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. See gsSurv
.
Type I error rate. Default is 0.025 since 1-sided testing is default.
1
for 1-sided testing, 2
for 2-sided testing.
Type II error rate. Default is 0.10
(90% power); NULL
if power is to be computed based on
other input values.
Normally not specified. If test.type = 5
or 6
, astar
specifies the total probability
of crossing a lower bound at all analyses combined. This
will be changed to 1 - alpha
when default value of
0
is used. Since this is the expected usage,
normally astar
is not specified by the user.
A spending function or a character string
indicating a boundary type (that is, "WT"
for
Wang-Tsiatis bounds, "OF"
for O'Brien-Fleming bounds and
"Pocock"
for Pocock bounds). For one-sided and symmetric
two-sided testing is used to completely specify spending
(test.type = 1
, 2
), sfu
. The default value is
sfHSD
which is a Hwang-Shih-DeCani spending function.
Real value, default is -4
which is an
O'Brien-Fleming-like conservative bound when used with the
default Hwang-Shih-DeCani spending function. This is a
real-vector for many spending functions. The parameter
sfupar
specifies any parameters needed for the spending
function specified by sfu
; this will be ignored for
spending functions (sfLDOF
, sfLDPocock
)
or bound types ("OF"
, "Pocock"
)
that do not require parameters.
Specifies the spending function for lower
boundary crossing probabilities when asymmetric,
two-sided testing is performed
(test.type = 3
, 4
, 5
, or 6
).
Unlike the upper bound,
only spending functions are used to specify the lower bound.
The default value is sfHSD
which is a
Hwang-Shih-DeCani spending function. The parameter
sfl
is ignored for one-sided testing
(test.type = 1
) or symmetric 2-sided testing
(test.type = 2
).
Real value, default is -2
, which, with the
default Hwang-Shih-DeCani spending function, specifies a
less conservative spending rate than the default for the
upper bound.
Vector of increasing positive numbers with calendar times of analyses. Time 0 is start of randomization.
Select between calendar-based spending and information-based spending.
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.
Hazard ratio (experimental/control) under the alternate hypothesis (scalar).
Hazard ratio (experimental/control) under the null hypothesis (scalar).
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.
Matrix dropout hazard rates for the experimental
group specified in like form as eta
; if NULL
,
this is set equal to eta
.
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.
A scalar or vector of durations of time periods for
recruitment rates specified in rows of gamma
. Length is the
same as number of rows in gamma
. Note that when variable
enrollment duration is specified (input T = NULL
), the final
enrollment period is extended as long as needed.
A scalar or vector of durations of piecewise constant
event rates specified in rows of lambda
, eta
and etaE
;
this is NULL
if there is a single event rate per stratum
(exponential failure) or length of the number of rows in lambda
minus 1, otherwise.
A non-negative scalar less than the maximum value
in calendarTime
. Enrollment will be cut off at the
difference between the maximum value in calendarTime
and minfup
.
Randomization ratio of experimental treatment divided by control; normally a scalar, but may be a vector with length equal to number of strata.
Integer value controlling grid for numerical
integration as in Jennison and Turnbull (2000); default is 18,
range is 1 to 80. Larger values provide larger number of grid
points and greater accuracy. Normally r
will not be changed by
the user.
Tolerance for error passed to the gsDesign
function.
# 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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