Medical Statistics at a Glance 4th Edition by Aviva, Caroline 9781119167839 1119167833

Medical Statistics at a Glance 4th Edition Aviva Petrie Caroline Sabin – Ebook PDF Instant Download/Delivery, ISBN: 9781119167839, 1119167833

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• ISBN 10:1119167833
• ISBN 13:9781119167839
• Author: Aviva, Caroline 

Medical Statistics at a Glance

Table contents:

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
Part 2 Sampling and estimation
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
Part 3 Study design
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
Part 4 Hypothesis testing
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
Part 5 Basic techniques for analysing data
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
Part 6 Additional chapters
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?

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