Crash Course - your effective everyday study companion PLUS the perfect antidote for exam stress! Save time and be assured you have all the 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 new 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:
…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.
1 Evidence-based medicine
What is evidence-based medicine?
Formulating clinical questions
Identifying relevant evidence
Critically appraising the evidence
Assessing the results
Implementing the results
Evaluating performance
Creating guideline recommendations
2 Handling data
Types of variables
Displaying the distribution of a single variable
Displaying the distribution of two variables
Describing the frequency distribution: central tendency
Describing the frequency distribution: variability
Theoretical distributions
Transformations
Choosing the correct summary measure
3 Investigating hypotheses
Hypothesis testing
Choosing a sample
Extrapolating from ‘sample’ to ‘population’
Comparing means and proportions: confidence intervals
The P-value
Statistical significance and clinical significance
Statistical power
4 Systematic review and meta-analysis
Why do we need systematic reviews?
Evidence synthesis
Meta-analysis
Presenting meta-analyses
Evaluating meta-analyses
Advantages and disadvantages
Key example of a meta-analysis
Reporting a systematic review
5 Research design
Obtaining data
Interventional studies
Observational studies
Clinical trials
Bradford-Hill criteria for causality
Choosing the right study design
Writing up a research study
6 Randomised controlled trials
Why choose an interventional study design?
Parallel randomised controlled trials
Confounding, causality and bias
Interpreting the results
Types of randomised controlled trials
Advantages and disadvantages
Key example of a randomised controlled trial
Reporting a randomised controlled trial
7 Cohort studies
Study design
Interpreting the results
Confounding, causality and bias
Advantages and disadvantages
Key example of a cohort study
8 Case-control studies
Study design
Interpreting the results
Confounding, causality and bias
Advantages and disadvantages
Key example of a case-control study
9 Measures of disease occurrence and cross-sectional studies
Measures of disease occurrence
Study design
Interpreting the results
Confounding, causality and bias
Advantages and disadvantages
Key example of a cross-sectional study
10 Ecological studies
Study design
Interpreting the results
Sources of error in ecological studies
Advantages and disadvantages
Key example of an ecological study
11 Case report and case series
Background
Conducting a case report
Conducting a case series
Critical appraisal of a case series
Advantages and disadvantages
Key examples of case reports
Key example of a case series
12 Qualitative research
Study design
Organising and analysing the data
Validity, reliability and transferability
Advantages and disadvantages
Key example of qualitative research
13 Confounding
What is confounding?
Assessing for potential confounding factors
Controlling for confounding factors
Reporting and interpreting the results
Key example of study confounding
14 Screening, diagnosis and prognosis
Screening, diagnosis and prognosis
Diagnostic tests
Evaluating the performance of a diagnostic test
The diagnostic process
Examples of diagnostic tests
Bias in diagnostic studies
Screening tests
Prognostic tests
15 Statistical techniques
Choosing appropriate statistical tests
Comparison of one group to a hypothetical value
Comparison of two groups
Comparison of three or more groups
Measures of association
Prediction
16 Clinical audit
Introduction to clinical audit
Planning the audit
Choosing the standards
Audit protocol
Define the sample
Data collection
Analysing the data
Evaluating the findings
Implementing change
Example of a clinical audit
17 Quality improvement
Quality improvement vs audit
The model for quality improvement
The aim statement
Measures for improvement
Developing the changes
The Plan-Do-Study-Act (PDSA) cycle
Repeating the cycle
Example of a quality improvement project
18 Economic evaluation
What is health economics?
Economic question and study design
Cost-minimisation analysis
Cost-utility analysis
Cost-effectiveness analysis
Cost-benefit analysis
Sensitivity analysis
19 Critical appraisal checklists
Critical appraisal
Systematic reviews and meta-analyses
Randomised controlled trials
Diagnostic studies
Qualitative studies
20 Crash Course in statistical formulae
Describing the frequency distribution
Extrapolating from ‘sample’ to ‘population’
Study analysis
Test performance
Economic evaluation
21 Careers in academic medicine
Career pathway
Getting involved
Pros and cons
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---|---|---|
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