Inverse probability weighting

[Luca.Afferi]

I'm using the twang package.

When I try to create the survival function:

fitw <- prodlim(Hist(followup, om)~treatment, caseweights=matchcn$w.ATE, data=matchcn)

the following message comes:

Error in prodlim(Hist(followup, om) ~ treatment, caseweights = matchcn$w.ATE, :

The length of caseweights is: 639

this is not the same as the number of subjects with no missing values, which is 599

Can anyone help me?

Thank you in advance,

Luca

[mara]

It sounds like you have missing values in your dataset, matchcn. I don't know the twang package, but you probably need to subset your data to exclude cases where there is an NA value for the subject. There are lots of ways to do this in R, see answers in the Stack Overflow thread below:

Remove rows with all or some NAs (missing values) in data.frame

[Luca.Afferi]

Dear Mara,

Thank you for your kind answer.

Unfortunately, I can't use the na.omit function because it would delete the majority of my data.

I tried to search a package for the pair-wise dealing with missing data, but I couldn't find one. Could you recommend a package for me?

What's intersting is that if I do the analyses before the weighting, NA are treated as NA. Then that message comes.

[mara]

I'm afraid I'm a little out of my depth in terms of what you're asking. If you could make a reprex, it would go a long way towards helping others help you:

A self-contained reprex (short for reproducible example) means that others can reproduce exactly what you're seeing.

install.packages("reprex")

If you've never heard of a reprex before, you might want to start by reading the tidyverse.org help page. The reprex dos and don'ts are also useful.

There's also a nice FAQ on how to do a minimal reprex for beginners, below:

FAQ: How to do a minimal reproducible example ( reprex ) for beginners

A minimal reproducible example consists of the following items: A minimal dataset, necessary to reproduce the issue The minimal runnable code necessary to reproduce the issue, which can be run on the given dataset, and including the necessary information on the used packages. Let's quickly go over each one of these with examples: Minimal Dataset (Sample Data) You need to provide a data frame that is small enough to be (reasonably) pasted on a post, but big enough to reproduce your issue. Let's say, as an example, that you are working with the iris data frame head(iris) #> Sepal.Length Sepal.Width Petal.Length Petal.Width Species #> 1 5.1 3.5 1.4 0.2…

What to do if you run into clipboard problems

If you run into problems with access to your clipboard, you can specify an outfile for the reprex, and then copy and paste the contents into the forum.

reprex::reprex(input = "fruits_stringdist.R", outfile = "fruits_stringdist.md")

For pointers specific to the community site, check out the reprex FAQ.