The goal of Bayespmtools is to enable Bayesian sample size and precision calculations for external validation of risk prediction models.
For the details of the methodology, please refer to the accompanying paper: https://doi.org/10.1002/sim.70389
#Specify evidence:
evidence <- list(
prev ~ beta(116, 155), # Outcome prevalence
cstat ~ beta(3628, 1139), # C-statistic
cal_mean ~ norm(-0.009, 0.125), # Mean calibration error
cal_slp ~ norm(0.995, 0.024) # Calibration slope
)
#Specifying targets
#eciw=x indicates desired expected CI Width of x.
#qciw=c(a,b) indicates desired assurance CI Width of b at assurance level a.
#voi.nb indicates targeting a given EVSI/EVPI ratio
targets <- list(
eciw.cstat = 0.1, # Expected CI width for c-statistic
eciw.cal_oe = 0.22, # Expected CI width for O/E ratio
eciw.cal_slp = 0.30, # Expected CI width for calibration slope
qciw.cal_slp = c(0.90, 0.35), # Quantile of CI width for calibration slope (assurance)
voi.nb = 0.90 # EVSI/EVPI ratio
)
library(bayespmtools)
#Main function call
res <- bpm_valsamp(
evidence = evidence,
targets = targets,
n_sim = 1000, # Number of Monte Carlo simulations
threshold = 0.2 # Risk threshold for net benefit calculations
)
#> Processing evidence...
#> Generating Monte Carlo sample...
#> Imputing correlation, based on effective sample size: 272 ...
#> Infering calibration intercept...
#> Computing CI sample size...
#> Computing se/sp...
#> VoI / NB assuraance...
print(res$results)
#> eciw.cstat eciw.cal_oe eciw.cal_slp qciw.cal_slp voi.nb
#> 361 420 1072 870 717
