Enhancing Causal Inference in Observational Studies

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

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

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).

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

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)

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

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

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)

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

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