Bias

Question 1

Biases in SR & MA

Answer 1

Publication bias
Language bias
Duplicate data
Heterogeneity - statistical, clinical, methodological
Outdated studies

Question 2

Berkson bias

Answer 2

study sample taken from a sub-population

e.g. health-conscious people are more likely to join a trial

Question 3

Bradford Hill criteria

Answer 3

group of nine principles used in establishing evidence of a causal relationship between an exposure & an event

include strength, consistency, specificity, temporality

Question 4

Definition of confounder

Answer 4

variable that influences both exposure & outcome in triangular fashion, causing a spurious association not demonstrating causality

Question 5

Definition of selection bias

Answer 5

systematic differences between baseline characteristics of groups due to selective recruitment

Question 6

Definition of performance bias

Answer 6

systematic differences in care provided to different study groups other than the intervention of interest

Question 7

Definition of detection bias

Answer 7

systematic differences between groups in how outcomes are determined

Question 8

Definition of recall bias

Answer 8

systematic error that occurs when participants do not remember previous events or experiences accurately or omit details

Question 9

Definition of attrition bias

Answer 9

type of selection bias due to systematic differences in the number & way participants are lost from a study between study groups

Question 10

Definition of publication bias

Answer 10

when the outcome of a research study biases the decision to publish or otherwise distribute it

Question 11

What is bias

Answer 11

Factor which causes SYSTEMATIC over- or under- estimate of a particular result

Question 12

Types of biases that can occur in the designing of the study?

Answer 12

Selection bias, volunteer bias, channelling bias

Question 13

Biases that can occur when conducting the research?

Answer 13

Interview bias, recall bias, Hawthorne bias

Question 14

Biases that can occur in writing and publishing the study?

Answer 14

Publication bias, confounding

Question 15

What is confounding bias…?

Answer 15

Confounding is when you have an independent factor which is associated with both the exposure factor and the outcome of interest with the disease

Question 16

How can you minimise confounding factors?

Answer 16

Minimise through:
stratification,
multivariate analysis,
subgroup analysis
randomisation
restriction of criteria
matching

Question 17

What is internal validity and external validity?

Answer 17

Internal validity - accuracy of conclusions
External validity - generalisability of results

Question 18

Membership bias:

Answer 18

Explanation: Included patients are already participating in a study so might be more likely to look after their health, see the benefits of research, and educate themselves about treatment.

Question 19

Neyman/survival bias

Answer 19

Explanation of an example: Included patients who had to be alive months after the treatment, out-selecting those with more rapidly evolving conditions who may have realistic idea of the treatment effects

Question 20

Examples of confounders:

Answer 20

Age, ethnicity, sex, comorbidities (DM, alcohol intake), past surgery, previous family history, drug history, etc.

Question 21

Diagnostic purity bias

Answer 21

Comorbidity is excluded in the sample population, such that it does not reflect the true complexity of cases in the population

Question 22

Neyman bias (survival bias)

Answer 22

There is a time gap between the onset of a condition and the selection of the study population, such that some individuals with the condition are not available for selection.

Question 23

Volunteer bias

Answer 23

Individuals volunteer for studies but they differ some way from the target population e.g. they are more motivated to improve health

Question 24

Lead-time bias

Answer 24

Screening/testing increases the perceived survival time without affecting the course of the disease

Question 25

Observation bias

Answer 25

There is a problem with the way data is collected in the study, such that data has been unduly influenced by the expectations of the researchers and subjects

Question 26

Response bias

Answer 26

The subject answers questions in the way in which they think the researcher wants them to answer e.g. subjects in the experimental arm are more likely to give favourable responses

Question 27

Recall bias

Answer 27

Subjects selectively remember details from the past

Question 28

Exclusion bias

Answer 28

Results from differences in dropout rates between groups

Question 29

Hawthorne effect

Answer 29

Subjects alter their behaviour because they are aware they are being observed in a study

Question 30

Detection bias

Answer 30

Differences in how outcomes are assessed between groups

Question 31

Aggregation bias

Answer 31

Occurs when it is wrongly assumed that the trends seen in aggregated data also apply to individual data points

Question 32

What is a method to assess the potential role of publication bias?

Answer 32

Funnel plot

Question 33

Ecological fallacy

Answer 33

An ecological fallacy is a logical error that occurs when the characteristics of a group are attributed to an individual. In other words, ecological fallacies assume what is true for a population is true for the individual members of that population

Question 34

How does randomisation reduce confounding?

Answer 34

Aims to randomly distribute confounders between study groups

Question 35

How does restriction reduce confounding?

Answer 35

Restricts entry of individuals with confounding factors (risks bias in itself)

Question 36

How does matching reduce confounding?

Answer 36

Matching of individuals/groups aim for equal distribution of confounders

Question 37

How does stratification reduce confounding?

Answer 37

Confounders are distributed evenly within each stratum. Stratification allows the association between exposure and outcome to be examined within different strata of the confounding variable. This technique allows observation effects of an intervention in different subgroups (however, this becomes more difficult if there are several confounders). Creating several smaller groups reduces the power of the study.

Question 38

How does adjustment reduce confounding?

Answer 38

Usually distorted by choice of standard

Question 39

How does multivariate analysis reduce confounding?

Answer 39

Only works if you can identify and measure the confounders. This analysis is a statistical procedure for analysis of data involving more than one type of observation. Multivariate regression is an extension of multiple regression with one dependent variable and multiple independent variables.

Question 40

What are the advantages of multivariate analysis

Answer 40

Limited loss of power and ease of combining several confounders in one study