Warning: imagejpeg(C:\Inetpub\vhosts\kidney.de\httpdocs\phplern\24599889
.jpg): Failed to open stream: No such file or directory in C:\Inetpub\vhosts\kidney.de\httpdocs\pget.php on line 117 Stat+Med
2014 ; 33
(13
): 2297-340
Nephropedia Template TP
gab.com Text
Twit Text FOAVip
Twit Text #
English Wikipedia
Instrumental variable methods for causal inference
#MMPMID24599889
Baiocchi M
; Cheng J
; Small DS
Stat Med
2014[Jun]; 33
(13
): 2297-340
PMID24599889
show ga
A goal of many health studies is to determine the causal effect of a treatment or
intervention on health outcomes. Often, it is not ethically or practically
possible to conduct a perfectly randomized experiment, and instead, an
observational study must be used. A major challenge to the validity of
observational studies is the possibility of unmeasured confounding (i.e.,
unmeasured ways in which the treatment and control groups differ before treatment
administration, which also affect the outcome). Instrumental variables analysis
is a method for controlling for unmeasured confounding. This type of analysis
requires the measurement of a valid instrumental variable, which is a variable
that (i) is independent of the unmeasured confounding; (ii) affects the
treatment; and (iii) affects the outcome only indirectly through its effect on
the treatment. This tutorial discusses the types of causal effects that can be
estimated by instrumental variables analysis; the assumptions needed for
instrumental variables analysis to provide valid estimates of causal effects and
sensitivity analysis for those assumptions; methods of estimation of causal
effects using instrumental variables; and sources of instrumental variables in
health studies.
|*Causality
[MESH]
|*Confounding Factors, Epidemiologic
[MESH]
|*Data Interpretation, Statistical
[MESH]
|Humans
[MESH]
|Infant, Newborn
[MESH]
|Observational Studies as Topic/statistics & numerical data
[MESH]
free
free
free
