Enhancing causal influence (in observational studies) Spurious and real association: Suppose a study reveals an ass. Between coffee drinking and M.I., the association could be: Explanation Type of association Basis for association Causal Model Coping 1. Chance Spurious Random error Coffee not related to M.I. Sample size, ex.,every 100% M.I. we have 60 with Hx of coffee drinking,if we took 20 then should get 12 with Hx of coffee but we may get 19 by chance this will lead to spurious ass. So increase sample size to avoid this error.

Coffee drinking and M.I. are not related. Explanation Type of association Basis for association Causal Model Coping 2- Bias Spurious Systematic error Coffee drinking and M.I. are not related. Carefully consider the potential consequences of each difference between the research question and the study plan: subject, predictable outcome 3- Effect-cause   Real Caret before the horse M.I. is a cause of coffee drinking Usually not in cohort studies, assess the predictor variable at different points in time, usually with weak biological plausibility

Coping 4- Eeffect-Effect Real Confound Explanation Type of asso. Basis for asso. Causal Model Coping 4- Eeffect-Effect Real Confound Coffee drinking and M.I. are both caused by a third extrinsic factor ex. smoking 1-Specification (inclusion/excl.) Disadvantage:we may need the interaction between smoking and coffee to cause M.I.,if smoking is highly prevalent it will be difficult to get a sample of coffee with smoking 2- Matching: especially in case –control study ex. coffee+1 packet smoking/day should be compared with a control of coffee+1 packet smoking a day and so on. 3- Stratification: disadvantage: it will be very difficult to analyze many strata with many variables ex., smoking, coffee, B.Pr., serum cholesterol and alcohol(243 strata)

But must fulfill the concept of causation Explanation Type of asso. Basis for asso. Causal Model Coping 5- Cause-Effect Real Cause and effect Coffee M.I. But must fulfill the concept of causation adjustment: ex. lead ingestion and child I.Q. we straight the confided which is the parental education and it’s relation to the I.Q. in a straight line then measure the relation of I.Q. with lead ingestion

Strategy Advantage Disadvantage Stratification - Easily understood - Flexible and reversible - Can choose which variables to stratify upon after data collection Number of strata limited by sample size needed for each stratum Few co variables can be considered Few strata/co variables lead to less complete control of confounding Statistical adjustment - Multiple confounders can be controlled simultaneously - Information in continuous variables can be fully used - As flexible and reversible as stratification Incomplete control of confounding (if model does not fit confounder-outcome relationship Inaccurate estimate of strength of effect (if model does not fit predictor-outcome relationship. Results are hard to understand Relevant co-variables must have been measured

Causal models to consider if smoking association with coffee drinking and is a cause of M.I. Coffee drink smoking coffee drink factor x smoking coffee drink smoking (1)M.I. (2)M.I. (3)M.I Matching is harmful Matching may be helpful