Description Usage Arguments Details Value Examples
View source: R/IPW.meandifference.R
IPW.meandifference is used to estimate the treatment effect from a partially nested design (where the treatment arm has multiple treatment clusters, and the control arm has no clusters) with the inverse-propensity-weighted mean difference estimation approach adapted to the partially nested design feature.
1 2 3 4 5 6 7 | IPW.meandifference(
Y,
Trt,
clus,
Lyz,
Ly
)
|
Y |
An outcome variable |
Trt |
Treatment assignment indicator (1 for treatment and 0 for control). The treatment arm has multiple treatment clusters, and the control arm has no clusters. |
clus |
Observed treatment cluster assignment. clus = 0 for the control arm |
Lyz |
A matrix containing pre-treatment covariates. Lyz can contain two types of covariates. The first type of covariates affect both the treatment assignment and outcome directly. The second type of covariates do not affect the outcome in a given treatment cluster directly, but affect both the treatment assignment and treatment cluster assignment. |
Ly |
A matrix containing the pre-treatment covariates that affect both the treatment assignment and outcome directly. Ly is the first type of covariates contained in Lyz. |
The sandwisch type standard error estimation does not account for the clustering in the treatment arm.
IPW.meandifference returns a list "ipw10.md" containing the following components:
meanDiff |
the treatment effect estimate, i.e., the mean difference between the treatment and control arm. |
se_sw |
the sandwich-type standard error estimate of the treatment effect estimate. |
z.wald |
the Wald statistic (i.e., meanDiff/se_sw). |
1 2 3 4 5 6 7 |
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