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Confounding, Effect Modification, and Stratification

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Title: Confounding, Effect Modification, and Stratification


1
Confounding, Effect Modification, and
Stratification
  • Yu-Fen Li

2
Adding a Third Dimension to the RxC contingency
tables
3
Confounding
  • A confounding variable is associated with the
    exposure and it affects the outcome, but it is
    not an intermediate link in the chain of
    causation between exposure and outcome.

4
Confounding example
5
Confounding example
Smokers
Non-smokers
6
Confounding example
Smoking
X
Drinking
Lung cancer
  • Drinking is not associated with lung caner
  • Smoking is a confounder

7
Controlling for confounders
  • Confounders can be controlled for in the design
    phase of a study (randomization/
    restriction/matching).
  • Confounders can be controlled for in the analysis
    phase of a study (stratification/ multivariate
    model).

8
Analytical identification of confounders
  • Stratification
  • Mantel-Haenszel Procedure
  • Take a 2x2 table and break it into a series of
    smaller 2x2 tables (one table at each of G levels
    of the confounder yields G tables)
  • Homogeneity test/ Mantel-Haenszel Summary Odds
    Ratio
  • Example in testing for an association between
    lung cancer and alcohol drinking (yes/no),
    separate smokers and non-smokers.

9
Mantel-Haenszel Procedure Pros and Cons
  • Advantages
  • Mantel-Haenszel summary statistic is easy to
    interpret and calculate
  • Gives you a hands-on feel for the data
  • Disadvantages
  • Requires categorical confounders or continuous
    confounders that have been divided into intervals
  • Cumbersome if more than a single confounder

10
Effect modification
  • Effect modification occurs when the effect of an
    exposure is different among different subgroups.
  • aka interaction

11
Interaction example
12
Interaction example
Smokers
Non-smokers
13
Interaction example
Smoking
Drinking
Lung cancer
  • Drinking is associated with lung caner
  • Smoking is an effect modifier

14
Investigating interaction
  • Effect modifiers can NOT be controlled
  • Effect modification is not a bias, but a
    phenomena
  • Effect modifiers can be evaluated in the analysis
    phase of a study (stratification/ multivariate
    model with an interaction term).

15
HOMEWORK Q1
16
HOMEWORK Q2
17
HOMEWORK Q2
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