Presentation on theme: "PP (Study Design) for 2 nd Year Dave Garbera F1 Arrowe Park Hospital."— Presentation transcript:
PP (Study Design) for 2 nd Year Dave 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 = people must be treated with the new drug to prevent one MI
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’s Crohn’s Smoker? Non-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-smokers Smokers Get Crohn’s Don’t get Crohn’s Get Crohn’s Present2020
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 current best treatment Measure mortality rate = 20% Measure mortality rate = 10% Present2020 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 similar50, 51, 53, 53, 54, 56, 56 Median Eliminates extreme values22, 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 3 rd 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 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!