**README**
Here, you will find all data necessary to recreate the figures and tables of the main manuscript about the association between phenytoin exposure and post-COVID cognitive deficits.
Each RDS file contains an R object that contains the results for the comparison between phenytoin and another anti-epileptic drug:
**OSF_Phenytoin_vs_Levetiracetam.rds** contains the results for the comparison with levetiracetam.
**OSF_Phenytoin_vs_Valproate.rds** contains the results for the comparison with valproate.
Once loaded, the variable contains a list with the following fields:
`outcomeNames`: a vector of outcome names. This allows to then navigate the outcomes (e.g. "post-COVID cognitive deficits", the primary outcome, is outcome #2).
`outcomes`: a list of the same length as `outcomeNames` which contains the results for each outcome (i.e. it is a list of lists). For instance, `outcomes[[2]]` contains the results for post-COVID cognitive deficits. Each outcome contains the following fields:
- `HR`: the HR at 6 months
- `HR_CI`: the 95% CI of the HR at 6 months
- `HR_p`: the p-value of the HR at 6 months
- `KM`: a list that contains the data for the Kaplan-Meier curves and its confidence interval
`cohort1`: the name of the first cohort (set to 'Phenytoin')
`cohort2`: the name of the first cohort (set to 'Levetiracetam' or 'Valproate')
As an illustration, the following lines of code would plot the KM curves for post-COVID cognitive deficits (outcome #2) for the comparison with Valproate (corresponding to Figure 1B of the main manuscript without the 95% CI and without the style):
`res=readRDS('OSF_Phenytoin_vs_Valproate.rds')`
`plot(res$outcomes[[2]]$KM$time,1-res$outcomes[[2]]$KM$values2,type='l')`
`lines(res$outcomes[[2]]$KM$time,1-res$outcomes[[2]]$KM$values1,lty=2)`
Note that the KM values are stored as survival probability (and the need to calculate 1-value to get the incidence as in Fig. 1).
