Bias's

Question 1

Selection bias

Answer 1

systematic differences between baseline characteristics of the groups (minimised by randomisation)

Question 2

Performance bias

Answer 2

Systematic differences between groups in care provided other than the intervention of interest (minimised by blinding and having set protocol)

Question 3

Detection bias

Answer 3

Systematic differences between groups in how outcomes are determined (minimised by blinding and having objective outcomes and standardising assessment)

Question 4

Recall bias

Answer 4

A systematic error caused by differences in the accuracy of recollections retrieved by study participants

Question 5

Publication bias

Answer 5

Only studies with positive results are published, not the neutral or negative studies (means can overestimate the effect of the treatment or intervention)

Question 6

Language bias

Answer 6

Studies with positive findings are more likely to be published in an English-language journal and are also more quickly than those with inconclusive or negative findings

Question 7

Power

Answer 7

Probability of picking up a significant difference where one exists ( the number needed to avoid a type II error)

Adequate power is generally 80% (0.8)

Factors associated:
- Sample size
- Alpha level (alpha level is the probability of rejecting the null hypothesis when the null hypothesis is true)
- Variability of outcome measure e.g. SD (lower variation = higher power)
- Minimum clinically significant difference
- Estimated attrition rate

Question 8

Type 1 error

Answer 8

Wrongful rejection of the null hypothesis (false positive)

Causes may include: sampling error, data dredging & confounding

Question 9

Type 2 error

Answer 9

Wrongful acceptance of the null hypothesis (false negative)

Can be caused by an underpowered study

Question 10

Confounder

Answer 10

A confounder has a triangular relationship between the exposure & outcome, but is it not along the causal pathway

Reduced through matching and randomisation (accounts for unknown confounders) and restriction (inclusion/exclusion which removes effect from study but reduces generalisability)
Can also be reduced through standardisation (e.g. age-standardised risk) and subgroup analysis (but losses power) or multivariate analysis (allows you to study multiple confounders)

Question 11

Interval validity

Answer 11

How well a study is designed to answer their question and hence the extent to which we can trust the reported outcome
A measure of how methodologically robust a study is and how well systematic bias is eliminated/accounted for

Question 12

External validity

Answer 12

How generalisable the outcomes of the study are to the target population (compared to the study population)

Question 13

What is within participant comparison

Answer 13

Participants are assessed before and after an intervention
Analysis is of the same participant

Question 14

What is a cross-over trial

Answer 14

Participants receive both the intervention and control in a random order
Often is separated by a washout period

Question 15

What is an N-of-1 trial

Answer 15

A single subject trial where an individual is the sole observation
Provides optimal intervention for an individual (e.g. optimal dose)

Question 16

What is a factorial design

Answer 16

Study that investigates multiple independent variables on an outcome measure (both separately and combined)

Question 17

What is a surrogate endpoint

Answer 17

When a biomarker (often easy to measure) is used to predict the likelihood of a clinical outcome

Pros = reduces required follow-up period and sample size and allows measurement when ideal outcome measure is excessively invasive or unethical

Cons = assumes a direct and guaranteed correlation between the biomarker and clinical outcome

Question 18

What is a composite endpoint

Answer 18

Where multiple clinical events/outcomes are combined to form the primary outcome e.g. composite cardiovascular endpoints (unstable angina, stroke and MI)

Pros = allows for higher event rate so need shorter trial and fewer participants suitable when individual events occur infrequently

Cons = distribution of events may be unclear, main driver may be less severe/clinically relevant

Question 19

P value

Answer 19

The calculated probability that a particular outcome has occurred due to chance
The calculated probability that the null hypothesis is true

Question 20

Confidence interval

Answer 20

A range of values between which the true population value lies 95% of the time

Question 21

Intention to treat

Answer 21

Statistical approach where all subjects are included in the analyses as members of the groups to which they are allocated, regardless of whether they completed the study or not

Pros = More closely reflects real life, Accounts for effect of adverse events & reduces attrition bias, Can help baseline characteristics of both groups to remain similar

Cons = Imputed value may be inaccurate (e.g. last observation carried forward)

Question 22

Per protocol analysis

Answer 22

Where the final analysis only includes the patients that completed the treatment they were originally allocated to

Pros = accurately reflects the effects of treatment, useful in non-inferiority trials

Cons = subject to attrition bias - control and intervention groups may no longer have similar characteristics

Question 23

Number needed to treat

Answer 23

Number of participants required to take a medication/have an intervention (compared with the control) to see one positive event
Is 1/ARR

Question 24

Precision

Answer 24

How much agreement there is between repeat measurements

Question 25

Accuracy

Answer 25

How close measurements are to the true value

Question 26

Absolute risk

Answer 26

Incidence rate of the outcome

Question 27

Absolute risk reduction

Answer 27

Incidence rate in the control group – incidence rate in the intervention/exposure group CER – EER

Question 28

Relative risk

Answer 28

The risk in the experimental group relative to that in the control group EER/CER

Question 29

Relative risk reduction

Answer 29

The risk reduction in the experimental group, relative to that in the control group (CER-EER)/CER

Question 30

Odds ratio

Answer 30

Likelihood that an outcome will occur given a particular exposure, compared to the likelihood of the outcome occurring without the exposure

Question 31

Hazard ratio

Answer 31

is the relatively likelihood of the event occurring in the treatment vs control subjects at any given time point

Question 32

Sensitivity

Answer 32

How well the test is able to detect those with the disease True Positive (correctly detected with disease) /True Positive +False Negative (total with disease)

Question 33

Specificity

Answer 33

How well the test is able to rule out those without the disease True Negative (correctly detected without disease) /True Negative + False Positive (total without disease)

Question 34

Positive predictive value

Answer 34

The percentage of people that test positive, that truly have the disease True Positive (correctly detected with disease / True Positive +False Positive (total that tested positive)

Question 35

Negative predictive value

Answer 35

The percentage of people that test negative, that truly do NOT have the disease True Negative (correctly detected without disease)/ True Negative + False Negative (total that tested negative)

Question 36

Systematic bias

Answer 36

Anything that impacts the study result in a non-random way and leads to an incorrect estimate of effect or association

Question 37

Attrition bias

Answer 37

Systematic differences in withdrawals from the trial Reduced by intention-to-treat analysis

Question 38

Confounding bias

Answer 38

Refers to the presence of a factor that has a triangular relationship with the exposure and outcome, but is not along the causal pathway

Question 39

Berkson bias

Answer 39

Form of selection bias, in which the sample is taken from a subpopulation (not the general population) For example, participants being recruited from the hospital, rather than also community settings

Question 40

Advantages and disadvantages of an RCT

Answer 40

Advantages: - Gold standard for identification of treatment effects - Prospective design that can infer causality - Compares otherwise identical groups - Allows for meta analyses later on - Unbiased distribution of confounders - Blinding more likely Disadvantages: - May be underpowered - Expensive: time and money - Volunteer bias - Ethically problematic at times - Surrogate outcomes may not reflect outcomes that are important to patients

Question 41

Advantages and disadvantages of cohort studies

Answer 41

Advantages: - Identifies causal and temporal relationships - Gives information on prognosis - Good for rare exposure - Can answer questions about aetiology - ethically safe; - Allows determination of relative risk Disadvantages: - Expensive, time consuming - Not suitable for rare disease - Not suitable for disease with long latency periods - exposure may be linked to a hidden confounder; - blinding is difficult - randomisation not present - Attrition bias if subjects drop out (large cohort required)

Question 42

Advantages and disadvantages of case-control study

Answer 42

Advantages: - Identifies multiple risk factors for rare diseases - Suitable when long time between exposure and outcomes - Cheap and fast - Used when unethical to conduct an RCT Disadvantages: - Relies on quality of records - Selection bias (outcomes not established a priori) - Recall bias - Does not identify temporal association

Question 43

Advantages and disadvantages of cross-sectional study

Answer 43

A study that examines the relationship between diseases (or other health-related characteristics) and other variables of interest as they exist in a defined population at one particular time (ie exposure and outcomes are both measured at the same time). Best for quantifying the prevalence of a disease or risk factor, and for quantifying the accuracy of a diagnostic test - is descriptive and may be a survey Advantages: - Identifies trends - Cheap and fast Disadvantages: - Static - Does not deduce causality on association at best