Design and Analysis of a 2x2 Factorial Trial.

factorial2x2

The goals of the factorial2x2 package are twofold: First, to provide power calculations for a two-by-two factorial design in which the effects of the two factors may be sub-additive. Power is provided for the overall effect test for as well as the multiple testing procedures described in Leifer, Troendle, Kolecki, and Follmann (2020). Second, to analyze two-by-two factorial trial data which may include baseline adjustment covariates. Further details are described in the factorial2x2 vignette.

Installation

You can install the released version of factorial2x2 from CRAN with:

install.packages("factorial2x2")

Example of a power calculation

We reproduce the power calculations for scenario 4 from Table 2 in Leifer, Troendle, et al. using the fac2x2design function.

  n <- 4600          # total sample size
  rateC <- 0.0445    # one year event rate in the control group
  hrA <- 0.80        # simple A effect hazard ratio
  hrB <- 0.80        # simple B effect hazard ratio
  hrAB <- 0.72       # simple AB effect hazard ratio
  mincens <- 4.0     # minimum censoring time in years
  maxcens <- 8.4     # maximum censoring time in years
  fac2x2design(n, rateC, hrA, hrB, hrAB, mincens, maxcens, dig = 2, alpha = 0.05)
  
$events
[1] 954.8738         # expected number of events
        
$evtprob             # event probabilities for the C, A, B, and AB groups, respectively
    probC     probA     probB    probAB 
0.2446365 0.2012540 0.2012540 0.1831806 

$powerEA3overallA    
[1] 0.5861992        # Equal Allocation 3's power to detect the overall A effect

$powerEA3simpleA    
[1] 0.5817954        # Equal Allocation 3's power to detect the simple A effect

$powerEA3simplAB    
[1] 0.9071236        # Equal Allocation 3's power to detect the simple AB effect

$powerEA3anyA
[1] 0.7060777        # Equal Allocation 3's power to detect either the overall A or simple A effects

$powerPA2overallA
[1] 0.6582819        # Proportional Allocation 2's power to detect the overall A effect

$powerPA2simpleAB
[1] 0.9197286        # Proportional Allocation 2's power to detect the simple AB effect

$powerEA2simpleA
[1] 0.6203837        # Equal Allocation 2's power to detect the simple A effect

$powerEA2simpleAB
[1] 0.9226679        # Equal Allocation 2's power to detect the simple AB effect

$powerA
[1] 0.7182932        # power to detect the overall A effect at the two-sided 0.05 level

References

Leifer, E.S., Troendle, J.F., Kolecki, A., Follmann, D. Joint testing of overall and simple effect for the two-by-two factorial design. 2020. Submitted.

Lin, D-Y., Gong, J., Gallo, P., et al. Simultaneous inference on treatment effects in survival studies with factorial designs. Biometrics. 2016; 72: 1078-1085.

Slud, E.V. Analysis of factorial survival experiments. Biometrics. 1994; 50: 25-38.