Personalised Synthetic Controls.

psc

The goal of psc is to compare a dataset of observations against a parametric model

Installation

You can install the development version of psc from GitHub with:

# install.packages("devtools")
devtools::install_github("richJJackson/psc")

Example

This is a basic example which shows you how to solve a common problem:

library(psc)
library(survival)
## basic example code

### Load model
data("surv.mod")

### Load Data
data("data")

### Use 'pscfit' to compare
surv.psc <- pscfit(surv.mod,data)
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You can use standard commands for getting a summary of your analysis…

summary(surv.psc)
#> Summary: 
#>  
#> 100 observations selected from the data cohort for comparison 
#> CFM of type flexsurvreg identified  
#> linear predictor succesfully obtained with median: 
#>  trt: 3.15
#> Average expected response: 
#>  trt: 9.1
#> Average observed response: 6.366 
#> 
#> Counterfactual Model (CFM): 
#> A model of class 'flexsurvreg' 
#>  Fit with 3 internal knots
#> 
#> Formula: 
#> Surv(time, cen) ~ vi/age60 + ecog + allmets + logafp + alb + 
#>     logcreat + logast + aet
#> <environment: 0x11a95d660>
#> 
#> Call:
#>  CFM model + beta
#> 
#> Coefficients:
#>       median    2.5%      97.5%     Pr(x<0)   Pr(x>0) 
#> beta    0.3574    0.1204    0.5801    0.0004    0.9996
#> DIC   280.8601  273.6541  291.9444        NA        NA

… and to see a plot of what you have done

In that case, don’t forget to commit and push the resulting figure files, so they display on GitHub and CRAN.