Description Usage Format Estimated subgroup assignment References See Also
A simulated dataset of 1500 observations, with 'observed' binary treatment, Normal outcome, covariates, and a few other variables that were used in the generation of the 'observed' treatment and outcome.
1 |
A data frame with 1500 rows and 16 variables:
treatment, generated from Bern(expit(0.1 X1
- 0.1
X2
+ 1.1 X3
- 1.1 X4
+ 0.4 X5
))
outcome, corresponding to the potential outcome
(Y0
or Y1
) associated with the observed treatment
confounder, generated from N(0,1)
confounder, generated from N(0,1)
confounder, generated from N(0,1)
confounder, generated from N(0,1)
instrument, generated from N(0,1)
prognostic variable, generated from N(0,1)
effect modifier, generated from Bern(0.5)
effect modifier, generated from Bern(0.5)
effect modifier, generated from Bern(0.5)
potential outcome,
generated from N(-3.85 + 5 trt
+ 0.5 X1
- 2 X2
- 0.5 X3
+ 2 X4
+ X6
- E1
- 2E3
+ trt
*E1
+ 4 trt
* E2
- 4 trt
*E3
, 1 )
where trt
is set to 0
potential outcome,
generated from N(-3.85 + 5 trt
+ 0.5 X1
- 2 X2
- 0.5 X3
+ 2 X4
+ X6
- E1
- 2E3
+ trt
*E1
+ 4 trt
* E2
- 4 trt
*E3
, 1 )
where trt
is set to 1
the true average treatment effect in the subgroup that the observation belongs to
an integer value in {1, 2, ..., 10} that represents the estimated subgroup that the observation belongs to. See below for more information on how this value is assigned.
the estimated ITE, as determined via
regression using Bayesian Additive Regression Trees (as estimated by
bart()
of the bayesTree package).
To estimate what subgroup each
observation belongs to, an individual treatment effect (ITE) was estimated
(stored in mmt
). This empirical distribution of ITEs is then
partitioned into deciles, and an average treatment effect (ATE) estimated in
each. Each of the ten subgroups are arranged in ascending order by the
subgroup-specific ATE then assigned an integer value in {1, 2, ..., 10}
(stored in estGrp
).
Anoke SC, Normand S-L, Zigler CM (2017). Approaches to treatment effect heterogeneity in the presence of confounding (in revision).
simpleDataA
for data simulated with confounding
and no effect modification, simpleDataB
for effect modification
and no confounding, and simpleDataD
for effect modification and
confounding by effect modifiers.
Other simple simulated datasets: simpleDataA
,
simpleDataB
, simpleDataD
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