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PP (Study Design) for 2nd YearDave Garbera F1 Arrowe Park Hospital
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Learning Objectives• Population Measures
• Study Design
• Statistical Devices
• Problems with Analysis
• Sensitivity, Specificity and Positive Predictive Value
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Why PP?• Provides a good understanding of the basics of Evidence
Based Medicine
• Understand study designs, statistics and the strength of evidence
• Shows you how best to manage your patient
• EXAMS!• Paper 1 (30/150) and Paper 2 (30/100)• Critical Analysis of an article• 4 weeks preparation time
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Types of Data• Population Data• Census• Deprivation Index• Birth/Death rates
• Health Event Data• Hospital Episode Statistics• National Cancer Register• GP Research Database
• This data allows you to assess NEED
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Prevalence and Incidence• Prevalence• Number of people in a population with a disease at any given point
in time• E.g. The prevalence of asthma in Liverpool now is 40 per 1000 people• Tells you how widespread a disease is
• Incidence• New cases of a disease in a given time frame• E.g. The incidence of asthma in Liverpool from 1 January – 31
December 2012 was 5 per 1000 people• Tells you about RISK
• When would you see a high prevalence but low incidence and vice versa?
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Risk• Absolute Risk• The risk of getting a disease in a given population• E.g. the risk of having an MI in Liverpool is 1 in 50
• Relative Risk• The probability of getting a disease in one group compared to
another
• Relative Risk = exposed group non-exposed group
E.g the risk of having an MI in smokers is 1/5 and the risk in non-smokers is 1/20, so the relative risk is 4.
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Risk Reduction• Absolute Risk Reduction• The reduction in absolute risk when an intervention is applied to
a population group• E.g. Introduction of a new drug reduces risk of MI from 20% to
10%• ARR = 10% = 0.1
• Numbers Needed to Treat• The number of people that must be treated using a particular
intervention to prevent a bad outcome• 1 / ARR• 1 / 0.1 = 10• 10 people must be treated with the new drug to prevent one MI
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Study designs
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Study Design
• Case Report• Case Series• Population Case Series• Cross-sectional Study• Case Control Study• Cohort Study• Randomised Controlled Trial
Hierarchy
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Observational Studies• Everything except RCT
• Case Report• Single case study from one patient
• Case Series• Series of single patient reports
• Population Case Series• Case series in a defined geographical area
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Observational Studies• Cross-sectional Study• Looks at one characteristic at a point in time• Allows calculation of prevalence
• Case Control Study• These studies are RETROSPECTIVE• Compares those with the disease to those without• CANNOT PROVE CAUSALITY
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Case Control Studies
Don’t have Crohn’sDon’t have Crohn’s
Crohn’sCrohn’s
Smoker?Smoker?
Non-smoker?Non-smoker?
Non-smoker?Non-smoker?
Smoker?Smoker?
Present1980
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Observational Studies• Cross-sectional Study• Looks at one characteristic at a point in time• Allows calculation of prevalence
• Case Control Study• These studies are RETROSPECTIVE• Compares those with the disease to those without• CANNOT PROVE CAUSALITY
• Cohort Study• PROSPECTIVE studies• Follow two groups and record outcomes
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Cohort Studies
Non-smokersNon-smokers
SmokersSmokers
Get Crohn’sGet Crohn’s
Don’t get Crohn’sDon’t get Crohn’s
Don’t get Crohn’sDon’t get Crohn’s
Get Crohn’sGet Crohn’s
Present 2020
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Randomised Controlled Trial• The gold standard
• The only trial where YOU intervene
• Direct comparison of two standardised groups
• Control group and interventional group
• Most effective when patients researchers don’t know which group is which• BLINDING
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Randomised Controlled Trial
Group receiving new experimental
treatment
Group receiving new experimental
treatment
Group receiving current best treatment
Group receiving current best treatment
Measure mortality rate = 20%
Measure mortality rate = 20%
Measure mortality rate = 10%
Measure mortality rate = 10%
Present 2020
Shows that the new drug reduces mortality by 10% (absolute risk reduction)
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Analysis of data
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Measures of Central Tendency• Mean• Useful if all values are similar 50, 51, 53, 53, 54,
56, 56
• Median• Eliminates extreme values 22, 51, 53, 53, 54, 56, 98
• Mode• Analyses peaks in data 22, 22, 22, 51, 53, 98, 98, 98
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Standard Deviation• Allows you to see the spread of data
• A small SD shows that data is central around the mean and is, therefore, accurate
• A large SD shows data dispersion across a range of values and is, therefore, innaccurate
• You don’t need to know how to calculate SD!
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Statistical Devices• 95% Confidence Intervals• Gives the range of data you are confident the true result lies in• E.g. You expect 50% of the population to vote Labour• 95% CI says the true value lies between 45% and 55%
• Student’s t-test• Statistical test to determine how significant the results of a study
are• Uses a value known as a p-value• A p-value of less than 0.05 shows statistical significance and
demonstrates that the probability the results are due to chance is less than 5%
• A value of over 0.05 means study results are invalid
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Measures of Risk
• Odds• This is a measure of how likely something is to happen
• IT IS NOT THE SAME AS PROBABILITY
• It describes the chance of something happening versus it not happening
• E.g. the probability of rolling a six on a dice is 1 out of 6
• The odds of rolling a dice is 1 out of 5
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Odds Ratio• This is the ratio of two odds
• It tells you the odds of an event happening in one group compared to the same event in another group
• E.g we roll a dice. What is the odds ratio of rolling a six both times?
• (1/5) divided by (1/5) = 1
• The odds are the same in both groups
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Risk Ratio• This is similar to odds ratio, except probabilities are used• The risk ratio (relative risk) tells you the risk of developing a disease
related to a given risk factor
• It is calculated by dividing the exposed group by the non-exposed group
• If there is no increase in risk, the risk ratio is 1
• E.g. 30 people in the smoker group develop Crohn’s and 6 people in the non-smoker group develop Crohn’s
• 30/6 = 5 – You are 5 times more likely to get Crohn’s if you smoke
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Important!
•Odds ratio is used for case control studies
• Relative Risk is used for cohort studies
• You cannot use relative risk for case control studies because they do not
prove causality, and you cannot, therefore, estimate risk
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Problems with analysis
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Bias• Selection Bias• Bias in selecting study participants
• Volunteer Bias• Bias when only certain types of people volunteer
• Information Bias• Bias resulting from errors in in measurements of data
• Recall Bias• Bias created when patients are asked to remember information
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Confounders• Factors that may skew results as they correlate with both
variables
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Sample Size• This is important (and actually quite obvious!)
• You cannot draw pertinent conclusions from your data unless the sample size is large enough
• This helps to eliminate results that are due to chance
• There are ways of working out how large your sample size must be for any given study
• Covered in 3rd Year Critical Thinking Module
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Error• Type 1• Finding a difference between two datasets that isn’t really there
• Type 2• Missing a significant difference between two datasets
• Due to factors such as bias or chance
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Sensitivity and Specificity• Sensitivity• Measures how good a screening test is at identifying TRUE cases
of a disease
• True positives / True positives + false negatives (x100)
• Specificity• Measures how good a screening test is at identifying healthy
individuals with no disease
• True negatives / True negatives + false positives (x100)
• Very subtle difference between the two measurements
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Positive Negative
Positive 70 3
Negative 7 20
Screening method
Biopsy
Sensitivity = 70 / 70 + 3 = 96%
Specificity = 20 / 20 + 7 = 74%
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Positive Predictive Value• Tells us how good a diagnostic test is at identifying positive
patients• Similar to sensitivity• Represented as a decimal rather than percentage
• Calculated by True Positives / All positives
• 70 / 77 = 0.91
• A result of 1 would indicate a perfect test
• Negative predictive value also works in exactly the same way
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Other parts of PP• Bradford-Hill criteria• Wilson and Jugner criteria• Maxwell’s criteria• Impairment, Disability and Handicap• Ecological Fallacy• Utility and Opportunity Cost• Rates• Kaplan-Meier Plots• Prevention Paradox• Case Mix
• We can go through these another time if you’d like!
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Thank you