Performs interim or sequential analyses of simulated two-group
time-to-event data at one or more analysis times ("looks"). Each look is
defined by a target cumulative number of events (information-based timing),
by a calendar time (calendar-based timing), or by a calendar cutoff given
separately for every simulated trial (for example by
cutoff_fast).
At every look the data are administratively censored at the corresponding
calendar cutoff, and the requested statistics are computed for each
simulated trial by reusing survdiff_fast,
coxph_fast, rmst_fast, survfit_fast,
maxcombo_fast, ahsw_fast,
milestone_fast, rmw_fast,
ahr_fast, medsurv_fast,
wkm_fast, and wmst_fast. Optionally the same
statistics are also reported within each subgroup. The censoring and time
sorting are handled by a C++ backend, and the analysis cores are called with
presorted = TRUE, so each look avoids a redundant sort.
Usage
analysis_fast(
data,
control,
event.looks = NULL,
time.looks = NULL,
stat = "logrank",
tau = NULL,
t.eval = NULL,
conf.level = 0.95,
side = 2,
by.subgroup = FALSE,
weight = c("logrank", "fh", "mwlrt", "gehan", "tarone-ware"),
rho = 0,
gamma = 0,
t_star = NULL,
strata = NULL,
ms.method = c("wald", "loglog", "mover"),
s_star = 0.5,
mc.rho = c(0, 0, 1, 1),
mc.gamma = c(0, 1, 0, 1),
abseps = 1e-05,
maxpts = 25000,
medsurv.method = c("km", "nph"),
medsurv.bw = NULL,
wkm.weight = c("PF", "sqrtPF", "constant"),
wmst.tau1 = 0,
wmst.tau2 = NULL,
cutoff.looks = NULL,
mc.alpha = NULL
)Arguments
- data
A data frame from
simdata_fastfor a two-group trial, containing at leastsim,group,accrual_time,tte, andevent.- control
A scalar value indicating which level of
grouprepresents the control group.- event.looks
A vector of positive whole numbers, the target cumulative event counts, one per look. Mutually exclusive with
time.looksandcutoff.looks.- time.looks
A numeric vector of calendar times, one per look. Mutually exclusive with
event.looksandcutoff.looks.- stat
A character vector naming the statistics to compute. Any subset of
"logrank","coxph","rmst","km","maxcombo","ahsw","milestone","rmw","ahr","medsurv","wkm", and"wmst". Defaults to"logrank".- tau
A single positive numeric value, the restriction horizon for
"rmst", the truncation time for"ahsw"and"ahr", and the milestone timepoint for"milestone". Required only when"rmst","ahsw","milestone", or"ahr"is requested. No warning is given when it exceeds the largest observed time of a group at a look (for example at an early interim analysis); the Kaplan-Meier curve of that group is then carried forward flat, whereas the stand-alone functions such asrmst_fastgive a warning.- t.eval
A single positive numeric value, the landmark time for
"km". Required only when"km"is requested. As fortau, no warning is given when it exceeds the follow-up of a group at a look.- conf.level
A single numeric value in (0, 1), the confidence level of all the confidence-interval columns (and of the
"mover"milestone statistic). Defaults to 0.95.- side
An integer, either 1 or 2. When
side = 2(default), two-sided p-values2 pnorm(-|z|)are reported for log-rank, Cox, and RMST, and the two-sided max-combo test is used. Whenside = 1, the one-sided p-value in the direction of treatment benefit is reported for each of those statistics, and the one-sided max-combo test is used. The test statistics themselves are always reported with their natural sign, so the choice ofsideaffects only the p-value columns, exceptmaxcombo.statandrmw.stat, whose definition depends onside(see Details). For log-rank and Cox the benefit direction is a negative Z (the one-sided p-value is the lower tailpnorm(z)); for RMST it is a positive Z (the upper tailpnorm(-z)). The other statistics follow the benefit directions given in Details. For group-sequential boundary comparisons (for example with gsDesign or rpact), align the sign of the reported Z with the boundary convention before comparing.- by.subgroup
A logical value. When
TRUE, the analysis is also reported within each subgroup level, and the output gains apopulationcolumn (long form). WhenFALSE(default), only the whole-population analysis is returned and nopopulationcolumn is added. Requires at least one subgroup column indata.- weight
A character string naming the weight scheme for the
"logrank"statistic."logrank"(default) is the ordinary unweighted test."fh"is the Fleming-Harrington G(rho, gamma) test,"mwlrt"is the modestly-weighted log-rank test (requirest_star),"gehan"is Gehan-Breslow, and"tarone-ware"is Tarone-Ware. Seesurvdiff_fast.- rho
A numeric Fleming-Harrington first parameter, used only when
weight = "fh". Defaults to 0.- gamma
A numeric Fleming-Harrington second parameter, used only when
weight = "fh". Defaults to 0.- t_star
A single non-negative numeric value, the timepoint of the modestly-weighted log-rank test. Required only when
weight = "mwlrt". The weight is capped at the reciprocal of the pooled Kaplan-Meier value just beforet_star, and is not capped when that value is 0 (seesurvdiff_fast).- strata
An optional character vector naming one or more subgroup columns of
datato use as the stratification variable for the"logrank"and"coxph"statistics.NULL(default) gives the unstratified analyses. When several columns are named their interaction defines the strata. The columns must not contain missing values. Stratification applies only to the"logrank"and"coxph"statistics; the other statistics ignore it.- ms.method
A character string naming the inference method for the
"milestone"statistic, one of"wald"(default),"loglog", or"mover". Seemilestone_fast; the"mover"statistic and its p-value depend onconf.level.- s_star
A single numeric value in (0, 1], the survival-probability threshold of the modestly-weighted component of the
"rmw"statistic. The weight is capped at1 / s_star. Defaults to 0.5. Seermw_fast.- mc.rho
A numeric vector of Fleming-Harrington first parameters for the
"maxcombo"statistic, one per component weight. Defaults toc(0, 0, 1, 1).- mc.gamma
A numeric vector of Fleming-Harrington second parameters for the
"maxcombo"statistic, aligned withmc.rho. Defaults toc(0, 1, 0, 1).- abseps
A single positive numeric value, the absolute error tolerance passed to the multivariate normal integration of the
"maxcombo"p-value. Defaults to 1e-5. With the default weights the quasi-Monte-Carlo integration usually stops atmaxptsbefore reaching it (seemaxcombo_fast).- maxpts
A single positive integer, the maximum number of function evaluations for the quasi-Monte-Carlo integration used by the
"maxcombo"p-value with four or more weights or with a two-sided test (see Details). Defaults to 25000. Larger values reduce the integration error at a higher computing cost.- medsurv.method
A character string naming the variance method for the
"medsurv"statistic, one of"km"(default) or"nph". Seemedsurv_fast.- medsurv.bw
An optional single positive numeric value, the kernel bandwidth for the median hazard used by
medsurv.method = "km", applied to both groups.NULL(default) uses a Silverman type default computed per group from the cut data, matchingmedsurv_fast.- wkm.weight
A character string naming the weight for the
"wkm"statistic, one of"PF"(default, Pepe-Fleming combined censoring weight),"sqrtPF", or"constant". Seewkm_fast, which explains why the"PF"weight is recommended when follow-up differs between the groups.- wmst.tau1
A single non-negative numeric value, the lower window limit for the
"wmst"statistic. Defaults to 0.- wmst.tau2
A single positive numeric value, the upper window limit for the
"wmst"statistic, which must exceedwmst.tau1.NULL(default) falls back totau. Required (through either argument) only when"wmst"is requested. As fortau, no warning is given when it exceeds the follow-up of a group at a look.- cutoff.looks
A numeric matrix of per-simulation calendar cutoffs with one row per simulated trial and one column per look, such as the output of
cutoff_fast. When the matrix has row names they are matched to the values ofdata$sim; otherwise the rows are taken in the order of the sorted distinct values ofdata$sim. A numeric vector is treated as a single look.NAorInfmarks a look that is not reached. Thelook.valuecolumn of the output is taken from the"look.value"attribute of the matrix when it is present, and isNAotherwise. Mutually exclusive withevent.looksandtime.looks.- mc.alpha
An optional numeric vector of nominal levels in (0, 1), one per look or a single value for all looks, at which the
"maxcombo"p-value is compared. When supplied, the p-value is computed by integration only when the Bonferroni bounds do not decide the comparison (see Details), and the output gains a logical columnmaxcombo.p.exact.NULL(default) computes every p-value.
Value
A data frame. When by.subgroup = FALSE, it has
nsim * length(looks) rows. When by.subgroup = TRUE, it has
nsim * length(looks) * (1 + total subgroup levels) rows and an
extra population column placed after look.value. The common
columns are sim, look (1-based look index),
look.value (the requested event count or calendar time, or the
"look.value" attribute of cutoff.looks), optionally population, cutoff (the
calendar time used, NA when the look was not reached),
reached, n.enrolled, n.event, n.dropout (the
number of enrolled subjects whose dropout occurred on or before the cutoff)
and n.pipeline (n.enrolled - n.event - n.dropout, the
subjects still in follow-up at the cutoff), followed by the
columns of the requested statistics. A statistic that cannot be computed
for a row (no events, or an empty group) is NA. The statistic
columns are logrank.z, logrank.chisq, and logrank.p
for "logrank"; cox.coef, cox.hr, cox.se,
cox.z, cox.p, cox.lower, and cox.upper for
"coxph"; rmst.ctrl, rmst.trt, rmst.diff,
rmst.diff.lower, rmst.diff.upper, rmst.z, and
rmst.p for "rmst"; km.surv.ctrl and
km.surv.trt for "km"; maxcombo.stat and
maxcombo.p (and maxcombo.p.exact when mc.alpha is
supplied) for "maxcombo"; and ahsw.ah.ctrl,
ahsw.ah.trt, ahsw.rah, ahsw.rah.lower,
ahsw.rah.upper, ahsw.p.rah, ahsw.dah,
ahsw.dah.lower, ahsw.dah.upper, and ahsw.p.dah for
"ahsw"; milestone.surv.ctrl, milestone.surv.trt,
milestone.diff, milestone.diff.lower,
milestone.diff.upper, milestone.z, and milestone.p
for "milestone"; rmw.stat and rmw.p for "rmw";
and ahr.ahr, ahr.theta.ctrl, ahr.theta.trt,
ahr.z, and ahr.p for "ahr";
medsurv.ctrl, medsurv.trt, medsurv.diff,
medsurv.diff.lower, medsurv.diff.upper, medsurv.z, and
medsurv.p for "medsurv"; wkm.wdiff, wkm.lower,
wkm.upper, wkm.z, and wkm.p for "wkm"; and
wmst.ctrl, wmst.trt, wmst.diff,
wmst.diff.lower, wmst.diff.upper, wmst.z, and
wmst.p for "wmst". The Z columns
logrank.z, cox.z, and
rmst.z carry the natural sign of each test, and the p-value columns
follow side.
Details
The input data is the data frame returned by
simdata_fast for a two-group trial. The columns sim,
group, accrual_time, tte, and event are
required and must not contain missing values. The tte column must be
numeric and non-negative, event must be numeric or logical and coded
as 0 or 1, and group must have exactly two distinct values, one of
which is control. For a
multi-arm trial, subset the data to two arms first or use
pairwise_fast.
For a look at calendar time cutoff, each subject with accrual time
a contributes only if enrolled by then (a <= cutoff). The
observed time at the look is min(tte, cutoff - a) and the observed
event indicator is the original event when the natural event or
dropout occurred on or before cutoff (a + tte <= cutoff), and
zero otherwise (administrative censoring at the look).
When event.looks is supplied, the calendar cutoff for a target of
d events is the calendar time of the d-th event in that
simulated trial, counted over the whole trial population. If a simulation
contains fewer than d events, the target is never reached: the full
data are used, so the statistics are those of the final data, reached
is FALSE, and cutoff is NA. When several events occur
at the calendar time of the d-th event, all of them are included, so
the number of events analyzed can exceed the target. When time.looks is supplied, the cutoff is the specified
calendar time and reached is always TRUE. In both cases the
cutoff is determined once on the whole population and then used for the
overall analysis and for every subgroup analysis at that look.
When cutoff.looks is supplied, the calendar cutoff of each look is
given separately for every simulated trial, typically by
cutoff_fast. This analyzes an endpoint at cutoffs determined
by another endpoint (for example overall survival at the event-driven looks
of progression-free survival), by a subset of the subjects, or by a
combination of event and calendar-time rules. A missing or infinite entry
marks a look whose trigger was not met in that simulation: as for an
unreached event.looks target, the full data are used, reached
is FALSE, and cutoff is NA. In contrast,
pairwise_fast reports NA counts and statistics for an
unreached look, because its contrasts share one cutoff and have no common
final data.
Exactly one of event.looks, time.looks, and
cutoff.looks must be supplied.
The statistics are selected with stat, which may name one or more of
"logrank", "coxph", "rmst", "km",
"maxcombo", "ahsw", "milestone", "rmw",
"ahr", "medsurv", "wkm", and "wmst". The
references for each statistic are given on the help page of the
corresponding function listed under See Also.
The "logrank" statistic is configurable. By default it is the
ordinary unweighted, unstratified two-group log-rank test and reproduces the
behavior of earlier versions of this function exactly. A non-default
weight selects a weighted log-rank test (Fleming-Harrington,
modestly-weighted, Gehan-Breslow, or Tarone-Ware) for non-proportional
hazards, and a non-NULL strata selects the stratified test,
summing the per-stratum contributions. The two options combine to give the
stratified weighted log-rank test. Whatever configuration is chosen, the
result is written to the same logrank.z, logrank.chisq, and
logrank.p columns; the unweighted unstratified case is the K = 1
single-stratum degenerate form of the general statistic. The columns
rho, gamma, and t_star parametrize weight, and
strata names one or more subgroup columns of data used as the
stratification variable. The stratification is determined on the whole cut
data, independently of the population marginalization, so a stratified
overall analysis is the canonical primary test. When strata names
the subgroup column that defines a subgroup population, the stratum is
constant within that population and the stratified test there reduces to
the ordinary one. Several columns define their combinations as strata.
The same strata also stratify the
"coxph" statistic, which is then the stratified Pike-Halley estimate
of coxph_fast (a common hazard ratio with a separate baseline
hazard in each stratum).
The "maxcombo" statistic is the max-combo test of mc.rho and
mc.gamma Fleming-Harrington weights. Its maxcombo.stat is the
most extreme component (min of the component Z-scores when
side = 1, so that a negative value favors treatment, and the maximum
absolute component when side = 2), and maxcombo.p is the joint
multivariate-normal p-value, which already follows side. As in
maxcombo_fast, the p-value of a one-sided test with two or
three weights is computed by the deterministic TVPACK algorithm, and
otherwise by the randomized quasi-Monte-Carlo GenzBretz algorithm of
mvtnorm, which uses R's random-number generator; call set.seed()
beforehand for p-values that are reproducible to the last digit. The
correlation matrix of the default weights is singular, and the GenzBretz
p-values then carry an integration error of the order of 1e-4 with the
default maxpts (see maxcombo_fast).
The multivariate normal integration dominates the computing time of the
"maxcombo" statistic in a large simulation. When only the decision
at a nominal level matters, mc.alpha avoids most of these
integrals. With K weights and p_min the smallest
component p-value (pnorm(m) for side = 1 and
2 * pnorm(-m) for side = 2, where m is
maxcombo.stat), the max-combo p-value satisfies
p_min <= p <= min(1, K * p_min) (the Bonferroni inequality). When
p_min > mc.alpha, p_min is reported; when
K * p_min <= mc.alpha, K * p_min is reported; otherwise the
p-value is computed by integration. A bound is reported only when it is on
the same side of mc.alpha as the exact p-value, so the rejection
decisions maxcombo.p <= mc.alpha (as in simsummary_fast
with alpha = mc.alpha) that the bounds settle are those of the exact
p-values, and the reported value is a bound unless
maxcombo.p.exact is TRUE. The remaining p-values are
integrated as without mc.alpha. When the randomized GenzBretz
algorithm is used, the random numbers of each integral change with the
number of integrals, so near the level a decision can differ from that of
a run without mc.alpha, as it can between two runs with different
seeds.
Use exact p-values (the default mc.alpha = NULL) when the p-values
themselves, or decisions at other levels such as Bonferroni-adjusted levels,
are needed.
The "ahsw" statistic is the average hazard with survival weight of Uno
and Horiguchi on the window from 0 to tau. It reports the per-group
average hazards, the ratio (RAH) and difference (DAH) contrasts with their
confidence intervals, and p-values for both contrasts that follow
side, matching ahsw_fast. As there, a group without
an event up to tau has an average hazard of 0, and the contrasts
are then NA. The benefit direction is a
ratio below 1 (a negative log ratio) and a negative difference, so the
one-sided p-values are lower-tail probabilities.
The "milestone" statistic compares the Kaplan-Meier survival
probabilities of the two groups at the milestone timepoint tau. It
reports the per-group survival, the difference (treatment minus control) with
its confidence interval, the test statistic, and the p-value. The inference
method is selected with ms.method ("wald", "loglog", or
"mover"), matching milestone_fast. The benefit direction
is a positive difference (higher treatment survival). For "wald" and
"mover" a positive Z favors treatment; for "loglog" the
statistic is the difference of the complementary log-log transforms, so a
negative Z favors treatment, and the one-sided p-value is the lower tail as
in milestone_fast. The "mover" statistic is obtained
by inverting the MOVER interval at conf.level, so its p-value depends
on conf.level, consistently with the interval (see
milestone_fast). When the Kaplan-Meier estimate of a group
is 0 or 1 at tau, its one-sample interval degenerates to the
estimate, so the interval of the difference is still reported, while the
"loglog" statistic is NA.
The "rmw" statistic is the robust modestly-weighted log-rank test of
Magirr and Öhrn, the maximum of the standard log-rank component
and a single modestly-weighted component with survival-threshold
s_star. Its
rmw.stat is the most extreme standardized component (the minimum when
side = 1, so a negative value favors treatment, and the maximum
absolute component when side = 2), and rmw.p is the joint
bivariate-normal p-value, which already follows side, matching
rmw_fast.
The "ahr" statistic is the Kalbfleisch-Prentice average hazard ratio
over the window from 0 to tau. It reports the average hazard ratio
(treatment relative to control), the two group shares of the total hazard,
the test statistic on the group-share scale, and the p-value. The benefit
direction is an average hazard ratio below 1 (a negative Z), as in
ahr_fast.
The "medsurv" statistic compares the non-parametric Kaplan-Meier
median survival times of the two groups. It reports the per-group medians,
their difference (treatment minus control) with a plain-scale confidence
interval, the Wald statistic, and the p-value. The variance method is
selected with medsurv.method ("km" or "nph"), matching
medsurv_fast; medsurv.bw optionally overrides the kernel
bandwidth used by medsurv.method = "km". The benefit direction is a
positive difference (a longer median under treatment), so a positive Z favors
treatment. When the Kaplan-Meier estimate of a group stays at 0.5 up to an
infinite observed time, as for subjects with tte = Inf at a look
that is not reached, the median of that group is not defined and the
"medsurv" columns that depend on it are NA.
The "wkm" statistic is the weighted Kaplan-Meier (Pepe-Fleming) test.
It reports the weighted integrated survival difference (treatment minus
control) with its confidence interval, the Wald statistic, and the p-value.
The weight is selected with wkm.weight ("PF", "sqrtPF",
or "constant"), matching wkm_fast. The benefit direction
is a positive weighted difference (a positive Z favors treatment). The
integral runs over the observed range, so all the "wkm" columns are
NA when an observed time is infinite, as for a subject without a
finite event or dropout time (tte = Inf) at a look that is not
reached.
The "wmst" statistic compares the window mean survival time, the area
under the Kaplan-Meier curve between wmst.tau1 and wmst.tau2.
It reports the per-group WMST values, their difference (treatment minus
control) with its confidence interval, the Wald statistic, and the p-value,
matching wmst_fast. With wmst.tau1 = 0 the window mean
survival time is the restricted mean survival time at wmst.tau2. The
benefit direction is a positive difference (a positive Z favors treatment).
When by.subgroup = TRUE, the output is given in long form with a
population column. Each (sim, look) produces one row for the
whole trial (population = "overall") plus one row per subgroup level
of each subgroup factor in data. Subgroup factors are the columns
named subgroup or subgroup1, subgroup2, and so on. Each
factor is marginalized separately, so a factor with levels 1 and 2 yields
the populations subgroup_1 and subgroup_2 (or
subgroup1_1, subgroup1_2, and so on for numbered factors).
The look cutoff is always determined on the whole population, so subgroup
rows at a given look share the same cutoff, reached, and
look.value; their n.enrolled and n.event are the counts
within that subgroup. When by.subgroup = FALSE (default) the output
has no population column and one row per (sim, look), matching
the whole-population analysis.
Examples
df <- simdata_fast(
nsim = 50,
n = c(150, 150),
a.time = c(0, 12),
a.rate = 300 / 12,
e.hazard = list(list(0.10, 0.07), 0.05),
prevalence = c(0.5, 0.5),
seed = 1
)
# Whole-population analysis at two event-based looks
res1 <- analysis_fast(df, control = 1, event.looks = c(80, 140))
head(res1)
#> sim look look.value cutoff reached n.enrolled n.event n.dropout n.pipeline
#> 1 1 1 80 11.76464 TRUE 291 80 0 211
#> 2 1 2 140 16.08387 TRUE 300 140 0 160
#> 3 2 1 80 11.40147 TRUE 285 80 0 205
#> 4 2 2 140 16.62229 TRUE 300 140 0 160
#> 5 3 1 80 11.48259 TRUE 286 80 0 206
#> 6 3 2 140 16.06186 TRUE 300 140 0 160
#> logrank.z logrank.chisq logrank.p
#> 1 -1.171526 1.372472 2.413876e-01
#> 2 -1.555453 2.419433 1.198383e-01
#> 3 -2.780165 7.729316 5.433132e-03
#> 4 -4.862092 23.639938 1.161516e-06
#> 5 -3.631606 13.188562 2.816629e-04
#> 6 -5.043861 25.440534 4.562306e-07
# Stratified log-rank and stratified Cox on the subgroup factor
res2 <- analysis_fast(df, control = 1, time.looks = 24,
stat = c("logrank", "coxph"), strata = "subgroup")
head(res2)
#> sim look look.value cutoff reached n.enrolled n.event n.dropout n.pipeline
#> 1 1 1 24 24 TRUE 300 208 0 92
#> 2 2 1 24 24 TRUE 300 192 0 108
#> 3 3 1 24 24 TRUE 300 194 0 106
#> 4 4 1 24 24 TRUE 300 206 0 94
#> 5 5 1 24 24 TRUE 300 194 0 106
#> 6 6 1 24 24 TRUE 300 203 0 97
#> logrank.z logrank.chisq logrank.p cox.coef cox.hr cox.se cox.z
#> 1 -3.059687 9.361684 2.215684e-03 -0.4286952 0.6513584 0.1410554 -3.039197
#> 2 -5.125810 26.273925 2.962617e-07 -0.7470592 0.4737577 0.1487554 -5.022065
#> 3 -5.532218 30.605431 3.162074e-08 -0.8010695 0.4488486 0.1482634 -5.403014
#> 4 -4.736340 22.432915 2.176126e-06 -0.6711369 0.5111272 0.1438130 -4.666733
#> 5 -3.741408 13.998135 1.829920e-04 -0.5408537 0.5822510 0.1461903 -3.699654
#> 6 -1.584396 2.510311 1.131036e-01 -0.2229715 0.8001377 0.1410111 -1.581234
#> cox.p cox.lower cox.upper
#> 1 2.372098e-03 0.4940301 0.8587893
#> 2 5.111897e-07 0.3539448 0.6341283
#> 3 6.553017e-08 0.3356587 0.6002083
#> 4 3.060273e-06 0.3855805 0.6775523
#> 5 2.158937e-04 0.4371926 0.7754391
#> 6 1.138246e-01 0.6069262 1.0548570
# Fleming-Harrington G(0, 1) weighted log-rank for delayed effects
res3 <- analysis_fast(df, control = 1, time.looks = 24,
stat = "logrank", weight = "fh", rho = 0, gamma = 1)
head(res3)
#> sim look look.value cutoff reached n.enrolled n.event n.dropout n.pipeline
#> 1 1 1 24 24 TRUE 300 208 0 92
#> 2 2 1 24 24 TRUE 300 192 0 108
#> 3 3 1 24 24 TRUE 300 194 0 106
#> 4 4 1 24 24 TRUE 300 206 0 94
#> 5 5 1 24 24 TRUE 300 194 0 106
#> 6 6 1 24 24 TRUE 300 203 0 97
#> logrank.z logrank.chisq logrank.p
#> 1 -3.156345 9.962516 1.597596e-03
#> 2 -4.348303 18.907740 1.371949e-05
#> 3 -4.895489 23.965814 9.806151e-07
#> 4 -4.627069 21.409771 3.708763e-06
#> 5 -3.584443 12.848231 3.377984e-04
#> 6 -1.224151 1.498546 2.208952e-01
# Max-combo and AHSW together
res4 <- analysis_fast(df, control = 1, time.looks = 24,
stat = c("maxcombo", "ahsw"), tau = 18)
head(res4)
#> sim look look.value cutoff reached n.enrolled n.event n.dropout n.pipeline
#> 1 1 1 24 24 TRUE 300 208 0 92
#> 2 2 1 24 24 TRUE 300 192 0 108
#> 3 3 1 24 24 TRUE 300 194 0 106
#> 4 4 1 24 24 TRUE 300 206 0 94
#> 5 5 1 24 24 TRUE 300 194 0 106
#> 6 6 1 24 24 TRUE 300 203 0 97
#> maxcombo.stat maxcombo.p ahsw.ah.ctrl ahsw.ah.trt ahsw.rah ahsw.rah.lower
#> 1 3.192382 3.050321e-03 0.07881581 0.05237260 0.6644936 0.5034745
#> 2 5.332615 9.694169e-08 0.08345052 0.04012456 0.4808185 0.3581851
#> 3 5.574249 2.489953e-08 0.09089574 0.03990436 0.4390125 0.3278702
#> 4 4.927248 2.213839e-06 0.09257119 0.04918586 0.5313301 0.4019580
#> 5 3.840689 2.192330e-04 0.08147862 0.04592903 0.5636943 0.4213291
#> 6 1.467686 2.403875e-01 0.07356423 0.06111041 0.8307082 0.6176444
#> ahsw.rah.upper ahsw.p.rah ahsw.dah ahsw.dah.lower ahsw.dah.upper
#> 1 0.8770091 3.890394e-03 -0.02644321 -0.04446913 -0.008417282
#> 2 0.6454384 1.091478e-06 -0.04332597 -0.06151709 -0.025134854
#> 3 0.5878301 3.250633e-08 -0.05099138 -0.06965300 -0.032329748
#> 4 0.7023413 8.920363e-06 -0.04338533 -0.06288035 -0.023890304
#> 5 0.7541639 1.135479e-04 -0.03554959 -0.05370932 -0.017389860
#> 6 1.1172711 2.199664e-01 -0.01245382 -0.03244248 0.007534848
#> ahsw.p.dah
#> 1 4.038005e-03
#> 2 3.040474e-06
#> 3 8.534762e-08
#> 4 1.289882e-05
#> 5 1.246293e-04
#> 6 2.220316e-01
# Milestone survival, robust modestly-weighted, and average hazard ratio
res5 <- analysis_fast(df, control = 1, time.looks = 24,
stat = c("milestone", "rmw", "ahr"), tau = 18,
ms.method = "loglog", s_star = 0.5, side = 1)
head(res5)
#> sim look look.value cutoff reached n.enrolled n.event n.dropout n.pipeline
#> 1 1 1 24 24 TRUE 300 208 0 92
#> 2 2 1 24 24 TRUE 300 192 0 108
#> 3 3 1 24 24 TRUE 300 194 0 106
#> 4 4 1 24 24 TRUE 300 206 0 94
#> 5 5 1 24 24 TRUE 300 194 0 106
#> 6 6 1 24 24 TRUE 300 203 0 97
#> milestone.surv.ctrl milestone.surv.trt milestone.diff milestone.diff.lower
#> 1 0.2235911 0.3877849 0.16419389 0.04833989
#> 2 0.2298256 0.4769207 0.24709510 0.12940737
#> 3 0.1925399 0.4894197 0.29687982 0.18292621
#> 4 0.1795688 0.4098869 0.23031806 0.11556461
#> 5 0.2192132 0.4413683 0.22215515 0.10341390
#> 6 0.2885018 0.3493613 0.06085952 -0.05387747
#> milestone.diff.upper milestone.z milestone.p rmw.stat rmw.p
#> 1 0.2734819 -2.778169 2.733305e-03 -3.221206 8.381633e-04
#> 2 0.3557723 -4.084347 2.210050e-05 -5.332615 7.139961e-08
#> 3 0.4002439 -5.017927 2.611594e-07 -5.574249 1.855481e-08
#> 4 0.3362999 -3.913444 4.549455e-05 -4.764203 1.359804e-06
#> 5 0.3323191 -3.652509 1.298453e-04 -3.812917 9.333850e-05
#> 6 0.1734329 -1.037737 1.496961e-01 -1.467686 8.350360e-02
#> ahr.ahr ahr.theta.ctrl ahr.theta.trt ahr.z ahr.p
#> 1 0.6875365 0.5925798 0.4074202 -2.584198 4.880285e-03
#> 2 0.4548326 0.6873643 0.3126357 -5.546342 1.458539e-08
#> 3 0.4408430 0.6940381 0.3059619 -5.854461 2.392791e-09
#> 4 0.5446814 0.6473827 0.3526173 -4.280992 9.303115e-06
#> 5 0.5873300 0.6299887 0.3700113 -3.657652 1.272682e-04
#> 6 0.8184949 0.5499053 0.4500947 -1.364031 8.627888e-02