Confounding/ controlling for confounding.

Question 1

At what stage(s) of the study can confounding be corrected for?

Answer 1

The design or analysis stage.

Question 2

What is a crude association?

Answer 2

An association between an exposure and an outcome before adjusting for any confounders.

Question 3

Describe what is meant by confounding.

Answer 3

An extraneous variable that is a common cause of an exposure and outcome that fully or partially explains the observed effect.

Question 4

What three conditions need to be satisfied at the data analysis stage for something to be a confounder?

Answer 4

1. Association with the study exposure in the control group.
2. Associated with the disease in the unexposed group.
3. Not on the causal pathway for exposure-disease.

Question 5

If you stratify by a potential confounder (in this case smoking) what will the H0 be?

Answer 5

ORnonsmoke = ORsmoke.

Question 6

What is a Mantel-Haenazel OR and how is it used?

Answer 6

A OR adjusted for a confounding variable. This will take into account the weighted averages of the separate OR’s of the confounding groups. This adjusted OR can then be compared to the crude OR. if there is a roughly 15% different the factor can then be counted as a confounder.

Question 7

You only need to correct for confounders if the Mantel-Haenazel ratio is 15% different from the crude OR. True or false?

Answer 7

False. You should still correct for a confounding factor as it is still an error, even if it is not technically classes as a confounder.

Question 8

If the analysis of data suggests that there is no confounding effect you should accept that there is no cofounding effect, even if literature says that something may be a biologically confounding factor. This is because the statistical analysis is more robust. True or false?

Answer 8

False. The causal definition is the most important thing to consider when deciding wether if something is a confounder.

Question 9

If the analysis of data suggests that there is a confounding effect you should accept that there is a cofounding effect, even if past literature says that something is not a biologically confounding factor. This is because the statistical analysis is more robust. True or false?

Answer 9

False. The causal definition is the most important thing to consider when deciding wether if something is a confounder.

Question 10

What is the limiting factor when using the causal definition in deciding whether something is a confounding factor?

Answer 10

Current biological knowledge.

Question 11

It is not ideal to adjust for every single factor that could be confounding. Why? (2 reasons).

Answer 11

1. Efficiency is reduced.

2. Confidence intervals become wider.

Question 12

If you adjust for a non confounder what can it produce?

Answer 12

Additional bias.

Question 13

What sort of confounding is this?

crude odd > adjusted odd.

Answer 13

Positive confounding.

Question 14

What sort of confounding is this?

crude odd < adjusted odd.

Answer 14

Negative confounding.

Question 15

What is qualitative confounding?

Answer 15

When one OR is less than 1 and one is more than 1.

Question 16

If a confounder is very highly related to an exposure (‘Excessive correlation/ over adjustment) what can adjusting it also do?

Answer 16

Adjust for the exposure.

Question 17

Computationally you can adjust for confounding, but it is complex. True or false?

Answer 17

False. There is no statistical test for confounding.

Question 18

What three ways can you control for confounding at the design stage of a study?

Answer 18

1. Randomisation (in RCTS.)
2. Restriction.
3. Matching.

Question 19

What two ways can you control for confounding at the analysis stage of a study?

Answer 19

1. Stratification.

2. Direct statistical adjustment.

Question 20

What is the H0 used after stratifying for a potential confounder?

Answer 20

There is no significant difference between the OR in the stratified groups.

Question 21

What is the best method to use when adjusting for confounding? Why is this the best method?

Answer 21

Randomisation (design stage). This is best as you can adjust for both known and unknown covariates.

Question 22

How does the restriction method help adjust for confounders?

Answer 22

Eligibility criteria is limited to a certain subgroup.

Question 23

What are the downsides of using restriction to adjust for confounders (2 things)?

Answer 23

1. The pool of available subjects is reduced.

2. External validity reduced.

Question 24

What is eliminated through the use of individual matching when controlling for confounders?

Answer 24

Bias comparison between cases and controls.

Question 25

What can matching for a strong confounder do?

Answer 25

Increase statistical power and with it efficacy.

Question 26

What do you risk happening if you plan to adjust at the analysis stage instead of the design stage?

Answer 26

There may be distinct distributions due to confounding due to chance. Without an overlap of distributions you can not perform adjustment (0’s will be present in the 2 by 2 table).

Question 27

What sort of confounders are normally adjusted for at the design stage?

Answer 27

Very important ones, such as age and ethnicity.

Question 28

Matching for a strong confounder can improve statistical efficacy. What can reduce it?

Answer 28

Matching for a non confounder.

Question 29

What is ‘Over matching’?

Answer 29

A phenomenon that occurs when you match for a variable that is too strongly correlated to an exposure resulting in a reduction of the effect that the exposure has on the outcome.

Question 30

‘If you have a strong indication that something is a confounder you should match for it.’ Why is this statement false?

Answer 30

As you can only practically match for a few confounders. You should hence match for the most important ones.

Question 31

When you have used matching to reduce confounding you need to use specific statistical tests to account for this. Name two examples of these tests.

Answer 31

1. McNemar test.

2. Conditional logistic regression.

Question 32

What ratio of cases:controls maximises statical power.

Answer 32

1:4.

Question 33

What is the odds ratio equation used to calculate the OR after matching?

Answer 33

b/c (exposed case and nonexposed control)/(exposed control and non exposed case).

Question 34

What are four advantages of using regression models to control for confounding?

Answer 34

1. Can study numerous cofounders.
2. Can look at large datasets.
3. Confounders can have any distribution.
4. Can include non linear terms.

Question 35

What type of bias is confounding?

Answer 35

Systematic bias.

Question 36

By accounting for confounding factors in a case control or cohort study what are you essentially trying to imitate?

Answer 36

Randomisation in a RCT.