PP (Study Design) for 2nd YearDave Garbera F1 Arrowe Park Hospital

Learning Objectives• Population Measures

• Study Design

• Statistical Devices

• Problems with Analysis

• Sensitivity, Specificity and Positive Predictive Value

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

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

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?

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.

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

Study designs

Study Design

• Case Report• Case Series• Population Case Series• Cross-sectional Study• Case Control Study• Cohort Study• Randomised Controlled Trial

Hierarchy

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

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

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

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

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

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

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)

Analysis of data

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

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!

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

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

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

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

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

Problems with analysis

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

Confounders• Factors that may skew results as they correlate with both

variables

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

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

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

Positive Negative

Positive 70 3

Negative 7 20

Screening method

Biopsy

Sensitivity = 70 / 70 + 3 = 96%

Specificity = 20 / 20 + 7 = 74%

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

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!

Thank you