- A
- B
Bar charts:

> Frequency distributions (graphical display of data)

> More than two variables (graphical display of data)

> Means within each group (displaying results)

> Means within each group with error bars added (displaying results)

Bland-Altman limits of agreement (reliability and validity)

Blind trial (planning a study) Binary data: small samples and extreme proportions (paired data)

Binary variables

Blinding (planning a study)

Block randomisation (planning a study)

Boxplots (displaying results)- C
Case-control study (planning a study)

Categorical data (summarising data)

Categorical data (making inferences)

Categorical data (paired data)

Categorical differences (quantifying)

Categorical variables

Centre and spread measures (summarising data)

Changing the form of data

Chi-square test (significance):

> Fishers exact test (non-parametric)

Choice of comparison groups

Cluster randomisation (planning a study)

Cohort study (planning a study)

Comparing two groups (non-parametric)

Comparing more than two proportions (>Two comparison groups or times)

Comparison groups - more than two

Comparisons between groups (planning a study)

Comparisons of several groups (displaying results)

Competing study designs (planning a study)

Confidence intervals (making inferences):

> For a single median (non-parametric)

> For other population parameters (making inferences)

> One sample: small samples and extreme proportions (making inferences)

> Relationship between confidence intervals and significance tests

> Two sample: small samples and extreme proportions (making inferences)

Confidence intervals when standard error cannot be estimated (making inferences)

Confounding factors (planning a study)

Contingency table - two variables (graphical display of data)

Contingency tables: more than two variables (graphical display of data)

Continuous variables

Control groups (planning a study)

Correlation coefficient r - standard error (making inferences)

Correlation - spearman (nonparametric)

Crossover trial (paired data)

Crossover trial (planning a study)

Cross-sectional study (planning a study)- D
Data assumptions (making inferences)

Data display

Data: ranking (non-parametric)

Data storage

Data types:

> Categorical variables

> Changing the form

> Data types practical

> Numeric variables

Defining and documenting the research question

Descriptive statistics (summarising data)

Difference between two means: standard error (making inferences)

Difference between two percentages: standard error (making inferences)

Difference between two proportions: standard error (making inferences)

Differences (quantifying)

Discrete variables

Displaying data:

> More than two variables

> One variable

> Two variables

Displaying results:

> Bar charts of means within each group

> Bar charts of means within each group with error bars added

> Boxplots

> Comparisons of several groups

> Displaying results alone

> Displaying results practical

> Serial measurements over time

> Summary

> Superimposing results on data

> Displaying serial measures

Distribution: non-normal data:

> J-shaped distributions

> Testing normality

> Transformations

Distribution - the normal distribution

Distribution - normal tables

Dot diagrams (graphical display of data)

Double blind study (planning a study)- E
Ecological study (planning a study)

Effects of confounding

Evaluation of knowledge (True or False Questions):

> True or False Questions - Solutions

Evaluation of knowledge (Best of Five Questions)

> Best of Five Questions - Solutions

Evaluation of knowledge (Extended Matching Questions):

> Extended Matching Questions - Solutions

Evaluation of knowledge (Longer Exercises):

> Longer Exercises - Solutions

Experimental studies (planning a study)- F
- G
Graphical display of data:

> More than two variables

> One variable

> Two variables

Groups - comparing two groups (non-parametric)

Groups - more than two:

> Analysis of serial measurements

> Comparing more than two proportions

> Displaying serial measures

> One-way analysis of variance

> Ordered categories

> More than two groups practical

> Serial measures

Groups - randomisation in (planning a study)- H
- I
- J
- K
- L
- M
Making inferences:

> Categorical data

> Confidence intervals

> Confidence intervals for other population parameters

> Confidence intervals when standard error cannot be estimated

> Making inferences practical

> Numeric data

> One sample: small sample and extreme proportions

> Standard error Standard error for other population parameters

> Two sample: small sample and extreme proportions

> What a sample tells us

Matching (comparison groups)

Mean (summarising data):

> Bar charts of means in each group (displaying results)

> Bar charts of means in each group with error bars added (displaying results)

> Standard error for difference between two means (making inferences)

> Standard error for a single mean (making inferences)

Median (summarising data)

> Confidence intervals for a single median (nonparametric)

Measures of centre (summarising data)

> Comparing the mean and median

Mean

Median

> Which to use: mean or median

Measures of centre and spread (summarising data)

Measures of spread (summarising data):

> Comparing the range, interquartile range and standard deviation

> Interquartile range

> Range

> Standard deviation

Minimisation (planning a study)

More than two comparison groups and/or times:

> Analysis of serial measurements

> Displaying serial measurements

> Comparing more than two proportions

> One-way analysis of variance

> Ordered categories

> More than two groups practical

> Serial measures

> Which test

More than two variables (displays of data)

> Contingency tables

> Scatterplots

> Side-by-side and stacked bar charts- N
Nominal variables

Non-normal data (summarising data):

> J-shaped distributions

> Testing normality

> Transformations

Nonparametric

> Comparing two groups

> Confidence intervals for a single median

> Fisher's exact test

> Other non-parametric tests

> Nonparametric practical

> Ranking data

> Sign test

> Spearman's correlations

> Mann-Whitney U test

> Wilcoxon signed rank test

Non-parametric correlation coefficient

Normal distribution (summarising data)

Normal tables (summarising data)

Normal tables - using the table examples (summarising data)

Normal vs diseased patients (non-parametric)

Null hypothesis (significance tests)

Numeric data (summarising data)

Numeric data (making inferences)

Numeric data (paired data)

Numeric difference (quantifying)- O
Observational study (planning a study)

Odds ratio - standard error for (making inference)

One or two-sided (significance)

One sample: small samples and extreme proportions (making inferences)

One sample t-test

One variable (graphical display of data)

> Dot diagrams

> Frequency distributions and bar charts

> Histograms

> Pie charts

> Summary of displays

One-way analysis of variance

Ordered categories (> two comparison groups or times)

Ordinal variables- P
p-values

Paired data: > Analysis of crossover trail data

> Categorical data

> Numeric data

> Paired binary data: small samples and extreme proportions

> Paired data practical

Paired or unpaired?

Paired t-test

Paired binary data: small samples and extreme proportions

Parametric significance tests

Percentages (summarising data): > Chi-square test for percentages (significance)

> One sample proportion/percentage test (significance)

> Standard error for difference between two percentages (making inferences)

> Standard error for difference between two proportions (making inferences)

Pie charts (graphical display of data)

Pilot studies (planning a study)

Plan of investigation

Planning a study: > Blinding

> Comparisons between groups

> Competing study designs

> Confounding factors

> Control groups

> Defining and documenting the research question

> Intention-to-treat analysis

> Losses to follow up

> Pilot studies

> Refusals to participation

> Random allocation

> Removal of confounding

> Sample size

> Target and sample populations

> Types of study

> Written protocol

Populations

Practical 1 (statistics in medical research)

Practical 2 (types, storage and graphical display of data)

Practical 3 (summarising data)

Practical 4 (quantifying differences and associations)

Practical 5 (making inferences)

Practical 6 (significance tests)

Practical 7 (paired data)

Practical 8 (non-parametric)

Practical 9 (reliability and validity)

Practical 10 (more than two groups and/or times)

Practical 11 (displaying results)

Practical 12 (evaluation - true or false)

Practical 12 (evaluation - best of five)

Practical 12 (evaluation - extended matching)

Practical 12 (evaluation - longer exercises)

Proportions (summarising data): > Chi square test for proportions (significance)

> Comparing more than two proportions (>two comparison groups/times)

> One sample: small samples and extreme proportions (making inferences)

> One sample proportions/percentage test (significance)

> Paired binary data: small samples and extreme proportions (paired data)

> Standard error for difference between two proportions (making inferences)

> Standard error for single proportion (making inferences)

> Two samples: small samples and extreme proportions (making inferences)

Prospective study (planning a study)- Q
- R
Random allocation (planning a study)

Randomisation (planning a study)

> In groups

> In practice

Randomised controlled trial (RCT) (planning a study)

Range (summarising data)

Ranking data (non-parametric)

Refusals to participation (planning a study)

Relationship between confidence intervals and significance tests

Relative Risk (quantifying differences)

Relative risk - standard error for (making inferences)

Reliability

> Bland-Altman limits of agreement

> Relation/difference between reliability and validity

> Intraclass correlation coefficient

> Kappa

> Weighted Kappa

> Quantifying reliability

> Quantifying validity

> Reliability and validity practical

> Reliability studies

Removal of confounding (planning a study)

Research question

Retrospective study (planning a study)- S
Sample - what a sample tells us (making inferences)

Sample size (planning a study)

Samples to populations (making inferences)

Scatterplot - two variables (graphical display of data)

Scatterplot - more than two variables (graphical display of data)

Serial measurements - analysis (>two comparison groups or times)

Serial measurements - display (>two comparison groups or times)

Serial measurements over time (displaying results)

Serial measures (>two comparison groups or times)

Side-by-side bar chart - two variables (graphical display of data)

Side-by-side bar chart - more than two variables (graphical display of data)

Sign test (non-parametric)

Significance level

Significance tests:

> Alternative significance tests

> 1- or 2-sided p-value

> Chi-square test

> Level of significance

> Null hypothesis

> One sample t-test

> Other parametric significance tests

> p-values

> Relationship between confidence intervals and significance tests

> Significance tests practical

> Transformations (for RR, OR, r)

> Two sample (unpaired) t-test

> Validity of tests

Simple randomisation (planning a study)

Single blind study (planning a study)

Skew and skewed distributions (summarising data)

Spearman's correlations (non-parametric)

Spread and centre measures (summarising data)

Square and square root (summarising data)

Stacked bar charts - two variables (graphical display of data)

Stacked bar charts- more than two variables (graphical display of data)

Standard deviation (summarising data)

Standard error (making inferences)

Standard error for other population parameters (making inferences):

> Confidence intervals when standard error cannot be estimated

> Correlation coefficient r

> Difference between two means

> Difference between two percentages

> Difference between two proportions

> Odds ratio

> Relative risk

> Single mean

> Single percentage

> Single proportion

Statistical inference (making inferences)

Stratification (planning a study)

Summarising categorical data (summarising data)

Summarising data:

> Comparing the mean and median

> Comparing the range, interquartile range and standard deviation

> Mean

> Median

> Non-normal data

> Non-normal data - J-shaped distribution

> Non-normal data - testing normality

> Non-normal data - transformations

> Normal distribution

> Normal tables

> Summarising data practical

> Which to use: mean or median

Summarising numeric data (summarising data)

Superimposing results on data (displaying results)

Symmetric distribution (summarising data)

Systematic assignment (planning a study)- T
T-tests:

> One-sample t-test

> Two-samples t-test

> Paired samples t-test

> Paired or unpaired? (Paired data)

Target and sample populations (planning a study)

Testing normality (summarising data)

Three dimensional bar charts (graphical display of data)

Transformations (significance)

Transformations of non-normal data (summarising data)

Two groups - comparing:

> Two samples t-test (significance tests)

> Two samples Mann-Whitney U test (nonparametric)

> Two groups chi squared test (significance tests)

> Two groups fisher's exact test (nonparametric)

> Two samples small samples and extreme proportions (inferences)

Two variables (graphical display of data):

> One numeric and one categorical

> Two categorical variables

> Two numeric variables

> Summary of displays for two variables

Types, storage and graphical display of data:

> Categorical variables

> Changing the form

> Data storage

> Graphical display of data

> Graphical display - more than two variables

> Graphical display - one variable

> Graphical display - two variables

> Numeric variables

> Types of data

> Types, storage and graphical display of data practical

Types of study (planning a study)- U
- V
Validity:

> Relation/difference between reliability and validity

> Quantifying validity

> Reliability and validity practical

> Validation studies

Validation studies

Validity of standard error calculation (making inferences)

Variables - binary

Variables - categorical

Variables - continuous

Variables - discrete

Variables - nominal

Variables - numeric

Variables - ordinal

Variance (summarising data)- W
- XYZ
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