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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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Dr. Sunita Dodani

Assistant Professor

Family Medicine, CHS

The Aga Khan University


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
  • 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


  • 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

  • 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

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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

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


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



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


  • 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



  • 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



  • 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


  • 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



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



  • 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


  • 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


  • 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