I am trying to match two samples on several covariates using MatchIt, but I am having difficulty creating samples that are similar enough. Both my samples are plenty large (~1000 in the control group, ~5000 in the comparison group).
I want to get a matched sample with participants as closely matched as possible and I am alright with losing sample size in the control group. Right now, MatchIt only returns two groups of 1000, whereas I want two groups that are very closely matched and would be fine with smaller groups (e.g., 500 instead of 1000).
Is there a way to do this through either MatchIt or another package? I would rather avoid using random sampling and then match if possible because I want as close a match between groups as possible.
Apologies for not having a reproducible example, I am still pretty new to using R and couldn't figure out how to make a sample of this issue...
Below is the code I have for matching the two groups.
data<- na.omit(data)
data$Group<- as.numeric(data$Group)
data$Group<- recode(data$Group, '1 = 1; 2 = 0')
m.out <- matchit(Group ~ Age + YearsEdu + Income + Gender, data = data, ratio = 1)
s.out <- summary(m.out, standardize = TRUE)
plot(s.out)
matched.data <- match.data(m.out)
MatchIt, like other similar packages, offers several matching routines that enable you to play around with the settings. Check out the argument method, which is set to method = 'nearest' by default. This means that unless you specify, it will look for the best match for each of the treatment observations. In your case, you will always have 1000 paired matches with this setting.
You can choose to set it to method = 'exact', which is much more restrictive. In the documentation you will find:
This technique matches each treated unit to all
possible control units with exactly the same values on all the covariates, forming subclasses
such that within each subclass all units (treatment and control) have the same covariate values.
On the lalonde dataset, you can run:
m.out <- matchit(treat ~ educ + black + hispan, data = lalonde, method = 'exact')
summary(m.out)
As a consequence, it discards some of the treatment observation that could not get matched. Have a look at the other possibilities for method, maybe you will find something you will like better.
That being said, be mindful not to discard too many treatment observations. If you do, you will make the treatment group look like the control group (instead of the opposite), which might lead to unwanted results.
You should look into the package designmatch, which implements a form of matching called cardinality matching that does what you want (i.e., find the largest matched set that yields desired balance). Unlike MatchIt, designmatch doesn't use a distance variable; instead, it uses optimization to solve the matching problem. You select exactly how balanced you want each covariate to be, and it will do its best to solve the problem while retaining as many matches as possible. The methodology is described in Zubizarreta, Paredes, & Rosenbaum (2014).
Related
I would like to use nearest neighbour matching with MatchIt in R.
So far I have used the following code:
Matching<- matchit(Treatment ~ Size+ Age + Expenses, data=data, method = "nearest", distance="glm", replace=TRUE)
I have two questions:
Question 1.)
When I run this code and run Matching again then I get a summary.
One line then says
A matchit object
- method: 1:1 nearest neighbor matching with replacement
I want to have the same control observation to be matched multiple times if neeeded.
Is the code above doing that?
I am confused since it says 1:1 nearest neighbor matching with replacement, I don't know if it now only uses an observation in the control group not more than once due to the 1:1 part in the sentence. However, since I use replace=true in the code I thought that this does exactly that so that one observation in the control group can be matched several times.
Could someone explain to me if my understanding is correct?
Question 2.)
After having run
Matching<- matchit(Treatment ~ Size+ Age + Expenses, data=data, method = "nearest", distance="glm", replace=TRUE)
I would like to estimate the average treatment effect.
I use the following document as a reference on how to estimate it:
https://cran.r-project.org/web/packages/MatchIt/vignettes/estimating-effects.html#after-pair-matching-with-replacement
However, I would like to use clustered standard errors by subclass and id.
Therefore, I need to first write the code:
Matching_gm <- get_matches(Matching)
When I look at the weights of Matching_gm they are always 1. However, when I run summary(Matching$weigths) there are many weights that are different from 1.
Why do the weights change when I use get_matches ? As far as I know, this should not be the case.
It is called 1:1 matching because each treated unit gets one match, but it is possible that control units are reused as matches for multiple treated units. If you set ratio to something other than 1, you would be doing k:1 matching, where each treated units gets k matches, composed of controls that may be resued for other treated units.
get_matches() produces a row for each unit for each time it is matched. If a control unit is matched twice (i.e., to two different treated units), it will have two rows each with a weight of 1 in the get_matches() output, but it will have a weight of 2 in the matchit() output (though this weight may be scaled to be different from 2). If you use match.data() instead of get_matches(), you will see that each unit receives only one row and the weights for each control unit are the same as in the matchit() output.
Matchit R - how to run ratio of 1 CONTROL: 2 TREATED units? (20 CONTROLS FOR 40 TREATED SUBJECTS)
Ratio = for methods that allow it, how many control units should be matched to each treated unit in k:1 matching.
0.5 IS IMPOSSIBLE
how can I apply it?
I think this isn't possible because it doesn't make sense. Your question implies that treated units can be paired, but that presupposes a prior step in which treated units are matched with each other, which is not part of what MatchIt is designed to do (and doesn't really make sense from an experimental design standpoint anyway).
Either that, or you have pairs of treated cases that match exactly, in which case you're not really gaining any analytical leverage by trying to use them both. If that's the case, I would just reduce the pairs to single observation and go with 1:1 matching.
This is actually a much a harder question than it may seem. There is definitely no way to do this with nearest neighbor matching. In theory, it should be possible with optimal matching, e.g., using method = "full" or the optmatch package directly. Unfortunately, in my experiments, I am unable to get optmatch to do what you want. Ideally, you should be able to run
fullmatch(., min.controls = 1/2, max.controls = 1/2, mean.controls = 1/2)
but that doesn't actually produce the desired results. Maybe you can contact the opmatch authors with this problem, as it's an interesting one that their package should be able to solve.
There is another method you can use called cardinality matching, which will implemented in the next release of MatchIt but can be accessed in the development version using devtools::install_github("ngreifer/MatchIt"). Cardinality matching uses optimzation to select a subset of units that satisfy a sample size constraint and balance constraints set by the user. In this case, your sample size constraint is that the number of treated units is twice the number of control units. You need to set balance constraints, which are the maximum imbalance (in standardized mean difference units) allowable between the treated and control groups. If A is your treatment variable and X1 and X2 are the variables you are trying to match on, you could run the following code:
m <- matchit(A ~ X1 + X2, dataa = data, method = "cardinality",
estimand = "ATC", ratio = 2, tols = .01)
If a solution is possible, it will be produced. If not, you will have to relax the tolerance (i.e., increase tols) until it is. You can the run
summary(m, un = FALSE)
which will produce a summary of balance and the remaining sample size. If not all of your treated units are matched, then continue to increase tols until they are.
I'm rather new to R and especially to the method of matching by propensity scores. My dataset includes two groups of people that differ in whether they were treated or not- unfortunately they also differ significantly in age and disease duration, therefore my wish to match them.
So far this is my code:
set.seed(2208)
mod_match <- matchit(TR ~ age + disease_duration + sex + partner + work + academic,
data = Data_nomiss,
method = "nearest",
caliper = .025)
summary(mod_match)
This code works fine, but I wondered whether there is a possibility to weight the importance of the covariates regarding the accuracy of matching? For me it is crucial that the groups are as close as possible concerning age and disease duration (numeric), whereas the rest of the variables (factors) should also be matched, but for my purposes might differ in means a little more than the first two.
While searching for a solution to my problem I came across the request of this one guy, who had basically the same problem http://r.789695.n4.nabble.com/matchit-can-I-weight-the-parameters-td4633907.html
In this case it was proposed to combine nearest neighbor and exact matching, but transferred to my dataset this leads to an unproportional reduction of my sample. In the end what I'd like to have is some sort of customized matching process focussing on age and disease duration while also involving the last three variables but in a weaker way.
Does anyone happen to have an idea how this could be realized? I'd be really glad to receive any kinds of tips on this matter and thank you for your time!
Unfortunately, MatchIt does not provide this functionality. There were two ways to do this instead of using MatchIt, but they are slightly advanced. Note that neither use propensity scores. The point of propensity score matching is to match on a single number, the propensity score, which makes the matching procedure blind to the original covariates for which balance is desired.
The first is to use the package Matching and include your own weight matrix to Weight.matrix in Match(). You could upweight age and disease duration in the weight matrix.
The second is to use the package designmatch to do cardinality matching, which allows you to specify balance constraints, and it will use optimization to find the largest sample that meets those constraints. In bmatch(), enter your covariates of interest into the mom argument, which also allows you to include specific balance constraints for each variable. You can require stricter balance constraints for age and disease duration.
I am currently working on a propensity scoring match model.
An example of my code is provided below:
library(MatchIt)
head(data)
match.it.data <- matchit(TARGET ~ PROBABILITY, data = data, method = "nearest", ratio = 1)
I was wondering if anyone knew how to allow my Treated group observations to be matched with the Control group observations more than once.
In other words, a control group observation would be able to be matched with my Treated group more than once. So, rather than doing a one to one doing a one to the closest possible. So, potentially allow the the treated group to resample the control group more than once sort of like a bootstrap simulation would work with multiple iterations.
thank you for your help.
Simply add replace = TRUE in the call to matchit().
I am using the following code to match 2 cohorts (2800 controls, 460 treated) of different patients:
set.seed(99)
m.out <- matchit(treatment ~ gender + age + VarC + diseaseDuration +
pastActivity + activity + country, data = Pat.match,
method = "nearest", ratio = 5, discard = "control",
caliper = 0.1, m.order = "smallest")
After matching, the cohorts are reduced to about 1230 controls vs. 400 treated.
These numbers are similar when I change the seed. However, if I check more accurately (patient ID), the total cohorts for different seeds differ in about 20% of the patients. To be more precise:
set.seed(99) results in a cohort, that has an overlap of only 80% with the resulting cohort of set.seed(27).
And this might have a huge impact on further general models and statistical analyses. Have I overseen something ?
Regards !
Sometimes this occurs when units have the same or very similar propensity scores; I believe MatchIt resolves this with a randomly selected match. I actually disagree with #dash2 that you shouldn't change the seed until you get a result you like. You should perform the matching procedure as many times as you want until you arrive at covariate balance. If your data is balanced and all your treated units are retained (or at least the same ones across matching specifications), then your effect estimation will not vary systematically with your matched set. Just remember that once you have estimated your treatment effect, you can't go back and redo your matching results (which is probably what #dash2 is getting at). But at the matching phase, this is not a concern.
So the computing side of this is that matchit is probably doing something random, even though you haven't e.g. specified m.order="random". What that could be, it's probably easiest to find out by looking through the source code...
The statistical side is really for crossvalidated, not here, but I would suggest:
if any of your results depend non-trivially on the seed, don't trust the results, they are not robust.
in particular - I'm sure you know this but it is worth reiterating - do NOT try different seeds until you get a result you like!