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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.
You can install the released version of factorial2x2 from CRAN with:
install.packages("factorial2x2")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 levelLeifer, 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.
These binaries (installable software) and packages are in development.
They may not be fully stable and should be used with caution. We make no claims about them.
Health stats visible at Monitor.