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Treatment Resistance in Schizophrenia
Recently, there's been some interest in formalizing what is meant by treatment resistance. Using data from the CATIE trial, we looked at:
- can a consensus algorithm for treatment resistance be implemented and used on "repurposed" data?
- can we then estimate the incidence rate for a community-based sample of people with chronic illness?
- can we perform meaningful inferential and predictive analyses (i.e. to predict people likely to develop TRS from baseline data)?
The CATIE data is public by application here. To make this project reproducible (as far as possible), the R notebooks are given below, and links to the necessary data (derived from the CATIE source data set). There's a lot of pre-processing that can't easily be made public because it requires the source data. However, happy to provide R scripts that performs this on request if, for example, you have access to the source data.
The analyses are presented as R notebooks:
- missing data analyses: to run this standalone, you'll need these derived data
- descriptive and inferential analyses: to run this, you'll need the output from the missing data analyses notebook above. If you want to run it "stand alone", you'll need:
- RData store for descriptives/inferential
- predictive modeling - initial attempts to predict people who will develop TRS, given the baseline clinical state and demographic information available in CATIE. To run standalone, you'll need this CSV collection:
- tabulated data for predictive analyses