In observational studies, participants are not
randomly assigned to intervention groups. In fact, individuals receiving
a given treatment may be markedly different than those not receiving
treatment. Covariates that are independently associated with both
treatment and outcome variables are called confounders. Illness
severity, for example, would be considered a confounding variable if it
influences whether or not a patient receives a given treatment and is
also associated with the outcome of interest. Important covariates
may not be available in existing datasets. Ignoring group differences
in important covariates, whether available or not, can lead to
biased estimates of treatment effects. It is important to remember
that random error (chance) leads to imprecise results, whereas
systematic error (bias) leads to inaccurate results.
Common approaches to control for group
differences include stratified analyses, matching, or multivariable
modeling using observed covariates, but these strategies are limited in
the number of covariates that can be included, and none address
unobserved covariates. Alternative techniques to deal with confounding
include sensitivity, propensity score, or instrumental variable
Sensitivity analysis identifies what
the strength and prevalence of an unmeasured confounder would have to
be to alter the conclusion of the study. In other words, sensitivity
analysis does not rule out the possibility that confounding exists;
it describes the circumstances necessary for an unmeasured confounder
to negate the observed effect of the treatment (or exposure) on the
Propensity score analysis uses any and
all observed covariates to determine the likelihood (conditional
probability) that a person belongs to the treatment group. The
propensity scores can then be used, through a variety of options, to
balance observed covariates and thus, reduce observed confounding.
Instrumental variable (IV) analysis involves
identifying a variable (instrument) that is associated with
treatment, but not directly associated with the outcome. Since all
unmeasured factors are part of error term, selection bias is (likely)
present when error term is correlated with both the outcome and the
treatment variable. IV analysis involves 1) modeling treatment as a
function of covariates and instrument, and 2) use this information to
'break link' with unobserved confounder(s). The unique feature of IV
analysis is that it reduces confounding from both observed and
MA Brookhart et al. (2010) Instrumental variable methods in
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RB D'Agostino (1998) Tutorial in biostatistics propensity
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JP Leigh & M Schembri (2004) Instrumental variables
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(2006) Instrumental variables application and limitations. Epidemiol.
PR Rosenbaum (2005) Sensitivity analysis in observational
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MG Stineman et al. (2008) The effectiveness of inpatient
rehabilitation in the acute postoperative phase of care after
transtibial or transfemoral amputation: study of an integrated health
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JA Stukel et al. (2007) Analysis of observational studies in
the presence of treatment selection bias: Effects of invasive cardiac
management on AMI survival using propensity score and instrumental
variable methods. JAMA. 297(3): 278-285.
The videos below cover analytic procedures for
dealing with confounding and recorded during the Comparative
Effectiveness Research with Population-Based Data conference in the
Baker Institute at Rice University on July 13, 2012.