## Types of Study

**Case control study**

- observational and retrospective
- compares a group with a disease to a group without
- Looks for prior exposure or risk factor
- Asks “what happened”?
*Measures*(think of odd case)**Odds Ratio**

#### Cohort Study

- Observational and prospective
- Compares two groups, one with exposure, one without exposure
- Looks to see if exposure increases likelyhood of disease
- Asks “what will happen”
*Measures*(Think of Arnold in court – relative co(h)ort)**Relative Risk**

#### Cross Sectional Study

- Observational
- Collects data from a group of people to asses frequency of disease and related risk factors at a particular point in time
- Asks “What is happening”
*Can show risk factor association – but not causality*

#### Twin Concordance Study

- Compares the frequency with which both monozygotic twins and dizygotic twins or both sets develop a disease
- Compares sibling raised by biolgic vs adoptive
*Measures heritability*

#### Adoptive Study

- Compares siblings raised with biologic vs adoptive
*Measures heritability and influence of environment*

## Evaluation of Diagnostic Tests

Uses a 2 x 2 table to compare test results with the actual presence of the disease

TP = True Positive, FP = False Positive, TN = True Negative, FN = False Negative.

with this be aware that the disease and test may be flipped around which will inver the table

###### Sensitivity

= all the people with the disease who test positive**Sensitivity**- Sensitivity = TP/(TP+FN)
- It’s the ability of the test to detect the disease when it is present
- Value approaching 1 is desirable – indicates a low false negative
- If it’s a very sensitive test (when negative) will rule OUT the disease – SeNsitive = OUT –> SNOUT

###### Specificity

- Specificity = proportion of those without the disease who test negative
- Specificity = TN / (FP + TN)
- The ability of the test to indicate NON disease when disease is truely not present
- A value of approaching 1 is good – indicates a low false-positive rate
- A very specific test rules IN a disease with a high degree of certainty – SPecificity = In –> SPIN

#### Positive Predictive Value

- The portion of positive test results which are true positives
- Given a positive test result this is the probability the person actually has the disease
- NB: If the prevalance of a disease in a population is low even a test with a high sensitivity and specificity will have a low postitive predictive value
- PPV = TP / (TP + FP)

#### Negative Predictive Value

- The probablity of a person actually not having the disease given a negative test result
- NPV = TN / (TN + FN)

#### Likelyhood Ratio for positive test result

- How much the odds of a disease increase when the test result is positive
- sensitivity / (1 – specificity)

#### Likelyhood ratio for negative test result

- How much the odds of the disease decrease when the test result is negative
- (1 – sensitivity) / specificity

*Neumonic: Remember the PIG is always ontop – (SNOUT = sensitivity is always in the numerator) *

## Prevalence Vs Incidence

Point Prevalence = Total cases in a population at a given time / . total population at a give time

Incidence = new cases in population over a given time period / . total population at risk in that time period

Prevalence = incidence x disease duration

For chronic diseases prevalence is more than incidence

For acute diseases prevalence is same as incidence

*Note: when doing calculations – those with the disease or who have previously had it are not considered at risk for that disease*

# Odds Ratio Vs Relative Risk

- EE = Experimental group where the event happened
- EN = Experimental group where the event didn’t happen (negative)
- CE = Control group where the event happened
- CN = Control group where the event didn’t happen (never happened / negative)
- ES = Total Number of Subjects in the Experimental group
- CS = Total number of subjects in the Control Group
- EER = Experiment Event Rate
- CER = Control Event Rate

#### Odds Ratio OR

- This is used for
*Case control studies* - Mneumonic – Think of this ODD suitCASE
- Odds ratios are used for Case Control studies

Odds ratio = Odds of having the disease in the exposed group divided by the odds of having the disease in the unexposed group

*Exposed group up top – imagine a flasher at the top of the railway bridge *

**Odds ratio = (EE / EN) / (CE / CN) **

Or Odds ratio = (EE x CN) / (CE x EN)

#### Relative Risk (RR)

- Used for cohort studies
*Nmeumonic: Think of Arnold in Court – Relative in Court = Relative Cohort**Relative Risk Uses TOTALS (ES & CS) – Arnold in court – a TOTAL FUCKUP (Odds ratio – doesn’t use totals)*- Relative probability of getting a disease in the exposed group compared to the unexposed group
- Calculated as the percentage with the disease in the exposed group divided by percentage with disease in the unexposed group
- (AGAIN the exposed group is upto – flasher ontop of railway bridge)
**RR = (EE / ES) / (CE / CS)**- RR = EER/CER

So same thing written a different way would be to say that Relative risk – used for cohort studies is EER / CER

(relative risk = experimental event rate / control event rate)

#### Relative Risk Reduction

RRR = ARR / CER

(Lion = Pirate / cerebellum)

#### Attributable Risk

- The difference in risk between exposed and unexposed groups
- or the proportion of disease occurrence which are attributable to exposure
- Attributable risk = (EE/ES) – (CE/CS)
- Or AR = EER – CER

#### Absolute Risk Reduction (or absolute risk increase)

- The reduction or increase in risk with a treatment when compared to a placebo
- The difference in the event rate in the intervention group compared with the event rate in the control group
- ARR = CER – EER
- If It’s CER-EER < 0 then it’s an absoulte risk reduction
- If CER – EER > 0 then it’s an absolute risk increase

**Attributable risk and absolute risk are opposite – Attributable risk is EER – CER, Absoulte risk recuction is CER – EER. **

Att Eer Cer

Ab Cer Er (abs cool – er)

#### Number Needed to Treat

NNT = 1/ARR

#### Number Needed To Harm

NNH = 1 / Absolute risk increase

## Worked Example

EER = EE/ES = 15/150 = 0.1 = 10%

CER = CE/CS = 100/250 = 0.4 = 40%

Absolute Risk Reduction = ARR = CER – EER (abs cool er) = 0.4 – 0.1 = 0.3 = 30%

Relative Risk Reduction = ARR / CER (lion = pirate / cerebellum) = 0.3/0.4 = 0.75 = 75%

Number needed to treat = 1/lion = 1/ARR = 1/0.3 = 3.33

Relative Risk = EER / CER = (EE/ES) / (CE/CS) = 0.1/0.4 = 0.25 = 25%

Odds Ratio = (it’s odd it doesn’t have total) = (EE/EN)/(CE/CN) = (15/135)/(100/150) = -0.111/0.666 = 0.167

Attributable Risk = (At risk ere if youre cqueer) = At Risk = ERR – CER = 0.1 = 0.4 = -0.3

## Bias

### Type 1 error (α)

- stating that there is an effect or a difference when none exists – accepting the experimental hypothesis in error
- p = probability of making a type 1 error
- p is judged against a preset level of significance – usually p < 0.05
- AKA “false positive error”

## Type 2 error (β)

- stating that there is not an effect of difference where one does exist
- β is a “False negative error”
- accepting a nul hypothesis which isn’t actually the case

## Power

- Probability of correctly rejecting the null hypothesis or correctly accepting the experimental hypothesis
- Power = 1 – β

## Normal distribution

So 68% fall within one standard deviation of the mean, 95% fall within 2 standard deviations of the mean, 99.7% fall within 3 standard deviations of the mean.

95% confidence interval = Mean +/- 1.96 x Standard Error

Standard Error = Standard deviation / √number of patients

**t-test:**checks the differences between the**MEANS of 2**groups*(MR T is MEAN!)***ANOVA**checks the differences between the**means of 3 groups**(MR T AND ANOVER! – mr t is two – add another and you have the means of three groups)**ANCOVA****Chi Squared (X2)**check the differences between percentages of proportions of categorical variable (like eye colour) (not means)

Metanalysis Displaying / interpretation of data

## Forest Plot / Blobogram

**Blob / Square**

- findings from each study are a blob or square
- If the square is to the left the new treatment is better, if to the right it’s worse
- The size of the square is proportional to the precision of the study (roughly proportional to the sample size)

#### The horizontal line on each square –

- this representst the 95% confidence interval
- represents the UNCERTAINTY of estimate of treatment effect
- The wider the line the less certainty
- If the line passes the vertical line of no effect it means the study is not statistically significant

#### The diamond

- the aggregate effect found from all studies are displayed as a diamond
- the width of the diamond shows the 95% confidence interval
- If the diamond crosses the vertical line of no effect, it means overall there is no statistically significant effect

#### The vertical line

- line of no effect
- odds ratio of 1
- Risk and benefits are equal
- any statistically significant study does not cross this line

#### Heterogenicity

- if all studies are kinda reporting different things this will be super low
- if the P value is say larger than 0.1 then we can be reassured they’re all measuring pretty similar things

# Funnel Plot

- Funnel plots are designed to highlight the existance of publication bias in systematic reviews & metaanyalysis
- It assumes that large studies will be near the average and smaller studies will be spread on both sides of the average
- Variation from this assumption can indicate publication bias

Its sometimes difficult to identify this by eye so **Egger’s Test **is a statistical test to check for publication bias – it is a formal way of looking at the funnel plot and working out whether there’s kinda like studies missing.

# Cox Model

- analyses survival data
- isolates effects of treatment from effects of other variables

# Kaplan – Meire Method

- Censored survival time – you can’t know when some people in the study are gonna die cos they’re still alive
- so the Kaplan meire method – it calculates the proportion of such people surviving a given lenght of time