Transcript Article Review Cara Carty 09-Mar-06 “Confounding by indication in non-experimental evaluation of vaccine effectiveness: the example of prevention of influenza complications” Hak E, Verheij TJM,

Article Review
Cara Carty
09-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
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low incidence of flu-related complications
virulence is variable and unpredictable
ethical concerns
Problems with observational studies
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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
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Confounding by indication
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‘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
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Design
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Natural experiments
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Ecological study
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communities need to be similar
Restriction and stratification
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difficult to find!
compare groups with similar prognosis
may limit generalizability, but enhance internal validity
Quasi-experiment
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individual matching within strata of important prognostic
variables
costly because it requires sufficient participants in each stratum
Strategy
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Design
Analyses
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Control of confounding variables in multivariable regression
model
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Use of an instrumental variable to enable statistical pseudo
randomization and to account for any residual confounding
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Subclassifying or matching on levels of ‘propensity scores’
Strategy
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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

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Design
Analyses
Control of confounding variables in multivariable regression
model
 Use of an instrumental variable to enable statistical pseudorandomization and to account for any residual confounding
?
Subclassifying or matching on levels of ‘propensity scores’
Propensity Scores: Definition
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Replace collection of confounding covariates in an observational
study with one function of these covariates—collapse
confounders into a single variable
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The score, e(X), is then used as only confounder
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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)
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Create strata of e(X)
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Compare cases and controls within a stratum to calculate
stratum-specific risk ratios
Propensity Scores: Basic Concept
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Purpose
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Problem
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association between vaccine and outcome
most vaccinees are different than unvaccinated
few outcomes relative to number of adjustment factors
Approach
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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
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Propensity scores balance observed covariates
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If it suffices to adjust for covariates X, then it
suffices to adjust for their propensity score e(X)
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Estimated propensity scores may remove both
systematic bias and chance imbalance in covariates
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Unlike random assignment, propensity score typically
doesn’t balance unobserved covariates
Propensity Scores: Comments
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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
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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
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Cons
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Design methods are standard practice
One ‘worked’ example is not entirely convincing
Pros
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Nice summary of non-randomization analytic issues
Gentle introduction to propensity scores and their utility
Bibliography
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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.