*! version 1.0.0 17june2014
help mqgamma

Title

mqgamma -- Marginal quantile estimation

Syntax

mqgamma depvar [indeps] [if] [in]
, [quantile(numlist) lns(varlist)
fail(varname) aequations
from(matrix) display options
]

Description

mqgamma estimates marginal quantiles for the potential outcome
corresponding to each treatment level from censored observational data in
which the dependent variable is inherently positive, such as time-to-event
data and health-expenditure data. Differences in these marginal quantiles
are quantile treatment effects.

mqgamma fits a two-parameter gamma distribution the conditional
distributions from which the marginal distributions are subsequently
estimated by regression adjustment.

Drukker (2014) derives the implemented estimator and provides an
introduction to this command.

Options

treat(varname is a required option and it specifies
the binary treatment variable. The treatment variable must be coded 0 for
control cases and 1 for treated cases.

quantile(numlist) specifies the marginal quantiles
to estimated. Each specified quantile must be in (0,1).

lns(varlist) specifies the variables used to model
the natural log of the scale. See the Methods and Formulas section of
Drukker (2014) for details.

fail(varname) specifies the binary failure indicator
which must be coded 1 for an observed value and 0 for a censored
observation.

aequations specifies that the auxiliary-equation parameters
should be displayed.

from(matrix) specifies a row vector of initial
values for the optimization routine. Each element in the specified matrix
specifies the initial value for the corresponding parameter.

display options are the standard display options. See
estimation options

Examples

Setup

. use exercise

Medians of potential outcomes estimation

. mqgamma t active, treat(exercise) fail(fail) lns(health)

QTE(.25) and QTE(.75) estimation

. mqgamma t active, treat(exercise) fail(fail) lns(health) quantile(.25 .75)

. nlcom (_b[q25_1:_cons] - _b[q25_0:_cons]) (_b[q75_1:_cons] - _b[q75_0:_cons])

References

Drukker, D. M. 2014. Quantile treatment effect estimation from
censored data by regression adjustment. Working paper, submitted to the
Stata Journal.
pdf paper