How to Teach Statistics in EBM Rafael Perera. Basic teaching advice Know your audience Know your...
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Transcript of How to Teach Statistics in EBM Rafael Perera. Basic teaching advice Know your audience Know your...
How to Teach Statistics in EBM
Rafael Perera
Basic teaching advice
• Know your audience• Know your audience!• Create a knowledge gap• Give a map of the main concepts• Decide which ones to focus on• Use plenty of examples• Let them do the work/thinking
Main Concepts
• Bias and Measurement error• P values and Confidence Intervals• Which Statistical tests are needed and when• Correlation and Association• Models / Regression and alternatives for
Adjustment• Survival Analysis• Meta Analysis • Statistics for Diagnostic Studies
There is a time and place…
Fundamental Equation of Error
• Measure = Truth + Bias + Random Error
Use good study design
Use large
numbers
Researcher
Critically AppraiseDesign
ConfidenceIntervals
andP-values
Reader
true result
Biaslow high
Ra
nd
om
err
or
hig
h
low
Bias versus Random error
Bias and Measurement error
Groups of 3-4 people
1 – subject
2 – measurers
Measurers – measure (twice) and record the head size of the subject. Keep measurements hidden.
Bias and Measurement error
Intra-Observer variability
Measurement error
• Same answer
• Varied by < 0.5 cm
• Varied by < 1cm
• Varied by < 2 cm
• Varied by >2 cm
Bias and Measurement error
Inter-Observer variability
Measurement error
• Same answer
• Varied by < 0.5 cm
• Varied by < 1cm
• Varied by < 2 cm
• Varied by >2 cm
Bias and Measurement error
Bias
Included ears?
Included nose?
Which part of the head?
Other?
Does it matter?
In paediatric practice following meningitis, a head circumference that increases by 7mm in a day will result in urgent head imaging
In obstetrics measurements of the fundal height can vary by up to 5cm (the difference between having a baby delivered early due to IUGR or not when opposite occur)
The question is can you reproduce the test in your setting and will it perform as
well in your setting
Measuring Random error
Most things don’t work!
Two methods of assessing the role of random error
•P-values • (Hypothesis Testing)
– use statistical test to examine the ‘null’ hypothesis
– if p<0.05 then result is statistically significant
•Confidence Intervals(Estimation)
– estimates the range of values that is likely to include the true value
Relationship between p-values and confidence intervalsIf the ‘no effect’ value falls outside the CI then the result is statistically significant
The Steps in Testing a Hypothesis
State the
null hypothesis
H0 Choose the
test statistic
that
summarizes
the data
Based on H0
calculate the probability of
getting the
value of the
test statistic
Interpret the
P-value
Some Statistical tests
• Comparing groups– T-tests (1 or 2 groups, normally distributed)– Chi-squared (2 or more groups, categorical or binary data)– Mann-Whitney U (2 groups, non-normal data)– Log-rank test (2 groups, survival data)– ANOVA (multiple groups, normally distributed)– …
• Tips: – Understand what the hypothesis being tested is– Use the p-value to assess the level of evidence against it– (Experienced) Assess if the test was adequate for the question
and data analysed
Hand outs
1. Incidence/ Prevalence and CI2. Survival analysis3. Regression models / Adjustment4. Linear association / Correlation 5. Confounding / Odds Ratios / Logistic
Regression6. Diagnostic Tests7. Meta-analysis
Reading confidence intervals
Clinically significant
Vitamin X shortens a 5 day cold
Would you take it twice per day if it shortened the cold by:
Clinically significant
Vitamin X shortens a 5 day cold
Would you take it twice per day if it shortened the cold by:
50%
Clinically significant
Vitamin X shortens a 5 day cold
Would you take it twice per day if it shortened the cold by:
50%20%
Clinically significant
Vitamin X shortens a 5 day cold
Would you take it twice per day if it shortened the cold by:
50%20%10%
Clinically significant
Vitamin X shortens a 5 day cold
Would you take it twice per day if it shortened the cold by:
50%20%10%5%
Clinically significant
Vitamin X shortens a 5 day cold
Would you take it twice per day if it shortened the cold by:
50%20%10%5%1%
(a) (b)
(c) (d)
Minimum clinicalImportant difference
No difference
Which are clinically significant?
010
20
Thank you
EXTRAS
Different types of measurements use different types of statistics
• Dichotomous: – Male,female OR infected, non-infected
• Categorical: – Red, green, blue OR
• Ordinal: – Nil, +, ++ of glucose
• Interval:– temperature
STATISTICS
Proportion, Risk
Mode, Proportions
Mode, Median?
Mean, Median
> t wo samples
Independence between two or more variables
Parametric
Non parametric
Between means for
continuous data
Between
distributions
Hypothesis testing and
a ssessing d ifferences
Parametric ANOVA
Sign test for related
samples
Rank sum test for independent samples
Kruskal Wallis
T test difference for related samples
Non parametric
T test for independent
samples
McNemar’s test for
related groups
Between one observed
variable and a theoretical
distribution
X2 test for goodness
of fit
X 2 test for
independence
Two samples
One sample vs. H 0
One sample vs. H 0 Z score equal
proportions
Z score
Between proportions forcategorical data
Flowchart of Statistical Tests for Hypothesis Testing
Flowchart of Statistical Tests for Hypothesis Testing
Between distributions
Between one observed variable and a theoretical distribution
Independence between two or more variables
c2 test for goodness of fit
c2 test for independence
McNemar’s test for related groups
Flowchart of Statistical Tests for Hypothesis Testing
Between means for continuous data
Two samples
t-test independent samples
Rank sum test for independent samples
Sign test for related samples
t-test difference for related samples
ANOVA
Kruskal – Wallis
Parametric
> two samples
Non Parametric
Parametric
Non Parametric
Flowchart of Statistical Tests for Hypothesis Testing
Between proportions for categorical data
One sample vs. H0
Two samples
Z-score
Z-score equal proportions
Summarising proportions One sample: Risk, OddsTwo samples: Relative risk, Odds ratios, Risk differences