# Confounding/ controlling for confounding. Flashcards

1
Q

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

A

The design or analysis stage.

2
Q

What is a crude association?

A

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

3
Q

Describe what is meant by confounding.

A

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

4
Q

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

A
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.
5
Q

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

A

ORnonsmoke = ORsmoke.

6
Q

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

A

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.

7
Q

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

A

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.

8
Q

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?

A

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

9
Q

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?

A

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

10
Q

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

A

Current biological knowledge.

11
Q

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

A
1. Efficiency is reduced.

2. Confidence intervals become wider.

12
Q

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

A

13
Q

What sort of confounding is this?

A

Positive confounding.

14
Q

What sort of confounding is this?

A

Negative confounding.

15
Q

Name an example of negative confounding?

A

Oral contraceptives reducing myocardial infarction.

16
Q

What is qualitative confounding?

A

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

17
Q

If the exposure can not change (ie sex) then can something ever truly be a confounder?

A

No.

18
Q

If the exposure is able to change a ‘confounder’ then the ‘confounder’ is not a true confounder. What is it?

A

A mediator.

19
Q

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

A

20
Q

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

A

False. There is no statistical test for confounding.

21
Q

Residual confounding always exists. What can cause this (5 things).

A
1. Categories of confounders are too broad.
2. Imperfect surrogates used ( a variable with varying importance in difference settings).
3. Misclassification of confounders.
4. Confounders remaining uncontrolled.
5. Limited biological knowledge for the causal definition.
22
Q

What source of residual confounding becomes more important as an exposure becomes more closely related to a disease?

A

Residual confounding caused by categories of confounding being too broad.

23
Q

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

A
1. Randomisation (in RCTS.)
2. Restriction.
3. Matching.
24
Q

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

A
1. Stratification.

25
Q

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

A

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

26
Q

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

A

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

27
Q

What is the downside of using randomisation to adjust for a confounder?

A

Can only be used in animal and human experiments.

28
Q

How does the restriction method help adjust for confounders?

A

Eligibility criteria is limited to a certain subgroup.

29
Q

What sort of differences can be limited via the use of restriction as a method to control for confounders?

A

Genetic differences.

30
Q

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

A
1. The pool of available subjects is reduced.

2. External validity reduced.

31
Q

Name an example of a genetic confounder?

A

Ethnicity.

32
Q

What type of studies can individual matching be used on?

A

Cohort or case control.

33
Q

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

A

Bias comparison between cases and controls.

34
Q

What can matching for a strong confounder do?

A

Increase statistical power and with it efficacy.

35
Q

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

A

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).

36
Q

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

A

Very important ones, such as age and ethnicity.

37
Q

Once you have matched for a predictor what can you not do?

A

Study the effect of that predictor on the outcome.

38
Q

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

A

Matching for a non confounder.

39
Q

What is ‘Over matching’?

A

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.

40
Q

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

A

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

41
Q

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.

A
1. McNemar test.

2. Conditional logistic regression.

42
Q

Name a technique that can be used for matching which does not have a paired design.

A

Alternative frequency density matching.

43
Q

What ratio of cases:controls maximises statical power.

A

1:4.

44
Q

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

A

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

45
Q

What test can examine homogeneity of stratum specific OR after stratification has occurred?

A

Breslow-Day test.

46
Q

What test is used to calculate a summary OR?

A

47
Q

What are two advantages of using stratification to control for confounding?

A
1. Can examine raw data in detail.

2. Can obtain a summary adjusted OR.

48
Q

What are three disadvantages of using stratification to control for confounding?

A
1. When stratifying for multiple confounders data becomes sparse.
2. Imprecise when samples are smaller.
3. Does not work for continuous confounders (unless you make them categorical which can be unfavourable).
49
Q

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

A
1. Can study numerous cofounders.
2. Can look at large datasets.
3. Confounders can have any distribution.
4. Can include non linear terms.
50
Q

What type of health conditions have multiple cofounders that can be accounted for by regression?

A

Chronic.

51
Q

Regression can allow for the addition of non linear terms when adjusting for confounders. What method does not allow you to do this?

A

Stratification.

52
Q

When does confounding occur?

A

When the effect of the exposure is mixed with the exposure of another variable.

53
Q

What type of bias is confounding?

A

Systematic bias.

54
Q

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

A

Randomisation in a RCT.

55
Q

What are the two steps in propensity score matching/adjustment?

A
1. Regress exposure on confounders to check the exposure is related to the confounder.
2. Regress the value obtained in the first regression onto the outcome variable.
56
Q

What sort of outcomes does propensity score matching work best on and why?

A

Rare outcomes. Smaller samples mean less chance of overlap of the separate distributions caused by the confounding factor.