stats

Question 1

odds

Answer 1

a+c/ B+D (yes/no)

Question 2

odds ratio

Answer 2

Odds treatment/Odds control
ad/bc
use in case control/cross section

Question 3

risk

Answer 3

yes/Yes+no

A+C/ A+B+C+D

Question 4

relative risk

Answer 4

Risk treatment/Risk control

a/a+b devide by c/c+d

Question 5

Confidence interval

Answer 5

range of plausible values for some summary measure

In repeated studies 95% of the confidence intervals will cover the true value of the summary measure

Question 6

sensitivity

Answer 6

proportion of true positive

A/A+C

Question 7

specificity

Answer 7

proportion of true negative

D/D+B

Question 8

Positive predictive value

Answer 8

probability that a subject with a positive test result actually has the disease

A/A+B

sensitivity x prevalence/ sensitivity x prevalence + (1-spec)x (1-prev)

depend on sensitivity and specificity
Depend on prevalence

Question 9

Negative predictive value

Answer 9

probability that a subject with a negative test result does not have the disease

D/C+D

spec x (1-prev)/ (1-sen)xprev + spec x(1-prev)

depend on sensitivity and specificity
Depend on prevalence

Question 10

If the prevalence is low

Answer 10

false positives are likely,
even if sensitivity is high

Question 11

selection bias

Answer 11

2 groups in study different due to allocation flaws, drop out, chance

Question 12

Information bias

Answer 12

info collected incorrect
e.g. recall bias

Question 13

confounding

Answer 13

stratify

a variable that influences both the dependent variable and independent variable, causing a spurious association

Question 14

But how do we stratify confounders that we don’t know
about yet?

Answer 14

Randomise

Question 15

Intention-to-treat

Answer 15

• Analysing data according to the treatment assigned, rather than what treatment was actually administered
  Dropouts are included in the analysis
• Avoids selection bias due to unequal dropouts

May underestimate treatment efficacy, as the treatment
effect is diluted by
* Dropouts
* Crossover
* Drop-ins to other treatment

Question 16

type 1 error

Answer 16

Incorrectly finding a difference when there really isn’t one

false positive result

due to chance/bias

Question 17

p value

Answer 17

The probability of a type 1 error due to chance

àanything less than a 5% chance of type 1 error (ie any p-value
less than 0.05) is acceptable enough for the observed difference
to be considered significant

Question 18

type 2 error

Answer 18

Type 2 error refers to finding no difference between
2 groups, when in fact one exists

false negative result

Most commonly this is due to sample size being too
small

Question 19

Power of study

Answer 19

The chance of a type 2 error is calculated at the
outset of a study, and is denoted by beta (β)
* The power of a study is 1 – β
Power is defined as the chance of finding a significant
difference between 2 groups, when such a difference exists

3 factors affect the power of a study:
* The sample size
* The magnitude of the difference between the groups
being studied
* The p-value required for a significant result (ie the ‘α’)

Question 20

case control

Answer 20

Most useful for rare conditions, with relatively common exposures

patient with disease match with control
compare the exposure of the disease/control

Question 21

cohort study

Answer 21

A group of normal subjects is identified and followed over time to see if they develop a disease

diseases with relatively high
incidence and short lead-time

Question 22

P < 0.05 .

Answer 22

means that there is
less than 5% chance of
observing the trial result due
to random sampling if the null
hypothesis were true

That random sampling would
provide a smaller difference
than we measured more than
95% of the time.

Question 23

relative risk reduction

Answer 23

1- RR

1- (a/a+b devide by c/c+d)

This is the MARKETED benefit

Question 24

NNT

Answer 24

1/AAR

If the effect is small, the ARR will be very low

• The ARR takes into account the total number of patients in the study
  whereas the RRR cancels it out can give deceivingly impressive results
• ARR is a BETTER measure of benefit than RRR
• The best measure is NNT