Sensitivity analysis: distributional assumptions and confounding assumptions.

Abstract:

:In a presentation of various methods for assessing the sensitivity of regression results to unmeasured confounding, Lin, Psaty, and Kronmal (1998, Biometrics54, 948-963) use a conditional independence assumption to derive algebraic relationships between the true exposure effect and the apparent exposure effect in a reduced model that does not control for the unmeasured confounding variable. However, Hernán and Robins (1999, Biometrics55, 1316-1317) have noted that if the measured covariates and the unmeasured confounder both affect the exposure of interest then the principal conditional independence assumption that is used to derive these algebraic relationships cannot hold. One particular result of Lin et al. does not rely on the conditional independence assumption but only on assumptions concerning additivity. It can be shown that this assumption is satisfied for an entire family of distributions even if both the measured covariates and the unmeasured confounder affect the exposure of interest. These considerations clarify the appropriate contexts in which relevant sensitivity analysis techniques can be applied.

journal_name

Biometrics

journal_title

Biometrics

authors

Vanderweele TJ

doi

10.1111/j.1541-0420.2008.01024.x

subject

Has Abstract

pub_date

2008-06-01 00:00:00

pages

645-9

issue

2

eissn

0006-341X

issn

1541-0420

pii

BIOM1024

journal_volume

64

pub_type

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