View source: R/stratumStructure.R
stratumStructure  R Documentation 
Tabulate treatment:control ratios occurring in matched sets, and the frequency of their occurrence.
stratumStructure(stratum, trtgrp = NULL, min.controls = 0, max.controls = Inf)
## S3 method for class 'optmatch'
stratumStructure(stratum, trtgrp, min.controls = 0, max.controls = Inf)
## Default S3 method:
stratumStructure(stratum, trtgrp, min.controls = 0, max.controls = Inf)
## S3 method for class 'stratumStructure'
print(x, ...)
stratum 
Matched strata, as returned by

trtgrp 
Dummy variable for treatment group membership. (Not
required if 
min.controls 
For display, the number of treatment group
members per stratum will be truncated at the reciprocal of

max.controls 
For display, the number of control group
members will be truncated at 
x 
stratumStructure object to be printed. 
... 
Additional arguments to 
A table showing frequency of occurrence of those treatment:control ratios that occur.
The ‘effective sample size’ of the stratification, in
matched pairs. Given as an attribute of the table, named
‘comparable.num.matched.pairs
’; see Note.
For comparing treatment and control groups both of size 10,
say, a stratification consisting of two strata, one with 9
treatments and 1 control, has a smaller ‘effective sample
size’, intuitively, than a stratification into 10 matched pairs,
despite the fact that both contain 20 subjects in
total. stratumStructure
first summarizes this aspect of
the structure of the stratification it is given, then goes on to
identify one number as the stratification's effective sample
size. The ‘comparable.num.matched.pairs
’
attribute returned by stratumStructure
is the sum of
harmonic means of the sizes of the treatment and control
subgroups of each stratum, a general way of calibrating such
differences as well as differences in the number of subjects
contained in a stratification. For example, by this metric the
9:1, 1:9 stratification is comparable to 3.6 matched pairs.
Why should effective sample size be calculated this way? The
phrase ‘effective sample size’ suggests the observations
are taken to be similar in information content. Modeling them
as random variables, this suggests that they be assumed to have
the same variance, \sigma
, conditional on what
stratum they reside in. If that is the case, and if also
treatment and control observations differ in expectation by a
constant that is the same for each stratum, then it can be shown
that the optimum weights with which to combine treatmentcontrol
contrasts across strata, s
, are proportional to the
stratumwise harmonic means of treatment and control counts,
h_s = [(n_{ts}^{1} + n_{cs}^{1})/2]^{1}
(Kalton, 1968). The thusweighted
average of contrasts then has variance 2\sigma/\sum_s
h_s
. This motivates the use of \sum_s
h_s
as a measure of effective sample size (Hansen, 2011).
Somewhat different motivations of the same calculation appear
in Hansen (2004) and in Hansen and Bowers (2008). Since for a
matched pair s
, h_s=1
, \sum_s
h_s
can be thought of as the number of matched pairs
needed to attain comparable precision.
Ben B. Hansen
Kalton, G. (1968), ‘Standardization: A technique to control for extraneous variables’, Applied Statistics, 17, 118–136.
Hansen, B.B. (2004), ‘Full Matching in an Observational Study of Coaching for the SAT’, Journal of the American Statistical Association, 99, 609–618.
Hansen B.B. and Bowers, J. (2008), ‘Covariate balance in simple, stratified and clustered comparative studies’, Statistical Science, 23 (2), 219–236.
Hansen, B.B. (2011), ‘Propensity score matching to extract latent experiments from nonexperimental data: A case study’. Ch. 9 of Looking Backwards: Proceedings from a Conference in Honor of Paul W. Holland, Springer.
matched
, fullmatch
data(plantdist)
plantsfm < fullmatch(plantdist) # A full match with unrestricted
# treatmentcontrol balance
plantsfm1 < fullmatch(plantdist,min.controls=2, max.controls=3)
stratumStructure(plantsfm)
stratumStructure(plantsfm1)
stratumStructure(plantsfm, max.controls=4)
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