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Confounding. Dr. Sunita Dodani Assistant Professor Family Medicine, CHS The Aga Khan University Pakistan. Learning objectives. To understand the role of confounders in a study To learn relationship between an exposure, disease and potential confounding factors

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confounding

Confounding

Dr. Sunita Dodani

Assistant Professor

Family Medicine, CHS

The Aga Khan University

Pakistan

learning objectives
Learning objectives
  • To understand the role of confounders in a study
  • To learn relationship between an exposure, disease and potential confounding factors
  • To understand difference between confounding and effect modification
  • To learn methods to control confounding in study designs and in data analysis
performance objectives
Performance objectives

After this lecture the student will be able to:

  • Differentiate the role of a confounder and a exposure in a study
  • Use methods to control effects of confounders in research projects
confounding1
Confounding
  • Confounding occurs when two factors are associated with each other, or “travel together” and the effect of one is confused with or distorted by the effect of the other.
  • A confounder is a variable which is associated with the exposure, and independent of that exposure is a risk factor of the disease
confounding2
Confounding

Examples:

  • Study one: found an association with smoking and loss of hairs.

The study was confounded by age

  • Study two: found improved outcome for maternal centers when compared to hospitals

Study might be confounded by highly motivated volunteers that may have selected these centers as an option

confounding3
Confounding
  • Confounders are generally correlates of other causal factors
    • HSV-2

Sexual activity

    • HPVCervical cancer
  • A confounder cannot be an intermediate link

in the causal pathway between exposure and disease

confounding4
Confounding
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  • In other words, confounding is a variable that is associated with the predictor variable and is a cause of the outcome variable
  • Aside from bias, confounding is often the likely alternative explanation to cause-effect and the most important one to try to rule out.
  • In contrast to bias, confounding can be controlled at several levels of a study
effect modification
Effect modification
  • Effect modification is a type of interaction
  • When the strength of the relationship between two variables is different with respect to some third variable called effect modifier.
effect modification1
Effect modification
  • EXAMPLES 1

relationship between dose of thiazide and risk of sudden death.addition of K sparing drug modifies the effect at several doses.

effect modifier…….. K sparing drug

effect modification2
Effect modification

Example 2

People who take monoamine oxidase inhibitors (MAOI) are at risk of stroke if they eat certain foods such as cheese.

effect modifier………. MAOI

  • MAOI is not associated with eating cheese. This is not a confounder
coping with confounders
Coping with confounders

In the design phase

  • Investigators should be aware of confounders and able to control them
  • First list the variables (like age & sex) that may be associated with the predictor variable of interest as well as cause of the outcome
coping with confounders1
Coping with confounders

Two design phase strategies

  • Specification
  • Matching

Both sampling strategies

Specification:

Design inclusion criteria that specify a value of the potential confounder and exclude everyone with a different value

e.g In coffee and MI , only non smokers could be included in the study.if an association observed b/w coffee and MI, it obviously could not be due to smoking

coping with confounders2
Coping with confounders

Specification:

Advantages

  • Easily understood
  • Focuses only on subjects for the research question at hand

Disadvantages

  • Limits generalizability
  • May make it difficult to acquire adequate sample size
coping with confounders3
Coping with confounders

Matching (mostly in case control studies)

  • Selection of cases and controls with matching values of the confounding variable

Pair wise matching

e.g in coffee drinking study as a predictor of MI, each case (a patient with MI) could be matched with one or more controls that smoked roughly the same amount as the case (10-20 cigarettes/day)

coping with confounders4
Coping with confounders

Matching

Advantages:

  • Can eliminate influence of strong confounders
  • Can increase precision (power) by balancing the number of cases and controls in each stratum
  • May be sampling convenience making it easier to select controls
coping with confounders5
Coping with confounders

Matching

Disadvantages

  • Time consuming
  • Requires early decision as to which variables are predictors and which are confounders
  • Requires matched analysis
  • Creates the danger of over matching( matching on a factor which is not a founder, thereby reducing power)
coping with confounders6
Coping with confounders

In the Analysis

  • Stratification
  • Adjustment

Stratification

  • Ensures that only cases and controls with similar level of a potential confounding variable are compared.
  • It involves segregating the subjects into strata.
coping with confounders7
Coping with confounders

Stratification

Advantages

  • Easily understood
  • Flexible and reversible
  • Can choose which variable to stratify upon after data collection
coping with confounders8
Coping with confounders

Stratification

Disadvantages

  • Number of strata limited by sample size needed for each stratum
  • Few co variables can be considered
  • Few strata per co variable leads to less complete control of confounding
coping with confounders9
Coping with confounders

Statistical Adjustment

  • Several statistical techniques are available to adjust for confounders.
  • These techniques model the nature of the associations among the variable to isolate the effects of predictor variables and confounders
  • This require software for multivariate analysis
coping with confounders10
Coping with confounders

Statistical Adjustment

Advantages

  • Multiple confounders can be controlled simultaneously
  • Information in continuous variables can be fully used
  • Flexible and reversible
coping with confounders11
Coping with confounders

Statistical Adjustment

Disadvantages

  • Model may not fit
  • Inaccurate estimates of strength of effect (if model does not fit predictor-outcome relationship)
  • Results may be hard to understand
  • Relevant co variables must have been measured