Generic summary function for class MAMS.

Produces a detailed summary of an object from class MAMS

Usage

# S3 method for class 'MAMS'
summary(
  object,
  digits = max(3, getOption("digits") - 4),
  extended = FALSE,
  ...
)

Arguments

object

An output object of class MAMS

digits

Number of significant digits to be printed.

extended

TRUE or FALSE

...

Further arguments passed to or from other methods.

Value

Text output.

Author

Dominique-Laurent Couturier

Examples

# \donttest{
# 2-stage design with triangular boundaries
res <- mams(K=4, J=2, alpha=0.05, power=0.9, r=1:2, r0=1:2,
             p=0.65, p0=0.55,
             ushape="triangular", lshape="triangular", nstart=30)
#> 
#>    i) find lower and upper boundaries
#>       
#> .
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#> .
#> 
#>   ii) define alpha star
#>  iii) perform sample size calculation
#>       (maximum iteration number = 259)
#>       
#> .
#> .
#> .
#> .
#> .
#> .
#> .
#> .
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#> .
#> .
#> 50
#>       
#> 
#>   iv) run simulation 

summary(res)
#> 
#> ── MAMS design ─────────────────────────────────────────────────────────────────
#> 
#> ── Design characteristics ──
#> 
#> • Normally distributed endpoint
#> • Simultaneous stopping rules
#> • 2 stages
#> • 4 treatment arms
#> • 5% overall type I error
#> • 90% power of detecting Treatment 1 as the best arm
#> • Assumed effect sizes per treatment arm:
#> 
#>                    | Under H1            | Under H0 
#>               abbr | cohen.d prob.scale  | cohen.d prob.scale
#>   Treatment 1   T1 |   0.545       0.65  |       0        0.5
#>   Treatment 2   T2 |   0.178       0.55  |       0        0.5
#>   Treatment 3   T3 |   0.178       0.55  |       0        0.5
#>   Treatment 4   T4 |   0.178       0.55  |       0        0.5
#> 
#> ── Limits ──
#> 
#>              Stage 1 Stage 2      shape
#> Upper bounds   2.432   2.293 triangular
#> Lower bounds   0.811   2.293 triangular
#> 
#> ── Sample sizes ──
#> 
#>                             | Expected (*)
#>             Cumulated       | Under H1         | Under H0
#>             Stage 1 Stage 2 | low     mid high | low     mid high
#> Control          50     100 |  50  67.501  100 |  50  71.828  100
#> Treatment 1      50     100 |  50  67.141  100 |  50  59.587  100
#> Treatment 2      50     100 |  50  55.723  100 |  50  59.489  100
#> Treatment 3      50     100 |  50  55.759  100 |  50  59.540  100
#> Treatment 4      50     100 |  50  55.759  100 |  50  59.498  100
#> TOTAL           250     500 | 250 301.883  500 | 250 309.942  500
#> 
#> ── Futility cumulated probabilities (§) ──
#> 
#>              Under H1        | Under H0
#>              Stage 1 Stage 2 | Stage 1 Stage 2
#> T1  rejected   0.029   0.066 |   0.789   0.973
#> T2  rejected   0.467   0.561 |   0.792   0.975
#> T3  rejected   0.466   0.561 |   0.790   0.973
#> T4  rejected   0.467   0.563 |   0.791   0.974
#> Any rejected   0.719   0.747 |   0.964   0.989
#> All rejected   0.022   0.064 |   0.539   0.950
#> 
#> ── Efficacy cumulated probabilities (§) ──
#> 
#>              Under H1        | Under H0
#>              Stage 1 Stage 2 | Stage 1 Stage 2
#> T1  rejected   0.616   0.921 |   0.007   0.015
#> T2  rejected   0.061   0.081 |   0.007   0.014
#> T3  rejected   0.060   0.080 |   0.007   0.015
#> T4  rejected   0.062   0.081 |   0.007   0.014
#> Any rejected   0.628   0.936 |   0.024   0.050
#> T1  is best    0.600   0.903 |   0.006   0.013
#> All rejected   0.006   0.007 |   0.000   0.000
#> 
#> • Estimated prob. T1 is best (§) = 90.29%, [90.03, 90.548] 95% CI
#> • Estimated overall type I error (*) = 4.982%, [4.792, 5.174] 95% CI
#> 
#> (*) Operating characteristics estimated by a simulation
#>     considering 50000 Monte Carlo samples
#> ────────────────────────────────────────────────────────────────────────────────
# }