Lecture 11-12 Flashcards
Define Counterfactual Theory
Loosely: The opposite.
- The effect of the exposure difference in the counterfactual outcome
- Counterfactual outcome for smokers is estimated by non-smokers
- In other words, it has to be agreed that the group you’re comparing against is EXCHANGEABLE, or comparable.
- If there’s no exchangeability, CONFOUNDING occurs. And that’s bad.
What is internal validity?
Researchers are “required” to check 3 aspects of their study before declaring whether two events are truly associated/comparable.
- Check for Confounding, or Effect Modification
- Check for Biases
- Check for Statistical Significance
What is a confounding variable, or a confounder?
- A 3rd variable that distorts an association (RR/OR/HR) between the Exposure and the Outcome
- A 3rd variable that makes the groups not exchangeable in terms of their associations
- An alternative explanation of the association (findings)
- Normally it should be Exposure Leads to Outcome. But by that Direct Acyclic Graph (D.A.G.), It becomes that and also Confound Affects Exposure and/Or Outcome
What are some other Choicey Keywords for describing Confounding Factors?
- MIXING OF EFFECTS - an association (between Exposure and Outcome) is DISTORTED due to them being mixed with another (3rd) factor which is also associated with the Outcome…at the same time
- CONFUSION OF EFFECTS, where effect of Exposure is DISTORTED because the effect of an extraneous (3rd) factor is mistaken for the effect of the Exposure
What 3 Requirements must something have to be a Confounder
- Associated with the Exposure
- Associated with the Outcome
- Not directly in the causal-pathway linking Exposure to Outcome (Independent)
What’s an example of a confounder
If the Exposure was studying the prevalence of smoking among people with CHD, the confounder might be the AGE of the population if they weren’t even.
How does one test for confounding presences?
Process called STRATIFICATION, or Regression…I think.
- Step One: Calculate your Crude, or your unadjusted OR/RR
- Step Two: Calculate outcome measure of association (OR/RR) between Exposure and Outcome for ALL STRATA (layers) of the 3rd variable (potential confounder)
- Create a weighted-average of all strata (if near-equal)
- Commonly called ADJUSTED association - Step Three: Compare the Crude vs. Adjusted measures of association between Exposure and Outcome
- The Crude & Adjusted estimates (RR/OR) of measure of association will be different by ~10%-20% (15%) if confounding IS present
What are two main impacts of confounders
- Intensity/Magnitude/Strength of Association
- An association more/less extreme than true association - Direction of Association
- Produces association in an opposite direction o
- Towards or away from a null association (RR/OR/HR=1.0)
Name 2 Ways to Control Confounding
- STUDY DESIGN STAGE
- Randomization (Simple or Stratified versions)
- Restriction
- Matching - ANALYSIS OF DATA STAGE
- Stratification (w/ Weighting)
- Multivariate statistical analysis (Regression analyses)
What is randomization?
Describe it’s strengths and weaknesses?
Randomization technique hopefully allocates an equal number of subjects with the known (and unknown) confounders into each intervention group
Strengths: With sufficient sample size (N), randomization will likely be successful in serving its purpose (making groups “equal”)
- Stratified version more precisely assures equal-ness
Weaknesses: Sample size (N) may not be large enough to control for all known and unknown confounders
- Process doesn’t guarantee successful, equal allocation between all intervention groups for all known and unknown confounders
- Practical only for Interventional studies
Describe Restriction.
What are some of Restriction’s strengths and weaknesses.
Study participation is restricted to only subjects who do not fall within pre-specified category(-ies) of confounder
- Strength: Straight forward, convenient and inexpensive
- Does not negatively impact Internal Validity
- Weakness: Sufficiently narrow restriction criteria may negatively impact ability to enroll subjects (reduced sample size (N)).
- If restriction criteria is not sufficiently narrow it will allow the introduction of residual confounding effects
- Eliminates researchers ability to evaluate varying levels of the factor being excluded
- Can negatively impact External Validity (Generalizability)
Describe Matching
What are the strengths and weaknesses of matching?
Study subjects selected in matched-pairs related to the confounding variable to equally distribute confounder among each study group
- Strength: Intuitive, some feel it gives greater analytic efficiency
- Weakness: Difficult to accomplish, very time consuming, and potentially expensive
- Doesn’t control for any confounders other than those matched on
- Over-matching possible; this will mask findings
Describe Stratification
What are the strengths and weaknesses of stratification?
Descriptive & Statistical analysis of data evaluating association between Exposure and Outcome within the various strata (layers) within the confounding variable(s), such as Young vs. Old; in the Smoking & CHD example)
- Strength: Intuitive (to some), straight-forward and enhances understanding of data
- Weakness: Impractical for simultaneous control of multiple confounders, especially those with multiple strata within each variable being controlled
Describe Multivariate Analysis.
What are multivariate analysis’s strengths and weaknesses?
Statistical analysis of data by mathematically factoring out the effects of the confounding variable(s)
- Strength: Can simultaneously control for multiple confounding variables
- In Regressions, beta coefficients can be converted to OR’s
- Weakness: Process requires all individuals to clearly understand (interpret) the data (results)
- Can be very time consuming for researcher/biostatistician
- Examples: (FYI for now; not on exam #1)
- Regressions (Linear & Logistic versions)
- Cox Proportional Hazards
What is effect modification?
How is it different than confounding?
- A 3rd variable, that when present, modifies the magnitude of effect of a TRUE association by varying it within different levels of a 3rd variable (modifies the effect across the strata)
- If an interaction is present, the researcher MUST report the measures of association for each strata individually
- So, unlike confounding, an effect modifying variable should be described and reported at each level of the variable, rather than controlled-for.
