Stats/Epi

Question 1

Outline types of studies & examples?

Answer 1

Grade 1: Systematic review/meta-analysis

RCT
- Intervention vs placebo
- Can prove causality
- Randomisation reduces bias
i.e new asthma puffer- 1x group receives placebo, other receives drug, participants/administrators/data collectors blinded

Cohort studies
- Observational (association)- exposure vs non-exposure
- Prospective = exp then f/u
- Retrospective = outcome then look back
- RR >3
-i.e 500 children followed up to investigate effects of extreme prematurity on learning at 2,5,10yrs (compared with 500 non prems)

Case control studies
- Observational (association)
- Disease vs no disease
- Small numbers, but can be the most biased
- OR >4
i.e 100 children with obesity compared to 100 children without- environments studied

Cross sectional
- Freq of disease/risk factors at point in time
- Can develop association
- Prevalence

Ecological study
- Attempts to relate exposures in an environment to health outcomes
i.e incidence of SIDS in smoker households vs non-smoker

Survival study
- Looks at the PR(event of interest) occurring over time period
- i.e onset of disease after exposure at childcare within 2 weeks

Question 2

Statistical tests and their uses?

Answer 2

Chi Squared (CATEGORIES): significance between two variables

T-Test (TWO GROUPS): difference in quantitative variable between two groups

ANOVA (MORE > 2): differences in quantitative variable in >2 groups

Kappa (lad- are you reliable): percentage agreement between different raters 0 = no agreement, 1 = perfect agreement

Question 3

Type 1 & Type 2 error definition

Answer 3

Type 1 (a)= fails to recognise NO difference
- rejects null but actually no difference
Type 2 (b)= not big enough to pick up difference
- fail to reject null hypothesis as study too small

Question 4

Adjustment for type 1error?

Answer 4

Setting p value <0.05 (5%)
- Less chance event is unexpected/due to chance

Question 5

Adjustment for type 2 error?

Answer 5

Setting power (increase sample size)
- To determine actual effect

Question 6

Sensitivity

Answer 6

SnOUT
- Number of true positives correctly identified by test
- Useful in ruling out

Question 7

Specificity

Answer 7

SpIN
- Number of true negatives correctly identified by test
- Helpful in ruling disease in

Question 8

Positive predictive value

Answer 8

The proportion of people who test positive who actually have the disease.

Question 9

Negative predictive value

Answer 9

The proportion of people who test negative who do not have the disease.

Question 10

How to calculate positive & negative LR?

Answer 10

Positive LR
* = sensitivity/(1-specificity).
* = (a/(a+c))/(1-(d/(d+b)))

Negative LR
* = (1- sensitivity)/specificity
* = (1-(a/(a+c)))/ (d/(d+b))