Wrapper for pmsampsize with R-squared estimation and allowing for imprecision in the estimate of rate and mean follow-up.

calcSS(rate, time, parameters, meanfollowup, imprecision = 0.2)

Arguments

rate: Event rate at time
time: Time point to show performance at
parameters: Number of parameters in the model
meanfollowup: The mean follow-up time
imprecision: Allowance for imprecision in the rate and mean follow-up

Value

GGPlot object

Examples

calcSS(
  rate = 0.1,
  time = 3,
  parameters = 5,
  meanfollowup = 4
)
#> NB: Assuming 0.05 acceptable difference in apparent & adjusted R-squared 
#> NB: Assuming 0.05 margin of error in estimation of overall risk at time point = 3  
#> NB: Events per Predictor Parameter (EPP) assumes overall event rate = 0.08  
#>  
#>              Samp_size Shrinkage Parameter    CS_Rsq Max_Rsq Nag_Rsq   EPP
#> Criteria 1         403      0.90         5 0.1052543   0.702    0.15 20.63
#> Criteria 2         133      0.75         5 0.1052543   0.702    0.15  6.81
#> Criteria 3 *       403      0.90         5 0.1052543   0.702    0.15 20.63
#> Final SS           403      0.90         5 0.1052543   0.702    0.15 20.63
#>  
#>  Minimum sample size required for new model development based on user inputs = 403, 
#>  corresponding to 1289.6 person-time** of follow-up, with 104 outcome events 
#>  assuming an overall event rate = 0.08 and therefore an EPP = 20.63  
#>  
#>  * 95% CI for overall risk = (0.176, 0.249), for true value of 0.213 and sample size n = 403 
#>  **where time is in the units mean follow-up time was specified in