fac2x2analyze: Significance testing for the Proportional Allocation 2, Equal...
In factorial2x2: Design and Analysis of a 2x2 Factorial Trial

Description Usage Arguments Details Value Author(s) References Examples

Performs significance testing for the Proportional Allocation 2, Equal Allocation 3, Equal Allocation 2 procedures. Also reports the hazard ratios, 95% confidence intervals, p-values, nominal significance levels, and correlations for the overall and simple test statistics.

1	fac2x2analyze(time, event, indA, indB, covmat, alpha, dig = 2, niter = 5)

`time`	follow-up times
`event`	event indicators (0/1)
`indA`	treatment A indicators (0/1)
`indB`	treatment B indicators (0/1)
`covmat`	covariate matrix, must be non-NULL. Factor variables MUST use 0/1 dummy variables
`alpha`	two-sided familywise significance level
`dig`	number of decimal places to which we `roundDown` the critical value
`niter`	number of interations passed to `crit2x2` function call

For each of the three multiple testing procedures, the critical values for the overall A (respectively, simple A) logrank statistics may be slightly different from the overall B (respectively, simple B) logrank statistics. This is due to their slightly different correlations with each other (i.e., correlation between overall A and simple A, respectively, overall B and simple B, statistics) as well as with the simple AB statistic.

`loghrAoverall`	overall A log hazard ratio
`seAoverall`	standard error of the overall A log hazard ratio
`ZstatAoverall`	Z-statistic for the overall A log hazard ratio
`pvalAoverall`	two-sided p-value for the overall hazard ratio
`hrAoverall`	overall A hazard ratio
`ciAoverall`	95% confidence interval for the overall A hazard ratio
`loghrAsimple`	simple A log hazard ratio
`seAsimple`	standard error of the simple A log hazard ratio
`ZstatAsimple`	Z-statistic for the simple A log hazard ratio
`pvalAsimple`	two-sided p-value for the simple A hazard ratio
`hrAsimple`	simple A hazard ratio
`ciAsimple`	95% confidence interval for the simple A hazard ratio
`loghrABsimple`	simple AB log hazard ratio
`seABsimple`	standard error of the simple AB log hazard ratio
`ZstatABsimple`	Z-statistic for the simple AB log hazard ratio
`pvalABsimple`	two-sided p-value for the simple AB hazard ratio
`hrABsimple`	simple AB hazard ratio
`ciABsimple`	95% confidence interval for the simple AB hazard ratio
`critEA3_A`	Equal Allocation 3's critical value for the overall A simple A, and simple AB hypotheses
`sigEA3_A`	Equal Allocation 3's p-value rejection criterion for the overall A, simple A, and simple AB hypotheses
`resultEA3_A`	Equal Allocation 3's accept/reject decisions for the overall A, simple A, and simple AB hypotheses
`critPA2overallA`	Proportional Allocation 2's critical value for the overall A statistic
`sigPA2overallA`	Proportional Allocation 2's p-value rejection criterion for the overall A hypothesis
`critPA2simpleAB`	Proportional Allocation 2's critical value for the simple AB hypothesis
`sigPA2simpleAB`	Proportional Allocation 2 procedure's p-value rejection criterion for the simple AB hypothesis
`resultPA2_A`	Proportional Allocation 2 procedure's accept/reject decisions for the overall A and simple A hypotheses
`critEA2_A`	Equal Allocation 2 procedure's critical value for the simple A and simple AB hypotheses
`sigEA2_A`	Equal Allocation 2 procedure's p-value rejection criterion for the simple A and simple AB hypotheses
`resultEA2_A`	Equal Allocation 2 procedure's accept/reject decisions for the simple A and simple AB hypotheses
`corAa`	correlation between the overall A and simple A logrank statistics
`corAab`	correlation between the overall A and simple AB logrank statistics
`coraab`	correlation between the simple A and simple AB logrank statistics

Eric Leifer, James Troendle

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.

 # First load the simulated data variables. The "simdataSub" file is
 # a 100-by-9 matrix which is loaded with the factorial2x2 package.
 time <- simdataSub[, "time"]
 event <- simdataSub[, "event"]
 indA <- simdataSub[, "indA"]
 indB <- simdataSub[, "indB"]
 covmat <- simdataSub[, 6:10]
 fac2x2analyze(time, event, indA, indB, covmat, alpha = 0.05, niter = 5)
#  $loghrA
# [1] 0.05613844

# $seA
# [1] 0.4531521

# $ZstatA
# [1] 0.1238843

# $pvalA
# [1] 0.9014069

# $hrA
# [1] 1.057744

# $ciA
# [1] 0.4351608 2.5710556

# $loghra
# [1] 0.1987329

# $sea
# [1] 0.6805458

# $Zstata
# [1] 0.2920198

# $pvala
# [1] 0.7702714

# $hra
# [1] 1.219856

# $cia
# [1] 0.3213781 4.6302116

# $loghrab
# [1] 0.2864932

# $seab
# [1] 0.6762458

# $Zstatab
# [1] 0.4236525

# $pvalab
# [1] 0.6718193

# $hrab
# [1] 1.331749

# $ciab
# [1] 0.3538265 5.0125010

# $critPA2A
# [1] -2.129

# $sigPA2A
# [1] 0.03325426

# $critPA2ab
# [1] -2.299

# $sigPA2ab
# [1] 0.02150494

# $result23
# [1] "accept overall A" "accept simple AB"

# $critEA3
# [1] -2.338

# $sigEA3
# [1] 0.01938725

# $result13
# [1] "accept overall A" "accept simple A"  "accept simple AB"

# $critEA2
# [1] -2.216

# $sigEA2
# [1] 0.0266915

# $result12
# [1] "accept simple A"  "accept simple AB"

# $corAa
# [1] 0.6123399

# $corAab
# [1] 0.5675396

# $coraab
# [1] 0.4642737