Description Usage Arguments Details Value References See Also Examples
Computes the critical values for null hypotheses rejection and corresponding nominal twosided significance levels for the Equal Allocation 3, Proportional Allocation 2, and Equal Allocation 2 procedures
1 2 3 4 5 6 7 8 9 10  crit2x2(
corAa,
corAab,
coraab,
dig = 2,
alpha = 0.05,
niter = 5,
abseps = 1e05,
tol = 1e04
)

corAa 
correlation between the overall A and simple A log hazard ratio estimates 
corAab 
correlation between the overall A and simple AB log hazard ratio estimates 
coraab 
correlation between the simple A and simple AB log hazard ratio estimates 
dig 
number of decimal places to which we 
alpha 
twosided familywise error level to control 
niter 
number of times we compute the critical values to average out
the randomness from the 
abseps 

tol 

This function computes the Dunnettcorrected critical values
based on the asymptotic correlations of the overall A, simple A, and simple AB
logrank statistics as described in Leifer, Troendle, et al. (2020) and are derived in
Lin, Gong, et al. (2016) and Slud (1994). pmvnorm
uses a random seed in its algorithm.
To smooth out the randomness, pmvnorm
is called niter
times.
The roundDown
function is used in conjunction with the dig
argument
to insure that any rounding of the (negative) critical values will be done conservatively to control
the familywise type I error at the desired level.
critEA3 
Equal Allocation 3 procedure's critical value for all three test statistics 
sigEA3 
twosided nominal significance level corresponding to 
critPA2A 
Proportional Allocation 2 procedure's critical value for the overall A statistic 
sigPA2A 
twosided nominal significance level corresponding to 
critPA2ab 
Proportional Allocation 2 procedure's critical value for the simple AB statistic 
sigPA2ab 
twosided nominal significance level corresponding to 
critEA2 
Equal Allocation 2 procedure's critical value for the simple A and AB statistics 
sigEA2 
twosided nominal significance level corresponding to 
Leifer, E.S., Troendle, J.F., Kolecki, A., Follmann, D. Joint testing of overall and simple effect for the twobytwo factorial design. 2020. Submitted.
Lin, DY., Gong, J., Gallo, P., et al. Simultaneous inference on treatment effects in survival studies with factorial designs. Biometrics. 2016; 72: 10781085.
Slud, E.V. Analysis of factorial survival experiments. Biometrics. 1994; 50: 2538.
roundDown
. eventProb
, lgrkPower
, strLgrkPower
, pmvnorm
1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 61  # Example 1: Compute the nominal significance levels for rejection using
# the asymptotic correlations derived in Slud (1994)
corAa < 1/sqrt(2)
corAab < 1/sqrt(2)
coraab < 1/2
crit2x2(corAa, corAab, coraab, dig = 2, alpha = 0.05, niter = 5)
# critEA3
# [1] 2.32
# sigEA3
# [1] 0.02034088
# critPA2A
# [1] 2.13
# sigPA2A
# [1] 0.03317161
# critPA2ab
# [1] 2.24
# sigPA2ab
# [1] 0.02509092
# critEA2
# [1] 2.22
# sigEA2
# [1] 0.02641877
# Example 2: Compute the nominal critical values and significance levels for rejection
# using the estimated correlations for simdat.
corAa < 0.6123399
corAab < 0.5675396
coraab < 0.4642737
crit2x2(corAa, corAab, coraab, dig = 2, alpha = 0.05, niter = 5)
# $critEA3
# [1] 2.34
# $critPA2A
# [1] 2.13
# $sigPA2A
# [1] 0.03317161
# $critPA2ab
# [1] 2.3
# $sigPA2ab
# [1] 0.02144822
#
# $sigEA3
# [1] 0.01928374
# $critEA2
# [1] 2.22
# $sigEA2
# [1] 0.02641877

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