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.
Related
I have a complicated data set which was made by a multistage stratified cluster design. I had originally analysed this using glm, however now realise that I have to use svyglm. I'm not quite sure about how is best to model the data utilising svyglm. I was wondering if anyone could help shed some light.
I am attempting to see the effect that a variety of covariates taken at time 1 have on a binary outcome taken at time 2.
The sampling strategy was as follows: state -> urban/rural -> district -> subdistrict -> village. Within each village, individuals were randomly selected, with each of these having an id (uniqid).
I have a variable in the df for each of these stages of the sampling strategy. I also have the following variables: outcome, age, sex, income, marital_status, urban_or_rural_area, uniqid, weights. The formula that I want for my regression equation is outcome ~ age + sex + income + marital_status + urban_or_rural_area . Weights are coded by the weights variable. I had set the family to binomial(link = logit).
If anyone has any idea how such an approach could be coded in R with svyglm I would be most appreciative. I'm quite confused as to what should be inputted as ID, fpc and nest. Do I have to specify all levels of the stratified design or just some?
Any direction, or resources which explain this well would be massively appreciated.
You don't really give enough information about the design: which of the geographical units are strata and which are clusters. For example, my guess is that you sample both urban and rural in all states, and you don't sample all villages, but I don't know whether you sample all districts or subdistricts. I also don't know whether your overall sampling fraction is large or small (so whether the with-replacement approximation is ok)
Let's pretend you sample just some districts, so districts are your Primary Sampling Units, and that the overall sampling fraction of people is small. The design command is
your_design <- svydesign(id=~district, weights=~weights,
strata=~interaction(state, urban_rural,drop=TRUE),
data=your_data_frame)
That is, the strata are combinations of state and urban/rural and any combinations that aren't in your data set don't exist in the population (maybe some states are all-rural or all-urban). Within each stratum you have districts, and only some of these appear in the sample. In your geographical hierarchy, districts are then the first level that is sampled rather than exhaustively enumerated.
You don't need fpc unless you want to specify the full multistage design without replacement.
The nest option is not about how the survey was done but is about how variables are coded. The US National Center for Health Statistics (bless their hearts) set up a lot of designs that have many strata and two primary sampling units per stratum. They call these primary sampling units 1 and 2; that is, they reuse the names 1 and 2 in every stratum. The svydesign function is set up to expect different sampling unit names in different strata, and to verify that each sampling unit name appears in just one stratum, as a check against data errors. This check has to be disabled for NCHS surveys and perhaps some others that also reuse sampling unit names. You can always leave out the nest option at first; svydesign will tell you if it might be needed.
Finally, the models:
svyglm(outcome ~ age + sex + income + marital_status + urban_or_rural_area,
design=your_design, family=quasibinomial)
Using binomial or quasibinomial will give identical answers, but using binomial will give you a harmless warning about non-integer weights. If you use quasibinomial, the harmless warning is suppressed.
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$weigths) there are many weights that are different from 1.
Why do the weights change when I use get_matches ? As far as I know, this should not be the case.
It is called 1:1 matching because each treated unit gets one match, but it is possible that control units are reused as matches for multiple treated units. If you set ratio to something other than 1, you would be doing k:1 matching, where each treated units gets k matches, composed of controls that may be resued for other treated units.
get_matches() produces a row for each unit for each time it is matched. If a control unit is matched twice (i.e., to two different treated units), it will have two rows each with a weight of 1 in the get_matches() output, but it will have a weight of 2 in the matchit() output (though this weight may be scaled to be different from 2). If you use match.data() instead of get_matches(), you will see that each unit receives only one row and the weights for each control unit are the same as in the matchit() output.
I am trying to match two samples on several covariates using MatchIt, but I am having difficulty creating samples that are similar enough. Both my samples are plenty large (~1000 in the control group, ~5000 in the comparison group).
I want to get a matched sample with participants as closely matched as possible and I am alright with losing sample size in the control group. Right now, MatchIt only returns two groups of 1000, whereas I want two groups that are very closely matched and would be fine with smaller groups (e.g., 500 instead of 1000).
Is there a way to do this through either MatchIt or another package? I would rather avoid using random sampling and then match if possible because I want as close a match between groups as possible.
Apologies for not having a reproducible example, I am still pretty new to using R and couldn't figure out how to make a sample of this issue...
Below is the code I have for matching the two groups.
data<- na.omit(data)
data$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).
I am trying to use the synth package in R.
The way synthetic control works is that it matches pre-treatment data for a treated unit and control units, and it selects weights to approximate equate the two, so that the treated unit "looks like" a synthetic control unit.
The way it works is explained here.
When matching on the pre-treatment outcomes, we pick up to T0 linear combination of the data. The synth package seems to only pick just one, and it is the one that equates the MEANS. This is what the predictor.op function does.
Suppose, however, I want to just have it so that I select all T0 linear combinations so X1 is a T0 x 1 vector rather than a 1x1, is there a way to do this non-manually?
I am not sure what exactly you are trying to do but I ran into your question because I had a similar issue with Synth() so maybe this will help:
I tried to create a synthetic control unit using all pre-treatment outcome observations and since Synth() averages across all pre-treatment periods, that wasn't too straightforward. What I did is to create individual covariates for each pre-treatment period and then specify those covariates in predictor. That is equivalent to not applying any operator to pre-treatment outcome data.
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!