Stats

Question 1

The p value

Answer 1

the probability of obtaining a result at least as extreme as the one that was actually observed, assuming that the null hypothesis is true.

Question 2

• Type I error

Answer 2

the null hypothesis is rejected when it is true - i.e. Showing a difference between two
groups when it doesn’t exist (= significance level)

(False positive)

Question 3

Type II error

Answer 3

the null hypothesis is accepted when it is false - i.e. Failing to spot a difference when
one really exists

Question 4

power of a study

Answer 4

probability of (correctly) rejecting the null hypothesis when it is false
* power = 1 - the probability of a type II error
* power can be ↑ by increasing the sample size

Question 5

Correlation tests

Answer 5

Parametric (normally distributed): Pearson’s coefficient
* Non-parametric: Spearman’s coefficient

Question 6

Parametric tests

Answer 6

Student’s t-test - paired or unpaired
* Pearson’s product-moment coefficient - correlation

Question 7

Non-parametric tests

Answer 7

Mann-Whitney - unpaired data

• Wilcoxon matched-pairs - compares two sets of observations on a single sample
• Chi-squared test - used to compare proportions or percentages
• Spearman, Kendall rank – correlation
• McNemar’s test is used on nominal data to determine whether the row and column marginal frequencies are equal

Question 8

Funnel Plot

Answer 8

primarily used to demonstrate the existence of publication bias in meta-analyses. Funnel plots are usually drawn with treatment effects on the horizontal axis and study size on the vertical axis.

Question 9

Central Limit Theorem (CLT)

Answer 9

the random sampling distribution of mean would always tend to be normal irrespective of the population distribution for which the sample were drown.
The mean of the random sampling distribution of means is equal to the mean of the original population

Question 10

Confidence Interval (CI):

Answer 10

describes the range of value around a mean, an odds ratio, a pvalue or a standard deviation within which the true value lies.

95% CI → 5% chance the true mean value for variable lies outside the range CI = mean ± 2xSE (Standard Error)

Question 11

Normal Distribution

Answer 11

known as Gaussian distribution or ‘bell-shaped’ distribution. It describes the spread of many biological and clinical measurements

Question 12

Standard deviation

Answer 12

The standard deviation (SD) represents the average difference each observation in a sample lies
from the sample mean
* SD = square root (variance)

Question 13

Positively skewed distribution:

Answer 13

mean > median > mode

Question 14

Negatively skewed distribution

Answer 14

mean < median < mode

Question 15

The Standard Error of the Mean (SEM

Answer 15

is a measure of the spread expected for the mean of the observations - i.e. how ‘accurate’ the calculated sample mean is from the true population mean

Question 16

Relative Risk (RR)

Answer 16

ratio of risk in the experimental group (experimental event rate, EER) to risk in the control group (control event rate, CER)

EER / CER

Question 17

Control event rate

Answer 17

(Number who had particular outcome with the control) / Total number who
had the control)

Question 18

Experimental event rat

Answer 18

rate at which events occur in the experimental group

(Number who had particular outcome with the intervention) / (Total
number who had the intervention)

Question 19

Relative risk reduction (RRR)

Answer 19

calculated by dividing the absolute risk reduction by the control event rate

RRR = (CER - EER) / CER

Question 20

The Hazard Ratio (HR)

Answer 20

similar to relative risk but is used when risk is not constant to
time. It is typically used when analysing survival over time

Question 21

Numbers Needed to Treat

Answer 21

Numbers needed to treat (NNT) is a measure that indicates how many patients would require an intervention to ↓ the expected number of outcomes by 1. It is rounded to the next highest whole number

NNT = 1 / (CER - EER), or 1 / Absolute Risk Reduction

Question 22

Odds Ratio

Answer 22

ratio of the odds of a particular outcome with experimental treatment and that of control

Question 23

Pre-test probability

Answer 23

The proportion of people with the target disorder in the population at risk at a specific time (point prevalence) or time interval (period prevalence)
For example, the prevalence of rheumatoid arthritis in the UK is 1%

Question 24

Post-test probability

Answer 24

The proportion of patients with that particular test result who have the target disorder Post-test probability = post test odds / (1 + post-test odds)

Question 25

Pre-test odds

Answer 25

The odds that the patient has the target disorder before the test is carried out Pre-test odds = pre-test probability / (1 - pre-test probability)

Question 26

The incidence

Answer 26

is the number of new cases per population in a given time period.

Question 27

prevalence

Answer 27

otal number of cases per population at a particular point in time.

Question 28

sensitivity

Answer 28

100% means that the test recognizes all sick people as such. Thus in a high sensitivity test, a negative result is used to rule out the disease.

TP / (TP + FN) how many of the sick patients can the test identify by %

Question 29

positive predictive value

Answer 29

ratio of true positives to combined true and false positives

TP / (TP + FP) how many of the test +ve samples are actually sick

Question 30

Specificity

Answer 30

TN / (TN + FP) how many of the healthy patients can the test identify by %

Question 31

Negative predictive value

Answer 31

TN / (TN + FN) how many of the test –ve samples are actually healthy

Question 32

Screening: Wilson and Junger Criteria

Answer 32

The condition should be an important public health problem
2. There should be an acceptable treatment for patients with recognised disease
3. Facilities for diagnosis and treatment should be available
4. There should be a recognised latent or early symptomatic stage
5. The natural history of the condition, including its development from latent to declared disease
should be adequately understood
6. There should be a suitable test or examination
7. The test or examination should be acceptable to the population
8. There should be agreed policy on whom to treat
9. The cost of case-finding (including diagnosis and subsequent treatment of patients) should be
economically balanced in relation to the possible expenditure as a whole
10. Case-finding should be a continuous process and not a ‘once and for all’ project

Question 33

Randomised controlled trial

Answer 33

Participants randomly allocated to intervention or control group (e.g. standard treatment or placebo)
* Practical or ethical problems may limit use

Question 34

• Superiority trail

Answer 34

whilst this may seem the natural aim of a trial one problem is the large sample size needed to show a significant benefit over an existing treatment

Question 35

Equivalence

Answer 35

an equivalence margin is defined (-delta to +delta) on a specified outcome. If the confidence interval of the difference between the two drugs lies within the equivalence margin then the drugs may be assumed to have a similar effect

Question 36

Non-inferiority

Answer 36

imilar to equivalence trials but only the lower confidence interval needs to lie within the equivalence margin (i.e. -delta). Small sample sizes are needed for these trials. Once a drug has been shown to be non-inferior large studies may be performed to show superiority