I would ignore the clusters when doing matching. CEM will find the
experiment hidden within the observed data, and that might be in only some
of the clusters or parts of the clusters, so I'd let it do that. then
decide what you want to estimate afterwards.
Best of luck with your research,
Gary
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*Gary King* - Albert J. Weatherhead III University Professor - Director,
IQSS <http://iq.harvard.edu/>- Harvard University
GaryKing.org - King(a)Harvard.edu - @KingGary <https://twitter.com/kinggary> -
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495-9271
On Tue, Apr 22, 2014 at 3:39 PM, Emily White Johansson <
emily.johansson(a)kbh.uu.se> wrote:
This is a question about matching procedures, CEM and
clustered
observations.
I am conducting a meta-analyses of national cross-sectional cluster
sample surveys (Demographic and Health Surveys) to examine the effect of
diagnostic testing on drugs used to treat pediatric fevers in multiple
sub-Saharan African countries. We are currently using mixed-models
adjusted for confounding covariates and data clustering (random effects:
PSU and country identifiers).
I would like to use CEM to pre-process data to balance a set of
confounders (e.g. maternal education, child’s age, etc) across treatment
groups (tested and untested kids in our study), and then run a logistic
regression on the matched dataset to quantify the influence of testing on
treatments.
My question then is how to account for data clustering in matching and
subsequent regression adjustments? If we matched children using CEM
according to country, could we then relax the model specifications such
that country does not need to be included a random effect? But still, we
need to account for data clustering at the PSU level. Do we still need a
mixed-model approach for the matched dataset, or is a simple multivariate
logistic regression adequate even if observations are not independent?
Otherwise, is matching even advisable in this scenario and best to
continue using our mixed-model approach?
Your thoughts on this issue is greatly appreciated.
With many thanks
Emily White Johansson
PhD student
Uppsala University
Dept Women's and Children's Health
International Maternal and Child Health
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