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Simple Causal Graphs

Simple Causal Graphs. Simple Casual Graphs. Hein Stigum Presentation, data and programs at: http://folk.uio.no/heins/. Causal graphs. Simple causal graphs Proper analysis (adjust or not) Direction of bias Directed Acyclic Graphs (DAGs) Formal tool Inventory of variables

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Simple Causal Graphs

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  1. Simple Causal Graphs Simple Casual Graphs Hein Stigum Presentation, data and programs at: http://folk.uio.no/heins/ H.S.

  2. Causal graphs • Simple causal graphs • Proper analysis (adjust or not) • Direction of bias • Directed Acyclic Graphs (DAGs) • Formal tool • Inventory of variables • Proper analysis (adjust or not) • Causal inference H.S.

  3. E D Exposure-Disease influenced by C • C can be: • Confounder • Intermediate (in 2. Path) • Collider • Effect modifier • Use graphs • Determine C-type • Choose analysis C H.S.

  4. Example • Exposure • Pysical Activity: PA • Disease • Diabetes type 2: D2 • Covariates • Smoking: S • Health Conscious: HC • Overweight: Ov • Blood Pressure: BP H.S.

  5. Linear models 0 • Best model? • Likelihood ratio tests or Akaike criteria  mod 4 • All changes in PA effect considered important  mod 4 • Claim mod 2. • Model choice can not be based on data only. • Need extra info or assumptions. H.S.

  6. C E D C E D C E D No influence of C H.S.

  7. - + biased true Confounder: Smoking • Should adjust for Smoking • Stratify • Regression S Negative bias PA D2 -3 -2 0 H.S.

  8. - + + biased true Confounder 2 Adjust for Smoking or for Health Consciousness Assume all following models are adjusted for smoking HC S Negative bias PA D2 -3 -2 0 H.S.

  9. - + Intermediate (in 2. path): Overweight Ov Alt 1: Ignore Overweight Total -2.0 PA D2 • Alt 2: Two models: • Direct c2 -1.5 • Indirect b1*c1 -0.5 • Total c2+ b1*c1 -2.0 Ov Ov c1 b1 c2 PA PA D2 Simply adjusting for Overweight is not OK! H.S.

  10. Select limping subjects Limp + + Hip arthritis Knee injury - Collider idea Two causes for limping • Conditioning on a collider induces an association between the causes • Condition = (restrict, stratify, adjust) • Bias direction? Limp + + Hip arthritis Knee injury H.S.

  11. - + biased true Collider: Blood Pressure • Should not adjust for Blood Pressure • Problem if selection is connected to BP BP Positive bias if we adjust PA D2 0 H.S.

  12. Best model (so far) • Model 2 is best • Used extra info in graphs to decide H.S.

  13. Alt 2: Model with interaction Technical Test for interaction Efficient (7 estimates) Effect modifier: Sex Sex • Alt 3 : Ignore Sex PA D2 • Alt 1 : Two models • Easy • No test for interaction • Inefficient (12 estimates) H.S.

  14. Effect modifier: SexModel with interaction term • Test for interaction • Wald test on b3=0 • If significant interaction • Sex is coded 0 for Males and 1 for Females • The effect of PA (1 unit increase) • Linear model -2.5 -1.5 H.S.

  15. Examples H.S.

  16. S Educ - - Smoke LRTI Smoking and LRTIThe truth is out there? LRTI=Lower Resperatory Tract Infections Want: effect of smoking in pregnancyon LRTIin children Have: 40% response, high education is overrepresented Best causal estimate: Crude smoke-LRTI under 100% response? Crude smoke-LRTI under 40% response? Education is a confounder Selection represents partial adjustment H.S.

  17. S Educ Smoke LRTI Smoking and LRTI, ex 2 • Education is a not a confounder • Crude smoke-LRTI in population is unbiased • Crude smoke-LRTI in sample is biased, S is a collider • Adjusted smoke-LRTI in sample is unbiased H.S.

  18. Ethnic Height Hemo Lung func Ethnicity and lung function • Exposure Ethnic group • Outcome Lung function • Covariates Hemoglobin, height • Draw DAG • Suggest analyzes/models • Model with all covariates meaningful? H.S.

  19. Model 1 Model 2 Height Ethnic Lung func Hemo Lung func Model 4 Model 3 Height Ethnic Height Hemo Lung func Hemo Lung func Hart rate Ethnic Models H.S.

  20. Summing up • In a study of 2 variables, a 3. variable may have 4 effects: Confounder, Intermediate, Collider, Effect modifier • Not distinguished from data, need extra info • Casual graphs help use the extra info H.S.

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