Description Usage Arguments Details Value Author(s) See Also Examples
Estimation of propensity score based treatment effects
1 2 3 
object 
an object of class 'stratified.pscore', 'stratified.data.frame', 'matched.pscore', 'matched.data.frame', 'matched.data.frames' or a data frame. 
resp 
an integer or a string indicating the outcome variable in
the data and in the matched data if 
treat 
an integer or a string indicating the treatment variable
in the data and in the matched data if 
stratum.index 
an integer or a string indicating the vector
containing the stratum indices. No specification is needed if

match.index 
an integer or a string indicating the vector
containing the matching indices. No specification is needed if

adj 
a formula or a vector of integers or strings indicating
covariates. The default is NULL, i.e. no additional adjustment for
covariates is done in stratified or matched data. If 
weights 
a string indicating how to weight the stratumspecific
estimates to obtain an overall estimate in case of stratified data.
The default is 'rr' indicating stratumspecific weights proportional
to the stratumspecific number of observations. In case of linear
outcome, 
family 
a description of the error distribution and link
function to be used in the model (see 
regr 
a formula or a vector of integers or strings indicating
covariates in data. The default is NULL, i.e. no regression model is
fitted. If 
... 
further arguments passed to or from other methods. 
Propensity score methods are used to estimate treatment effects in
observational data. The treatment effects are estimated without
adjustment and can to be interpreted as marginal effects. However, it
is additionally possible to adjust for residual imbalances in strata
or in the matched data (using adj
) and also to apply
traditional regression (using regr
).
The usage of ps.estimate()
depends slightly on the class of the
input object. If either ps.makestrata()
or ps.match()
are previously used, treat
, match.index
and
stratum.index
are not needed, contrary to the case where the
input object is a data frame. If both match.index
and
stratum.index
are specified, stratum.index
will be
ignored. If one specifies adj
and regr
as formulas, they
must be identical in form of 'outcome~treatment+covariates' and
'outcome' and 'treatment' must agree with resp
and
treat
.
ps.estimate
returns an object of the same class as the input
object. The number and the manner of values depends on the scale of
resp
:
data 
a data frame containing the input data. 
data.matched 
a data frame limiting 'data' only to matched
observations. It is only available if 
name.resp 
a string indicating the name of the outcome variable. 
resp 
a numeric vector indicating the outcome variable labeled by 'name.resp'. 
name.stratum.index 
a string indicating the name of the selected
stratum indices. It is only available if 
stratum.index 
a numeric vector containing the selected stratum
indices labeled by 'name.stratum.index'. It is only available if

intervals 
a vector of characters indicating the interval used
for stratification. It is only available if 
stratified.by 
a string indicating the name of the selected
stratification variable. It is only available if

formula.pscore 
a formula describing formally the propensity score model fitted at last. 
model.pscore 
an object of class 
name.pscore 
a string indicating the name of propensity score estimated at last. 
pscore 
a numeric vector containing the estimated propensity score labeled by 'name.pscore. 
name.treat 
a string indicating the name of the treatment variable. 
treat 
a numeric vector containing the treatment index labeled by 'name.treat'. 
matched.by 
a string indicating the name of the selected
matching variable. It is only available if 
name.match.index 
a string indicating the name of the selected
matching indices. It is only available if 
match.index 
a numeric vector containing the selected matching
indices labeled by 'name.match.index' whereas '0' indicates 'no
matching partner found'. It is only available if 
match.parameters 
a list of matching parameters including

lr.estimation 
a list containing information about the
regression model based treatment effect estimates. It correspond to
the argument

ps.estimation 
a list containing information about the estimated propensity score based treatment effects. The list entries depend on the scale of outcome.

Susanne Stampf susanne.stampf@usb.ch
1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35  ## STU1
data(stu1)
stu1.ps < pscore(data = stu1,
formula = therapie~tgr+age)
stu1.match < ps.match(object = stu1.ps,
ratio = 2,
caliper = 0.5,
givenTmatchingC = FALSE,
matched.by = "pscore",
setseed = 38902)
stu1.est <
ps.estimate(object = stu1.match,
resp = "pst",
adj = "tmass",
regr = pst~therapie+tgr+age)
## PRIDE
data(pride)
pride.ps < pscore(data = pride,
formula = PCR_RSV~SEX+RSVINF+REGION+
AGE+ELTATOP+EINZ+EXT,
name.pscore = "ps")
pride.strata < ps.makestrata(object = pride.ps,
breaks = quantile(pride.ps$pscore,
seq(0,1,0.2)),
stratified.ps = "ps")
pride.est <
ps.estimate(object = pride.strata,
resp = "SEVERE",
family = "binomial",
adj = "AGE",
regr = SEVERE ~PCR_RSV+SEX+ETHNO+FRUEHG+HERZ+
ELTATOP+REGION+AGE+TOBACCO+VOLLSTIL+EXT+
EINZ+KRANKSUM,
weights = "rr")

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