Article Review

Cara Carty09-Mar-06

“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.

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

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’

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’

Causal diagram

Old age, cardiovascular disease, asthma

Exposure: Flu vaccine

Pneumonia, Death

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

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’

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’

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’

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

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

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

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

Example

Hak et al., 2002

Example

Hak et al., 2002

Example

Hak et al., 2002

Example

Hak et al., 2002

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

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.