1. Article Review Cara Carty 09-Mar-06

2. “Confounding by indication in non-experimental evaluation of vaccine effectiveness: the example of prevention of influenza complications” Hak E, Verheij TJM, Grobbee DE, Nichol KL, Hoes AW. J Epidemiol Comm Health 2002; 56:951-955.

3. Background  Health impact of flu  Outcome of interest: post-flu complications  Few randomized trials low incidence of flu-related complications virulence is variable and unpredictable ethical concerns  Problems with observational studies conflicting results confounding by indication other confounding

4. Background  Confounding by indication ‘a variable that is a risk factor for disease among non-exposed persons and is associated with exposure of interest in the population from which cases derive, without being an intermediate step in the causal pathway between exposure and disease’

5. Background  Confounding by indication ‘a variable that is a risk factor for disease among non-exposed persons and is associated with exposure of interest in the population from which cases derive, without being an intermediate step in the causal pathway between exposure and disease’ ‘measured differences in patient groups receiving alternative therapies are more attributable to differences in patient characteristics than they are to differences in effectiveness of therapies’

6. Causal diagram Old age, cardiovascular disease, asthma Exposure: Flu vaccine Pneumonia, Death

7. Strategy  Design Natural experiments  difficult to find! Ecological study  communities need to be similar Restriction and stratification  compare groups with similar prognosis  may limit generalizability, but enhance internal validity Quasi-experiment  individual matching within strata of important prognostic variables  costly because it requires sufficient participants in each stratum

8. Strategy  Design  Analyses Control of confounding variables in multivariable regression model Use of an instrumental variable to enable statistical pseudo randomization and to account for any residual confounding Subclassifying or matching on levels of ‘propensity scores’

9. Strategy  Design  Analyses Control of confounding variables in multivariable regression model ? Use of an instrumental variable to enable statistical pseudo randomization and to account for any residual confounding ? Subclassifying or matching on levels of ‘propensity scores’

10. Strategy  Design  Analyses Control of confounding variables in multivariable regression model  Use of an instrumental variable to enable statistical pseudo- randomization and to account for any residual confounding ? Subclassifying or matching on levels of ‘propensity scores’

11. Propensity Scores: Definition  Replace collection of confounding covariates in an observational study with one function of these covariates—collapse confounders into a single variable  The score, e(X), is then used as only confounder  e(X) is estimated using logistic regression or discriminant model with binary exposure (Z=0 or Z=1) and observed covariates X so that e(X)=prob(Z=1|X)  Create strata of e(X)  Compare cases and controls within a stratum to calculate stratum-specific risk ratios

12. Propensity Scores: Basic Concept  Purpose association between vaccine and outcome  Problem most vaccinees are different than unvaccinated few outcomes relative to number of adjustment factors  Approach find out what factors “predict vaccination” by calculating propensity scores for every participant classify participants by quintiles of increasing probability of vaccination (propensity score) compare outcome in vaccinated and unvaccinated with equivalent propensity scores

13. Propensity Scores: Properties  Propensity scores balance observed covariates  If it suffices to adjust for covariates X, then it suffices to adjust for their propensity score e(X)  Estimated propensity scores may remove both systematic bias and chance imbalance in covariates  Unlike random assignment, propensity score typically doesn’t balance unobserved covariates

14. Propensity Scores: Comments  If scores are relatively constant within each stratum, then within each stratum, the distribution of all covariates should be approximately the same in both treatment groups  Balance can be checked and the score reformulated until better balance is achieved

15. Example Hak et al., 2002

16. Example Hak et al., 2002

17. Example Hak et al., 2002

18. Example Hak et al., 2002

19. Discussion  Cons Design methods are standard practice One ‘worked’ example is not entirely convincing  Pros Nice summary of non-randomization analytic issues Gentle introduction to propensity scores and their utility

20. Bibliography  Joffe MM, Rosenbaum PR. Invited commentary: propensity scores. Am J Epidemiol. 1999 Aug 15; 150(4):327-333.  Rubin DB. Estimating causal effects from large data sets using propensity scores. Ann Int Med. 1997 Oct 15;127(8):757-763.  Salas M, Hofman A, Stricker BH. Confounding by indication: an example of variation in the use of epidemiologic terminology. Am J Epidemiol. 1999 Jun 1;149(11):981-3.