1. Enhancing Causal Inference in Observational Studies

2. The five explanations when an association between coffee drinking and myocardial infarction (MI) is observed in a sample

4. Type of spurious association
Strengthening the inference that an association has a cause-effect basis: ruling out spurious associations
Type of spurious association
Chance (due to random error)
Design phase (How to prevent the rival explanation)
Increase sample size and other strategies (measurement precision and accuracy, and sample size and power)
Analysis phase (How to evaluate the rival explanation)
Interpret p value in context of prior evidence

5. Strengthening the inference that an association has a cause-effect basis: ruling out spurious associations
Type of spurious association
Bias (due to systematic error)
Design phase (How to prevent the rival explanation)
Carefully consider the potential consequence between the research question and the study plan:
Subject,
Predictor,
Outcome
Analysis phase (How to evaluate the rival explanation)
Obtain additional data to see if potential biases have actually occurred.
Check consistency with other studies (especially those using different methods).

6. Strengthening the inference that an association has a cause-effect basis: ruling out other real associations
Type of spurious association
Effect-Cause (the outcome is actually the cause of the predictor)
Design phase (How to prevent the rival explanation)
Do a longitudinal study
Obtain data on the historic sequence of variables
Analysis phase (How to evaluate the rival explanation)
Consider biologic plausibility

7. Strengthening the inference that an association has a cause-effect basis: ruling out other real associations
Type of spurious association
Effect-Effect (confounding variable is a cause of both the predictor and the outcome)

8. Design phase strategies for coping with confounders: Specification
Advantages
Easily understood
Focuses the sample of subjects for the research question at hand
Disadvantages
Limits generalizability
May make it difficult to acquire an adequate sample size

9. Design phase strategies for coping with confounders: Matching
Disadvantages
May be time consuming and expensive, less efficient than increasing the number of subjects (e.g., the number of controls per case)
Decision to match must be made at outset of study and can have irreversible adverse effect on analysis and conclusions
Requires early decision about which variables are predictors and which confounders
Advantages
Can eliminate influence of strong constitutional confounders like age and sex
Can eliminate influence of confounders that are difficult to measure
Can increase precision (power) by balancing the number of cases and controls in each stratum
May be a sampling convenience, making it easier to select the controls in case-control study

10. Design phase strategies for coping with confounders: Matching
Disadvantages
Removes option of studying matched variables as predictors or as intervening variables
Requires matched analysis
Creates the danger of overmatching (i.e., matching on a factor that is not a confounder, thereby reducing power)

11. Analysis phase strategies for coping with confounders: Stratification
Advantages
Easily understood
Flexible and reversible; can choose which variables to stratify upon after data collection
Disadvantages
Number of strata limited by sample size needed for each stratum:
Few covariables can be considered
Few strata per covariable leads to less complete control of confounding
Relevant covariables must have been measured

12. Analysis phase strategies for coping with confounders: Statistical adjustment
Advantages
Multiple confounders can be controlled simultaneously
Information in continuous variables can be fully used
As flexible and reversible as stratification
Disadvantages
Model may not fit
Incomplete control of confounding (if model does not fit confounder-outcome relationship)
Inaccurate estimates of strength of effect (if model does not fit predictor-outcome relationship)
Results are hard to understand
Relevant covariables must have been measured