect antidote for exam stress! Save time and be assured you have all the core information you need in one place to excel on your course and achieve exam success.
A winning formula now for over 15 years, each volume has been fine-tuned to make your life easier. Especially written by junior doctors – those who understand what is essential for exam success – with all information thoroughly checked and quality assured by expert Faculty Advisers, the result is a series of books which exactly meets your needs and you know you can trust.
This essential recent addition to the series clearly brings together the related disciplines of evidence-based medicine, statistics, critical appraisal and clinical audit – all so central to current study and to modern clinical practice. It starts with the basics that every student needs to know and continues into sufficient detail to satisfy anyone contemplating their own research studies. Excel in Student Selected Component (SSC) assessments and that dreaded evidence-based medicine and statistics exam! Ensure you know how to prepare the highest quality reports and maximize your chances of getting published.
If you are not sure:
- why you need to know the standard deviation of a sample
- when to use a case-control study and when a cohort study
- what to say to your patient who asks about the benefits and harms of a drug
- how to argue the case for the inclusion of a drug on the hospital formulary
- how to make audit and quality improvement work for you,
…then this groundbreaking book is for you! Answer these and hundreds of other questions and lay a foundation for your clinical practice that will inform every consultation over a lifetime in medicine.
Table of contents
Table of contents
1.1. What is evidence-based medicine?
1.2. Formulating clinical questions
1.3. Identifying relevant evidence
1.4. Critically appraising the evidence
1.5. Assessing the results
1.6. Implementing the results
1.7. Evaluating performance
1.8. Creating guideline recommendations
2. Handling data
2.1. Types of variables
2.2. Displaying the distribution of a single variable
2.3. Displaying the distribution of two variables
2.4. Describing the frequency distribution: central tendency
2.5. Describing the frequency distribution: variability
2.6. Theoretical distributions
2.7. Transformations
2.8. Choosing the correct summary measure
3. Investigating hypotheses
3.1. Hypothesis testing
3.2. Choosing a sample
3.3. Extrapolating from 'sample' to 'population'
3.4. Comparing means and proportions: confidence intervals
3.5. The P-value
3.6. Statistical significance and clinical significance
3.7. Statistical power
4. Systematic review and meta-analysis
4.1. Why do we need systematic reviews?
4.2. Evidence synthesis
4.3. Meta-analysis
4.4. Presenting meta-analyses
4.5. Evaluating meta-analyses
4.6. Advantages and disadvantages
4.7. Key example of a meta-analysis
4.8. Reporting a systematic review
5. Research design
5.1. Obtaining data
5.2. Interventional studies
5.3. Observational studies
5.4. Clinical trials
5.5. Bradford-Hill criteria for causality
5.6. Choosing the right study design
5.7. Writing up a research study
6. Randomised controlled trials
6.1. Why choose an interventional study design?
6.2. Parallel randomised controlled trials
6.3. Confounding, causality and bias
6.4. Interpreting the results
6.5. Types of randomised controlled trials
6.6. Advantages and disadvantages
6.7. Key example of a randomised controlled trial
6.8. Reporting a randomised controlled trial
7. Cohort studies
7.1. Study design
7.2. Interpreting the results
7.3. Confounding, causality and bias
7.4. Advantages and disadvantages
7.5. Key example of a cohort study
8. Case-control studies
8.1. Study design
8.2. Interpreting the results
8.3. Confounding, causality and bias
8.4. Advantages and disadvantages
8.5. Key example of a case-control study
9. Measures of disease occurrence and cross-sectional studies
9.1. Measures of disease occurrence
9.2. Study design
9.3. Interpreting the results
9.4. Confounding, causality and bias
9.5. Advantages and disadvantages
9.6. Key example of a cross-sectional study
10. Ecological studies
10.1. Study design
10.2. Interpreting the results
10.3. Sources of error in ecological studies
10.4. Advantages and disadvantages
10.5. Key example of an ecological study
11. Case report and case series
11.1. Background
11.2. Conducting a case report
11.3. Conducting a case series
11.4. Critical appraisal of a case series
11.5. Advantages and disadvantages
11.6. Key examples of case reports
11.7. Key example of a case series
12. Qualitative research
12.1. Study design
12.2. Organising and analysing the data
12.3. Validity, reliability and transferability
12.4. Advantages and disadvantages
12.5. Key example of qualitative research
13. Confounding
13.1. What is confounding?
13.2. Assessing for potential confounding factors
13.3. Controlling for confounding factors
13.4. Reporting and interpreting the results
13.5. Key example of study confounding
14. Screening, diagnosis and prognosis
14.1. Screening, diagnosis and prognosis
14.2. Diagnostic tests
14.3. Evaluating the performance of a diagnostic test
14.4. The diagnostic process
14.5. Examples of diagnostic tests
14.6. Bias in diagnostic studies
14.7. Screening tests
14.8. Prognostic tests
15. Statistical techniques
15.1. Choosing appropriate statistical tests
15.2. Comparison of one group to a hypothetical value
15.3. Comparison of two groups
15.4. Comparison of three or more groups
15.5. Measures of association
15.6. Prediction
16. Clinical audit
16.1. Introduction to clinical audit
16.2. Planning the audit
16.3. Choosing the standards
16.4. Audit protocol
16.5. Define the sample
16.6. Data collection
16.7. Analysing the data
16.8. Evaluating the findings
16.9. Implementing change
16.10. Example of a clinical audit
17. Quality improvement
17.1. Quality improvement vs audit
17.2. The model for quality improvement
17.3. The aim statement
17.4. Measures for improvement
17.5. Developing the changes
17.6. The Plan-Do-Study-Act (PDSA) cycle
17.7. Repeating the cycle
17.8. Example of a quality improvement project
18. Economic evaluation
18.1. What is health economics?
18.2. Economic question and study design
18.3. Cost-minimisation analysis
18.4. Cost-utility analysis
18.5. Cost-effectiveness analysis
18.6. Cost-benefit analysis
18.7. Sensitivity analysis
19. Critical appraisal checklists
19.1. Critical appraisal
19.2. Systematic reviews and meta-analyses
19.3. Randomised controlled trials
19.4. Diagnostic studies
19.5. Qualitative studies
20. Crash Course in statistical formulae
20.1. Describing the frequency distribution
20.2. Extrapolating from 'sample' to 'population'
20.3. Study analysis
20.4. Test performance
20.5. Economic evaluation
21. Careers in academic medicine
21.1. Career pathway
21.2. Getting involved
21.3. Pros and cons
Product details
Product details
- Edition: 1
- Latest edition
- Published: June 3, 2015
- Language: English
About the editor
About the editor
AP
Andrew Polmear
About the author
About the author
AK