In matchit(), setting method = "subclass" performs
subclassification on the distance measure (i.e., propensity score).
Treatment and control units are placed into subclasses based on quantiles of
the propensity score in the treated group, in the control group, or overall,
depending on the desired estimand. Weights are computed based on the
proportion of treated units in each subclass. Subclassification implemented
here does not rely on any other package.
This page details the allowable arguments with method = "subclass".
See matchit() for an explanation of what each argument means in a general
context and how it can be specified.
Below is how matchit() is used for subclassification:
matchit(formula,
data = NULL,
method = "subclass",
distance = "glm",
link = "logit",
distance.options = list(),
estimand = "ATT",
discard = "none",
reestimate = FALSE,
s.weights = NULL,
verbose = FALSE,
...) a two-sided formula object containing the treatment and covariates to be used in creating the distance measure used in the subclassification.
a data frame containing the variables named in formula.
If not found in data, the variables will be sought in the
environment.
set here to "subclass".
the distance measure to be used. See distance
for allowable options. Must be a vector of distance scores or the name of a method of estimating propensity scores.
when distance is specified as a string, an additional
argument controlling the link function used in estimating the distance
measure. See distance for allowable options with each option.
a named list containing additional arguments
supplied to the function that estimates the distance measure as determined
by the argument to distance.
the target estimand. If "ATT", the default,
subclasses are formed based on quantiles of the distance measure in the
treated group; if "ATC", subclasses are formed based on quantiles of
the distance measure in the control group; if "ATE", subclasses are
formed based on quantiles of the distance measure in the full sample. The
estimand also controls how the subclassification weights are computed; see
the Computing Weights section at matchit() for details.
a string containing a method for discarding units outside a region of common support.
if discard is not "none", whether to
re-estimate the propensity score in the remaining sample prior to
subclassification.
the variable containing sampling weights to be incorporated into propensity score models and balance statistics.
logical; whether information about the matching
process should be printed to the console.
additional arguments that control the subclassification:
subclasseither the number of subclasses desired or a vector of quantiles used to divide the distance measure into subclasses. Default is 6.
min.nthe minimum number of
units of each treatment group that are to be assigned each subclass. If the
distance measure is divided in such a way that fewer than min.n units
of a treatment group are assigned a given subclass, units from other
subclasses will be reassigned to fill the deficient subclass. Default is 1.
The arguments exact, mahvars, replace, m.order, caliper (and related arguments), and ratio are ignored with a warning.
All outputs described in matchit() are returned with
method = "subclass" except that match.matrix is excluded and
one additional component, q.cut, is included, containing a vector of
the distance measure cutpoints used to define the subclasses. Note that when
min.n > 0, the subclass assignments may not strictly obey the
quantiles listed in q.cut. include.obj is ignored.
After subclassification, effect estimates can be computed separately in the
subclasses and combined, or a single marginal effect can be estimated by
using the weights in the full sample. When using the weights, the method is
sometimes referred to as marginal mean weighting through stratification
(MMWS; Hong, 2010) or fine stratification weighting (Desai et al., 2017).
The weights can be interpreted just like inverse probability weights. See vignette("estimating-effects") for details.
Changing min.n can change the quality of the weights. Generally, a
low min.w will yield better balance because subclasses only contain
units with relatively similar distance values, but may yield higher variance
because extreme weights can occur due to there being few members of a
treatment group in some subclasses. When min.n = 0, some subclasses may fail to
contain units from both treatment groups, in which case all units in such subclasses
will be dropped.
Note that subclassification weights can also be estimated using WeightIt, which provides some additional methods for estimating propensity scores. Where propensity score-estimation methods overlap, both packages will yield the same weights.
In a manuscript, you don't need to cite another package when
using method = "subclass" because the subclassification is performed
completely within MatchIt. For example, a sentence might read:
Propensity score subclassification was performed using the MatchIt package (Ho, Imai, King, & Stuart, 2011) in R.
It may be a good idea to cite Hong (2010) or Desai et al. (2017) if the treatment effect is estimated using the subclassification weights.
Desai, R. J., Rothman, K. J., Bateman, B. . T., Hernandez-Diaz, S., & Huybrechts, K. F. (2017). A Propensity-score-based Fine Stratification Approach for Confounding Adjustment When Exposure Is Infrequent: Epidemiology, 28(2), 249–257. tools:::Rd_expr_doi("10.1097/EDE.0000000000000595")
Hong, G. (2010). Marginal mean weighting through stratification: Adjustment for selection bias in multilevel data. Journal of Educational and Behavioral Statistics, 35(5), 499–531. tools:::Rd_expr_doi("10.3102/1076998609359785")
matchit() for a detailed explanation of the inputs and outputs of
a call to matchit().
method_full for optimal full matching and method_quick for generalized full matching, which are similar to
subclassification except that the number of subclasses and subclass
membership are chosen to optimize the within-subclass distance.
data("lalonde")
# PS subclassification for the ATT with 7 subclasses
s.out1 <- matchit(treat ~ age + educ + race + nodegree +
married + re74 + re75, data = lalonde,
method = "subclass", subclass = 7)
s.out1
summary(s.out1, subclass = TRUE)
# PS subclassification for the ATE with 10 subclasses
# and at least 2 units in each group per subclass
s.out2 <- matchit(treat ~ age + educ + race + nodegree +
married + re74 + re75, data = lalonde,
method = "subclass", subclass = 10,
estimand = "ATE", min.n = 2)
s.out2
summary(s.out2)
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