help doseresponse, help doseresponse_model (SJ8-3: st0150)

Title

doseresponse -- Estimation of the dose-response function through adjustment for the generalized propensity score

Syntax

doseresponse
varlist
[if] [in]
[weight],
outcome(varname)
t(varname)
gpscore(newvar)
predict(newvar)
sigma(newvar)
cutpoints(varname)
index(string)
nq_gps(#)
dose_response(newvarlist)
[t_transf(transformation)
normal_test(test)
norm_level(#)
test_varlist(varlist)
test(type)
flag(#)
cmd(regression_cmd)
reg_type_t(type)
reg_type_gps(type)
interaction(#)
tpoints(vector)
npoints(#)
delta(#)
filename(filename)
bootstrap(string)
boot_reps(#)
analysis(string)
analysis_level(#)
graph(filename)
detail]

doseresponse_model
treat_var GPS_var
[if] [in]
[weight],
outcome(varname)
[cmd(regression_cmd)
reg_type_t(type)
reg_type_gps(type)
interaction(#)]

fweights, iweights, and pweights are allowed;
see weight.

Description

doseresponse estimates the generalized propensity score (GPS),
verifies the normal model used for the GPS, and tests the balancing property
by calling the routine gpscore. Then it estimates the conditional
expectation of the outcome given the observed treatment and the estimated GPS
by calling the routine doseresponse_model. Finally, doseresponse
estimates the average potential outcome for each level of the treatment in
which the user is interested.

doseresponse_model defines all these models and estimates each of
them by using the estimated GPS.

Options

Required

outcome(varname) (doseresponse and
doseresponse_model) specifies that varname is the outcome variable.

t(varname) specifies that varname is the
treatment variable.

gpscore(newvar) specifies the variable name for
the estimated GPS, which is added to the dataset.

predict(newvar) creates a new variable to hold
the fitted values of the treatment variable.

sigma(newvar) creates a new variable to hold
the maximum likelihood estimate of the conditional standard error of the
treatment given the covariates.

cutpoints(varname) divides the set of potential
treatment values into intervals according to the sample distribution of the
treatment variable, cutting at varname quantiles.

index(string) specifies the representative
point of the treatment variable at which the GPS has to be evaluated within
each treatment interval. string identifies either the mean (string =
mean) or a percentile (string = p1, ..., p100) of the
treatment.

nq_gps(#) specifies that the values of the GPS
evaluated at the representative point index(string) of
each treatment interval have to be divided into # (1 <= # <= 100)
intervals, defined by the quantiles of the GPS evaluated at the representative
point index(string).

dose_response(newvarlist) specifies the
variable name(s) for the estimated dose-response function(s).

Optional

t_transf(transformation) specifies the
transformation of the treatment variable used in estimating the GPS. The
default transformation is the identity function. The supported
transformations are the logarithmic transformation, t_transf(ln); the
zero-skewness log transformation, t_transf(lnskew0); the zero-skewness
Box-Cox transformation, t_transf(bcskew0); and the Box-Cox
transformation, t_transf(boxcox).

normal_test(test) specifies
the goodness-of-fit test that gpscore will perform to assess
the validity of the assumed normal distribution model for the
treatment conditional on the covariates. By default, gpscore
performs the Kolmogorov-Smirnov test (normal_test(ksmirnov)). Possible alternatives are the
Shapiro-Francia test, normal_test(sfrancia);
the Shapiro-Wilk test, normal_test(swilk); and
the Stata skewness and kurtosis test for normality, normal_test(sktest).

norm_level(#) sets the significance level of
the goodness-of-fit test for normality. The default is norm_level(0.05).

test_varlist(varlist) specifies that the extent
of covariate balancing has to be inspected for each variable in varlist.
The default varlist consists of the variables used to estimate the GPS.
This option is useful when there are categorical variables among the
covariates to test the balancing property for the omitted group.

test(type) specifies whether the balancing
property has to be tested using a standard two-sided t test (the default) or a
Bayes-factor-based method.

flag(#) specifies that
gpscore estimates the GPS without performing either a goodness-of-fit
test for normality or a balancing test. The default # is 1, meaning that
both the normal distribution model and the balancing property are tested; the
default level is recommended.

cmd(regression_cmd) (doseresponse and
doseresponse_model) defines the regression command to be used for
estimating the conditional expectation of the outcome given the treatment and
the GPS. The default for the outcome variable is cmd(logit) when
there are two distinct values, cmd(mlogit) when there are 3-5 values, and
cmd(regress) otherwise. The supported regression commands are logit,
probit, mlogit, mprobit, ologit, oprobit, and
regress.

reg_type_t(type) (doseresponse and
doseresponse_model) defines the maximum power of the treatment variable
in the polynomial function used to approximate the predictor for the
conditional expectation of the outcome given the treatment and the GPS. The
default type is linear. Alternatively, type can be
quadratic or cubic.

reg_type_gps(type) (doseresponse and
doseresponse_model) defines the maximum power of the estimated GPS in
the polynomial function used to approximate the predictor for the conditional
expectation of the outcome given the treatment and the GPS. The default
type is linear. Alternatively, type can be quadratic
or cubic.

interaction(#) (doseresponse and
doseresponse_model) specifies whether the model for the conditional
expectation of the outcome given the treatment and the GPS has the interaction
between treatment and GPS. The default # is 1, meaning that the
interaction is included.

tpoints(vector) specifies that
doseresponse estimates the average potential outcome for each level of
the treatment in vector. By default, doseresponse creates a
vector with the ith element equal to the ith observed treatment value.
This option cannot be used with the npoints(#)
option.

npoints(#) specifies that doseresponse
estimates the average potential outcome for each level of the treatment
belonging to a set of evenly spaced values t0, t1, ..., t#, that cover
the range of the observed treatment. This option cannot be used with
the tpoints(vector) option.

delta(#) specifies that doseresponse also
estimates the treatment-effect function considering a #-treatment gap,
which is defined as E[Y(t + #)] - E[Y(t)]. The
default # is 0, meaning that doseresponse estimates only the
dose-response function.

filename(filename) stores the treatment levels
specified through the tpoints(vector) option or the
npoints(#) option, the estimated dose-response
function, and, eventually, the estimated treatment-effect function, along with
their standard errors (if calculated), to a new file called filename.

bootstrap(string) specifies the use of
bootstrap methods to derive standard errors and confidence intervals. By
default, doseresponse does not apply bootstrap techniques. In such a
case, no standard error is calculated. To activate this option, string
should be set to yes.

boot_reps(#) specifies the number of bootstrap
replications to be performed. The default is boot_reps(50).
This option produces an effect only if the bootstrap() option is set to
yes.

analysis(string) specifies that
doseresponse plots the estimated dose-response function(s) and the
estimated treatment-effect function(s), along with the corresponding confidence
intervals if they are calculated with bootstrapping. By default,
doseresponse plots only the estimated dose-response and treatment
function(s). If the user types analysis(no), no plot is
shown.

analysis_level(#) sets the confidence level of the confidence intervals. The default is analysis_level(0.95).

graph(filename) stores the plots of the
estimated dose-response function and the estimated treatment effects to a new
file called filename. When the outcome variable is categorical,
doseresponse creates a new file for each category i of the outcome
variable and names it filename_i.

detail displays detailed output for the gpscore command
and the results of the regression of the outcome on the treatment and the GPS.

Remarks

Please remember to use the update query command before running
this program to make sure you have an up-to-date version of Stata installed.
Otherwise, this program may not run properly.

The treatment has to be continuous. The outcome can be of any nature:
binary, categorical, or continuous.

Make sure that the variables in varlist do not contain missing values.

Examples

. #delimit ;
. doseresponse agew ownhs male tixbot workthen yearw,
. outcome(year6) t(prize) gpscore(mygps) predict(hat_treat) sigma(hat_sd)
. cutpoints(cut) index(p50) nq_gps(5) dose_response(mydoseresponse)
. ;

. #delimit ;
. doseresponse agew ownhs male tixbot workthen yearw,
. outcome(year6) t(prize) gpscore(mygps) predict(hat_treat) sigma(hat_sd)
. cutpoints(cut) index(p50) nq_gps(5) dose_response(mydoseresponse)
. t_transf(ln) normal_test(0.01) reg_type_t(quadratic)
. reg_type_gps(quadratic) bootstrap(yes)
. ;

. #delimit ;
. doseresponse agew ownhs male tixbot workthen yearw,
. outcome(year6) t(prize) gpscore(mygps) predict(hat_treat) sigma(hat_sd)
. cutpoints(cut) index(p50) nq_gps(5) dose_response(mydoseresponse)
. t_transf(ln) normal_test(0.01) test(Bayes_factor)
. reg_type_t(quadratic) reg_type_gps(quadratic) bootstrap(yes) analysis(no)
. ;

Authors

Michela Bia

Laboratorio Riccardo Revelli

Centre for Employment Studies, Collegio Carlo Alberto

michela.bia@laboratoriorevelli.it

Alessandra Mattei

Department of Statistics, "Giuseppe Parenti", University of Florence

mattei@ds.unifi.it

Also see

Article: Stata Journal, volume 8, number 3: st0150

Online: gpscore