View source: R/gsSurvCalendar.R
gsSurvCalendar | R Documentation |
Time-to-event endpoint design with calendar timing of analyses
gsSurvCalendar(
test.type = 4,
alpha = 0.025,
sided = 1,
beta = 0.1,
astar = 0,
sfu = gsDesign::sfHSD,
sfupar = -4,
sfl = gsDesign::sfHSD,
sflpar = -2,
calendarTime = c(12, 24, 36),
spending = c("information", "calendar"),
lambdaC = log(2)/6,
hr = 0.6,
hr0 = 1,
eta = 0,
etaE = NULL,
gamma = 1,
R = 12,
S = NULL,
minfup = 18,
ratio = 1,
r = 18,
tol = 1e-06
)
test.type |
Test type. See |
alpha |
Type I error rate. Default is 0.025 since 1-sided testing is default. |
sided |
|
beta |
Type II error rate. Default is 0.10
(90% power); |
astar |
Normally not specified. If |
sfu |
A spending function or a character string
indicating a boundary type (that is, |
sfupar |
Real value, default is |
sfl |
Specifies the spending function for lower
boundary crossing probabilities when asymmetric,
two-sided testing is performed
( |
sflpar |
Real value, default is |
calendarTime |
Vector of increasing positive numbers with calendar times of analyses. Time 0 is start of randomization. |
spending |
Select between calendar-based spending and information-based spending. |
lambdaC |
Scalar, vector or matrix of event hazard rates for the control group; rows represent time periods while columns represent strata; a vector implies a single stratum. |
hr |
Hazard ratio (experimental/control) under the alternate hypothesis (scalar). |
hr0 |
Hazard ratio (experimental/control) under the null hypothesis (scalar). |
eta |
Scalar, vector or matrix of dropout hazard rates for the control group; rows represent time periods while columns represent strata; if entered as a scalar, rate is constant across strata and time periods; if entered as a vector, rates are constant across strata. |
etaE |
Matrix dropout hazard rates for the experimental
group specified in like form as |
gamma |
A scalar, vector or matrix of rates of entry by time period (rows) and strata (columns); if entered as a scalar, rate is constant across strata and time periods; if entered as a vector, rates are constant across strata. |
R |
A scalar or vector of durations of time periods for
recruitment rates specified in rows of |
S |
A scalar or vector of durations of piecewise constant
event rates specified in rows of |
minfup |
A non-negative scalar less than the maximum value
in |
ratio |
Randomization ratio of experimental treatment divided by control; normally a scalar, but may be a vector with length equal to number of strata. |
r |
Integer value (>= 1 and <= 80) controlling the number of numerical
integration grid points. Default is 18, as recommended by Jennison and
Turnbull (2000). Grid points are spread out in the tails for accurate
probability calculations. Larger values provide more grid points and greater
accuracy but slow down computation. Jennison and Turnbull (p. 350) note an
accuracy of |
tol |
Tolerance for error passed to the |
# First example: while timing is calendar-based, spending is event-based
x <- gsSurvCalendar() %>% toInteger()
gsBoundSummary(x)
# Second example: both timing and spending are calendar-based
# This results in less spending at interims and leaves more for final analysis
y <- gsSurvCalendar(spending = "calendar") %>% toInteger()
gsBoundSummary(y)
# Note that calendar timing for spending relates to planned timing for y
# rather than timing in y after toInteger() conversion
# Values plugged into spending function for calendar time
y$usTime
# Actual calendar fraction from design after toInteger() conversion
y$T / max(y$T)
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