Medical Statistics at a Glance
Námskeið
- LEI205G Vísinda- og teymisvinna
.
Lýsing:
Now in its fourth edition, Medical Statistics at a Glance is a concise and accessible introduction to this complex subject. Itprovides clear instruction on how to apply commonly used statistical procedures in an easy-to-read, comprehensive and relevant volume. This new edition continues to be the ideal introductory manual and reference guide to medical statistics, an invaluable companion for statistics lectures and a very useful revision aid.
This new edition of Medical Statistics at a Glance : Offers guidance on the practical application of statistical methods in conducting research and presenting results Explains the underlying concepts of medical statistics and presents the key facts without being unduly mathematical Contains succinct self-contained chapters, each with one or more examples, many of them new, to illustrate the use of the methodology described in the chapter.
Now provides templates for critical appraisal, checklists for the reporting of randomized controlled trials and observational studies and references to the EQUATOR guidelines for the presentation of study results for many other types of study Includes extensive cross-referencing, flowcharts to aid the choice of appropriate tests, learning objectives for each chapter, a glossary of terms and a glossary of annotated full computer output relevant to the examples in the text Provides cross-referencing to the multiple choice and structured questions in the companion Medical Statistics at a Glance Workbook Medical Statistics at a Glance is a must-have text for undergraduate and post-graduate medical students, medical researchers and biomedical and pharmaceutical professionals.
Annað
- Höfundar: Aviva Petrie, Caroline Sabin
- Útgáfa:4
- Útgáfudagur: 2019-07-23
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- Format:ePub
- ISBN 13: 9781119167839
- Print ISBN: 9781119167815
- ISBN 10: 1119167833
Efnisyfirlit
- Cover
- Also available to buy!
- Preface
- Part 1 Handling data
- 1 Types of data
- Data and statistics
- Categorical (qualitative) data
- Numerical (quantitative) data
- Distinguishing between data types
- Derived data
- Censored data
- 2 Data entry
- Formats for data entry
- Planning data entry
- Categorical data
- Numerical data
- Multiple forms per patient
- Problems with dates and times
- Coding missing values
- 3 Error checking and outliers
- Typing errors
- Error checking
- Handling missing data
- Outliers
- References
- 4 Displaying data diagrammatically
- One variable
- Two variables
- Identifying outliers using graphical methods
- The use of connecting lines in diagrams
- 5 Describing data: the ‘average’
- Summarizing data
- The arithmetic mean
- The median
- The mode
- The geometric mean
- The weighted mean
- 6 Describing data: the ‘spread’
- Summarizing data
- The range
- Ranges derived from percentiles
- The standard deviation
- Variation within- and between-subjects
- 7 Theoretical distributions: the Normal distribution
- Understanding probability
- The rules of probability
- Probability distributions: the theory
- The Normal (Gaussian) distribution
- The Standard Normal distribution
- 8 Theoretical distributions: other distributions
- Some words of comfort
- More continuous probability distributions
- Discrete probability distributions
- 9 Transformations
- Why transform?
- How do we transform?
- Typical transformations
- 1 Types of data
- 10 Sampling and sampling distributions
- Why do we sample?
- Obtaining a representative sample
- Point estimates
- Sampling variation
- Sampling distribution of the mean
- Interpreting standard errors
- SD or SEM?
- Sampling distribution of the proportion
- 11 Confidence intervals
- Confidence interval for the mean
- Confidence interval for the proportion
- Interpretation of confidence intervals
- Degrees of freedom
- Bootstrapping and jackknifing
- Reference
- 12 Study design I
- Experimental or observational studies
- Defining the unit of observation
- Multicentre studies
- Assessing causality
- Cross-sectional or longitudinal studies
- Controls
- Bias
- Reference
- 13 Study design II
- Variation
- Replication
- Sample size
- Particular study designs
- Choosing an appropriate study endpoint
- References
- 14 Clinical trials
- Treatment comparisons
- Primary and secondary endpoints
- Subgroup analyses
- Treatment allocation
- Sequential trials
- Blinding or masking
- Patient issues
- The protocol
- References
- 15 Cohort studies
- Selection of cohorts
- Follow-up of individuals
- Information on outcomes and exposures
- Analysis of cohort studies
- Advantages of cohort studies
- Disadvantages of cohort studies
- Study management
- Clinical cohorts
- 16 Case–control studies
- Selection of cases
- Selection of controls
- Identification of risk factors
- Matching
- Analysis of unmatched or group-matched case–control studies
- Analysis of individually matched case–control studies
- Advantages of case–control studies
- Disadvantages of case–control studies
- References
- 17 Hypothesis testing
- Defining the null and alternative hypotheses
- Obtaining the test statistic
- Obtaining the P-value
- Using the P-value
- Non-parametric tests
- Which test?
- Hypothesis tests versus confidence intervals
- Equivalence and non-inferiority trials
- References
- 18 Errors in hypothesis testing
- Making a decision
- Making the wrong decision
- Power and related factors
- Multiple hypothesis testing
- References
- 19 Numerical data: a single group
- The problem
- The one-sample t-test
- The sign test
- 20 Numerical data: two related groups
- The problem
- The paired t-test
- The Wilcoxon signed ranks test
- Reference
- 21 Numerical data: two unrelated groups
- The problem
- The unpaired (two-sample) t-test
- The Wilcoxon rank sum (two-sample) test
- Reference
- 22 Numerical data: more than two groups
- The problem
- One-way analysis of variance
- The Kruskal–Wallis test
- References
- 23 Categorical data: a single proportion
- The problem
- The test of a single proportion
- The sign test applied to a proportion
- 24 Categorical data: two proportions
- The problems
- Independent groups: the Chi-squared test
- Related groups: McNemar’s test
- Reference
- 25 Categorical data: more than two categories
- Chi-squared test: large contingency tables
- Chi-squared test for trend
- 26 Correlation
- Pearson correlation coefficient
- Spearman’s rank correlation coefficient
- 27 The theory of linear regression
- What is linear regression?
- The regression line
- Method of least squares
- Assumptions
- Analysis of variance table
- Regression to the mean
- 28 Performing a linear regression analysis
- The linear regression line
- Drawing the line
- Checking the assumptions
- Failure to satisfy the assumptions
- Outliers and influential points
- Assessing goodness of fit
- Investigating the slope
- Using the line for prediction
- Improving the interpretation of the model
- 29 Multiple linear regression
- What is it?
- Why do it?
- Assumptions
- Categorical explanatory variables
- Analysis of covariance
- Choice of explanatory variables
- Analysis
- Outliers and influential points
- Reference
- 30 Binary outcomes and logistic regression
- Reasoning
- The logistic regression equation
- The explanatory variables
- Assessing the adequacy of the model
- Comparing the odds ratio and the relative risk
- Multinomial and ordinal logistic regression
- Conditional logistic regression
- References
- 31 Rates and Poisson regression
- Rates
- Poisson regression
- 32 Generalized linear models
- Which type of model do we choose?
- Likelihood and maximum likelihood estimation
- Assessing adequacy of fit
- Regression diagnostics
- 33 Explanatory variables in statistical models
- Nominal explanatory variables
- Ordinal explanatory variables
- Numerical explanatory variables
- Selecting explanatory variables
- Interaction
- Collinearity
- Confounding
- 34 Bias and confounding
- Bias
- Confounding
- References
- 35 Checking assumptions
- Why bother?
- Are the data Normally distributed?
- Are two or more variances equal?
- Are variables linearly related?
- What if the assumptions are not satisfied?
- Sensitivity analysis
- References
- 36 Sample size calculations
- The importance of sample size
- Requirements
- Methodology
- Altman’s nomogram
- Quick formulae
- Power statement
- Adjustments
- Increasing the power for a fixed sample size
- References
- 37 Presenting results
- Numerical results
- Tables
- Diagrams
- Presenting results in a paper
- References
- 38 Diagnostic tools
- Reference intervals
- Diagnostic tests
- 39 Assessing agreement
- Measurement variability and error
- Reliability
- Categorical variables
- Numerical variables
- Reporting guidelines
- References
- 40 Evidence-based medicine
- 1 Formulate the clinical question (PICO)
- 2 Locate the relevant information (e.g. on diagnosis, prognosis or therapy)
- 3 Critically appraise the methods in order to assess the validity (closeness to the truth) of the evidence
- 4 Extract the most useful results and determine whether they are important
- 5 Apply the results in clinical practice
- 6 Evaluate your performance
- References
- 41 Methods for clustered data
- Displaying the data
- Comparing groups: inappropriate analyses
- Comparing groups: appropriate analyses
- Reference
- 42 Regression methods for clustered data
- Aggregate level analysis
- Robust standard errors
- Random effects models
- Generalized estimating equations (GEE)
- References
- 43 Systematic reviews and meta-analysis
- The systematic review
- Meta-analysis
- References
- 44 Survival analysis
- Censored data
- Displaying survival data
- Summarizing survival
- Comparing survival
- Problems encountered in survival analysis
- Reference
- 45 Bayesian methods
- The frequentist approach
- The Bayesian approach
- Diagnostic tests in a Bayesian framework
- Disadvantages of Bayesian methods
- Reference
- 46 Developing prognostic scores
- Why do we do it?
- Assessing the performance of a prognostic score
- Developing prognostic indices and risk scores for other types of data
- Reporting guidelines
- Appendix A: Statistical tables
- Reference
- Appendix B: Altman’s nomogram for sample size calculations (Chapter 36)
- Appendix C: Typical computer output
- Appendix D: Checklists and trial profile from the EQUATOR network and critical appraisal templates
- Equator Network Statements
- Critical Appraisal Templates
- Reference
- Appendix E: Glossary of terms
- Appendix F: Chapter numbers with relevant multiple-choice questions and structured questions from Medical Statistics at a Glance Workbook
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- Gerð : 208
- Höfundur : Caroline Sabin , Aviva Petrie , Aviva Petrie, Caroline Sabin
- Útgáfuár : 2009
- Leyfi : 380