Confounding for viderekommende. Laust H Mortensen, Department of Social Medicine University of Copenhagen

Transkript

1 Confounding for viderekommende Laust H Mortensen, LAMO@SUND.KU.DK Department of Social Medicine University of Copenhagen

2 Bias i empiriske studier Er det målte interesseparameter (fx OR, HR, RR, RD, mv) er udtryk for en kausal sammenhæng? Systematisk Selektionsbias Informationsbias Confounding Ikke-systematisk Random error

3 Confounding a distortion in the estimated exposure effect that results from differences in risk between the exposed and unexposed that are not due to exposure Rothman & Greenland

4 Two groups of patients 5 exposed to 5 exposed to Epi Dias 4

5 after six months 1/5 died 3/5 died RR = (3/5)/(1/5) = 3 RD = (3/5)-(1/5) = 0.4 OR = (3/2)/(1/4) = 6 Epi Dias 5

6 Types of interpretation RR = (3/5)/(1/5) = 3 RD = (3/5)-(1/5) = 0.4 OR = (3/2)/(1/4) = 6 Association: Patients receiving rather than have a 3-fold greater risk of dying within 6 months Epi Dias 6

7 Types of interpretation RR = (3/5)/(1/5) = 3 RD = (3/5)-(1/5) = 0.4 OR = (3/2)/(1/4) = 6 Association: Patients receiving rather than have a 3-fold greater risk of dying within 6 months Causal effect (at group level): If patients are given rather than this causes a 3-fold increase in the risk of dying within 6 months Epi Dias 7

8 But why and why? Indication ( Confounding by indication ) Epi Dias 8

9 Den almindelige definition Om confounderen C gælder det at: 1. C er en selvstændig årsag til udfaldet 2. C er associeret med interesseeksponeringen 3. C er ikke et led i årsagskæden fra eksponering til udfald C E O

10 Håndtering af confoundere Designfase: Randomisering (mere herom senere i kurset) Matching betingning Restriktion Analysefase: Matching Restriktion Stratifikation Regressionsanalyse adjust for control for standardized betingning

11 Den almindelige definition Om confounderen C gælder det at: 1. C er en selvstændig årsag til udfaldet 2. C er associeret med interesseeksponeringen 3. C er ikke et led i årsagskæden fra eksponering til udfald C E O MEN denne definition kan lede til -- justering for ikke-confoundere -- udeladelse af confoundere

12 Causal diagrams (DAGs) Definition Graphical models for causal relations Application An alternative to conventional approaches to identify confounding and other types of bias Visualize and explicitly elaborate the assumptions about the web of causation Identify variables that must be measured and controlled for to obtain unconfounded estimates

13 DAGs DIRECTED edges (arrows) linking nodes (variables) ACYCLIC links because no arrows from descendants (effects) to ancestors (causes) GRAPHS C E D

14 Afdeling for Socialmedicin What to include in causal diagrams Variables to include: All measured and unmeasured common causes of at least two variables Therefore: All arrows can be interpreted as direct causal influences, i.e. manipulating X will change Y No arrows from X directly into Y, means that manipulating X will not change Y, given all other variables are not manipulated Need not include variables that lie on the causal path between X and Y when there is no specific interest in them

15 A hypothetical example Personality Sex SES Work-related stress IHD Blood pressure

16 Example: Causal path Personality Sex SES Work-related stress IHD Blood pressure

17 Example: Open backdoor path Personality Sex SES Work-related stress IHD Blood pressure

18 Example: Closed backdoor path SES is a collider Personality Sex SES Work-related stress IHD Blood pressure

19 An association between E and D can be produced by one of the following causal structures: 1. Cause and effect E D 2. Common causes C E D 3. Conditioning on common effects E D C

20 Types of pathways 1 A and B are associated a c b 1 A and B are not associated a c b Box indicates conditioning (stratification, restriction, adjustment etc) 2 a c b 2 a c b 3 a c b Here, C is a collider 3 a c b

21 Example: conditioning on a common effect Sprinkler Rain The lawn is wet

22 Example: conditioning on a common effect Sprinkler Rain The lawn is wet Boksen indikere at vi har betinget på variablen Den stiplede linje ( ) indikerer at vi pga. betinging på et barn har giftet forældrene. Hvis plænen er våd er det enten fordi det har regnet eller fordi havevanderen har været tændt.

23 Example: conditioning on a common effect Diet Cancer Weight loss > 5 kg

24 Example: Height & speed Sample: NBA basketball players Speed Height NBA

25 Example: Height & speed Sample: NBA basketball players Speed Height NBA

26 Example: School grades & sports Sample: Harvard students Good grades Good at sports Acceptance to Harvard

27 Example: School grades & sports Sample: Harvard students Good grades Good at sports Acceptance to Harvard

28 Marginal association in a DAG 1. Cause and effect Type of bias E D Reversed causality 2. Common causes L 3. Conditioning on common effects E D Confounding E D Selection bias / collider stratification bias L

29 Graphical criteria for confounder control Given a subset S={S 1,.., S n } of variables that contain no descendant of exposure or disease Every unblocked backdoor path from E to D is intercepted by a variable in S Every unblocked path from E to D induced by adjustment for the variables in S is intercepted by a variable in S

30 1. Delete all arrows emanating from exposure Personality Sex SES Work-related stress IHD Blood pressure

31 2. Draw undirected arcs to connect every pair of variables that share a child Personality Sex SES Work-related stress IHD Blood pressure

32 3. See whether there is any unblocked backdoor path from exposure to disease Personality Sex Personality SES Sex Work-related stress SES IHD Work-related stress Blood pressure IHD

33 3. See whether there is any unblocked backdoor path from exposure to disease Personality Sex Personality SES Sex Work-related stress SES IHD Blood pressure Work-related stress IHD

34 EXERCISE!

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37 Why can DAGs be useful? Visualize, clarify and communicate prior beliefs about the causal structure Based on subject-matter knowledge instead of statistical associations detected in data Identification of a minimal sufficient set of confounders Includes both measured and unmeasured covariates