Core Concepts
concepts.RmdOverview
rxsim organises a clinical trial simulation around three
collaborating objects. A Population owns the subject-level
data and tracks each subject’s enrollment and dropout times. A
Timer drives the trial clock. It stores discrete timepoints
per arm and evaluates condition-triggered analyses when the data meet a
criterion. A Trial orchestrates the simulation by iterating
over timepoints, updating populations, snapshotting the enrolled cohort,
and collecting results.
graph LR
P1(Control) --> TR(Trial)
P2(Treatment) --> TR
TI(Timer) --> TR
TR --> LD(Locked Data)
TR --> RS(Results)
#> <div class="mermaid">
#> graph LR
#> P1(Control) --> TR(Trial)
#> P2(Treatment) --> TR
#> TI(Timer) --> TR
#> TR --> LD(Locked Data)
#> TR --> RS(Results)
#> </div>The run() loop that Trial executes at each
timepoint follows this sequence:
graph TD
A([Next timepoint]) --> B[Enroll and drop subjects]
B --> C[Snapshot enrolled subjects]
C --> D{Condition triggered?}
D -- Yes --> E[Run analysis]
D -- No --> F[Advance clock]
E --> F
F --> G{More timepoints?}
G -- Yes --> A
G -- No --> H([Done])
#> <div class="mermaid">
#> graph TD
#> A([Next timepoint]) --> B[Enroll and drop subjects]
#> B --> C[Snapshot enrolled subjects]
#> C --> D{Condition triggered?}
#> D -- Yes --> E[Run analysis]
#> D -- No --> F[Advance clock]
#> E --> F
#> F --> G{More timepoints?}
#> G -- Yes --> A
#> G -- No --> H([Done])
#> </div>In most workflows you will never construct these objects by hand.
Instead you use the high-level entry point
replicate_trial() + run_trials(), which build
and execute n independent Trial objects from
your generator functions. Understanding the three classes directly is
useful when you want to:
- inspect the locked snapshot mid-simulation for debugging
- write custom multi-timepoint designs that
gen_plan()cannot express - use
Trial$new()directly for a one-off single-run simulation (as in Example 8)
The rest of this vignette unpacks each building block in turn.
Population
A Population object represents a single arm’s worth of
subjects. It stores the endpoint data alongside two tracking vectors
(enrolled, dropped) that record the calendar
time each subject was enrolled or dropped — or NA if
neither event has occurred yet.
Data structure
The data field must be a data frame with at least four
columns:
| Column | Type | Meaning |
|---|---|---|
id |
integer | Unique subject identifier within the arm |
arm |
character | Arm label (auto-filled from name if absent) |
readout_time |
numeric | Lag from enrollment to endpoint observation |
| (endpoint) | numeric | At least one endpoint column (e.g., data,
y, tte) |
The readout_time column is not a calendar time — it is
the lag between a subject’s enrollment date and the date their endpoint
is observed. Use 0 for immediately available data (e.g., a
baseline measure) and a positive value for delayed readouts (e.g.,
readout_time = 12 means the endpoint is read 12 weeks after
enrollment).
vector_to_dataframe()
For the common case of a single continuous endpoint,
vector_to_dataframe() wraps a numeric vector into the
required format with columns id, data, and
readout_time = 0:
ep <- rnorm(6, mean = 1, sd = 0.5)
df <- vector_to_dataframe(ep)
df
#> id data readout_time
#> 1 1 0.2999782 0
#> 2 2 1.1276585 0
#> 3 3 -0.2186318 0
#> 4 4 0.9972144 0
#> 5 5 1.3107764 0
#> 6 6 1.5742058 0You can also build the data frame yourself — useful when you have covariates, time-varying endpoints, or different readout lags per subject.
Repeated measurements and n_readouts
When a subject contributes multiple measurements (a longitudinal or
pharmacokinetic design), each measurement appears as its own row with a
distinct readout_time. Population detects this
automatically and stores the number of rows per subject in
n_readouts:
# Two timepoints per subject: baseline (0) and week 12 (12)
long_df <- data.frame(
id = rep(1:4, each = 2),
readout_time = rep(c(0, 12), times = 4),
response = rnorm(8)
)
pop_long <- Population$new(name = "treatment", data = long_df)
pop_long$n # 4 unique subjects
#> [1] 4
pop_long$n_readouts # 2 rows per subject
#> [1] 2Enrollment and dropout vectors
enrolled and dropped are numeric vectors of
length n, initialised to NA for every subject.
NA means “not yet acted on”: the subject exists in the pool
but has not been enrolled or dropped. The Trial assigns
calendar times to these vectors as the simulation clock advances.
set_enrolled(n, time) marks n randomly
chosen currently unenrolled subjects as enrolled at time.
set_dropped(n, time) similarly marks n
randomly chosen enrolled-and-not-yet-dropped subjects as dropped at
time.
set.seed(1)
pop <- Population$new(name = "control", data = vector_to_dataframe(rnorm(8)))
pop$enrolled # all NA to start
#> [1] NA NA NA NA NA NA NA NA
pop$set_enrolled(5, time = 2)
pop$enrolled # 5 subjects enrolled at t=2, 3 still NA
#> [1] 2 2 2 NA NA 2 NA 2
pop$set_dropped(2, time = 7)
pop$dropped # 2 of the enrolled subjects dropped at t=7
#> [1] 7 NA NA NA NA NA NA 7The Trial’s run() method calls these
setters automatically based on the Timer’s schedule — you
rarely need to call them directly.
Timer
A Timer drives the trial clock. It holds two data
structures: a timelist that specifies how many subjects to
enroll or drop in each arm at each time unit, and a
conditions list that defines analysis-triggering criteria.
At each unique time in the timelist,
Trial$run() asks the Timer to evaluate its
conditions against the current snapshot.
Timepoints
Each entry in the timelist has four fields:
| Field | Type | Meaning |
|---|---|---|
time |
numeric | Calendar time of the event |
arm |
character | Which arm the event applies to |
enroller |
integer | Number of subjects to enroll at this time |
dropper |
integer | Number of subjects to drop at this time |
Timepoints are per-arm because the two arms in a trial may enroll subjects on different schedules, and enrollment events in one arm do not affect the other.
t <- Timer$new(name = "my_timer")
t$add_timepoint(time = 1, arm = "control", enroller = 5L, dropper = 0L)
t$add_timepoint(time = 1, arm = "treatment", enroller = 5L, dropper = 0L)
t$add_timepoint(time = 2, arm = "control", enroller = 3L, dropper = 1L)
t$add_timepoint(time = 2, arm = "treatment", enroller = 4L, dropper = 0L)
t$add_timepoint(time = 10, arm = "control", enroller = 0L, dropper = 0L)
t$add_timepoint(time = 10, arm = "treatment", enroller = 0L, dropper = 0L)
t$get_unique_times() # c(1, 2, 10)
#> [1] 1 2 10
t$get_end_timepoint() # 10
#> [1] 10
t$get_n_arms() # 2
#> [1] 2Rather than adding timepoints one by one,
add_timepoints() accepts a data frame with the four columns
above — exactly what gen_plan() and
gen_timepoints() return. See Enrollment & Dropout Modeling for
details.
Conditions
A condition pairs a filter expression with an optional analysis
function. When the Trial calls
check_conditions() at each timepoint, each condition’s
expression is evaluated against the current snapshot using
dplyr::filter(). If the filtered result is non-empty (the
condition is satisfied), the associated analysis function is called and
its return value is stored.
Conditions are added with add_condition(). The
... arguments are dplyr::filter-style boolean
expressions, evaluated lazily against the snapshot columns:
# A toy snapshot data frame
snapshot <- data.frame(
id = 1:8,
arm = rep(c("control", "treatment"), 4),
enroll_time = c(1, 1, 2, 2, NA, NA, NA, NA),
data = rnorm(8)
)
t2 <- Timer$new(name = "demo")
# Fire when at least 4 subjects are enrolled
t2$add_condition(
sum(!is.na(enroll_time)) >= 4,
analysis = function(df, current_time) {
data.frame(n_enrolled = sum(!is.na(df$enroll_time)),
fired_at = current_time)
},
name = "interim"
)
res <- t2$check_conditions(locked_data = snapshot, current_time = 5)
res[["interim"]]
#> n_enrolled fired_at
#> 1 4 5If no analysis function is provided, check_conditions()
returns the filtered subset instead of calling a function — convenient
for inspection or debugging.
Trial
A Trial wires one or more Populations and a
Timer together and runs the simulation.
Constructor
Trial$new(
name = "my_trial",
seed = 42, # optional; set for reproducibility
timer = my_timer,
population = list(pop_a, pop_b)
)The population argument is a list of
Population objects, one per arm. The arm labels in each
Population’s name field must match the
arm identifiers in the Timer’s timelist.
What run() does
Calling trial$run() executes the following loop:
- Iterate over each unique time
tin theTimer’s timelist, in ascending order. - For each arm, apply
set_enrolled()andset_dropped()according to that arm’s timepoint entry. - Build a combined snapshot by row-binding all arms’ enrolled
subjects. Four columns are appended by
Trial:
| Column | Meaning |
|---|---|
enroll_time |
Calendar time the subject was enrolled (NA if not yet
enrolled) |
drop_time |
Calendar time the subject dropped out (NA if still
active) |
measurement_time |
enroll_time + readout_time |
time |
The current clock time t
|
- Call
timer$check_conditions(snapshot, t)— evaluate all conditions and collect analysis results. - If any condition fired, store the snapshot in
locked_data[["time_t"]]and the analysis outputs inresults[["time_t"]].
locked_data and results
locked_data is a named list of snapshots, one per unique
timepoint at which at least one analysis fired. Each snapshot is the
full subject-level data frame at that moment — useful for auditing which
subjects were enrolled, computing post-hoc statistics, or debugging an
analysis function.
results has the same time-indexed structure, but each
element is itself a named list — one entry per condition that fired at
that timepoint. The value is whatever your analysis function
returned.
A minimal end-to-end example
set.seed(7)
# Two small populations
pop_a <- Population$new("A", data = vector_to_dataframe(rnorm(10)))
pop_b <- Population$new("B", data = vector_to_dataframe(rnorm(10, mean = 0.5)))
# Timer: enroll in two waves, final readout at time 15
tm <- Timer$new("tm")
tm$add_timepoint(time = 1, arm = "A", enroller = 5L, dropper = 0L)
tm$add_timepoint(time = 1, arm = "B", enroller = 5L, dropper = 0L)
tm$add_timepoint(time = 3, arm = "A", enroller = 5L, dropper = 0L)
tm$add_timepoint(time = 3, arm = "B", enroller = 5L, dropper = 0L)
tm$add_timepoint(time = 15, arm = "A", enroller = 0L, dropper = 0L)
tm$add_timepoint(time = 15, arm = "B", enroller = 0L, dropper = 0L)
# Trigger: fire at calendar time 15
trigger_by_calendar(15, tm, analysis = function(df, current_time) {
enrolled <- subset(df, !is.na(enroll_time))
data.frame(
n = nrow(enrolled),
mean_A = mean(enrolled$data[enrolled$arm == "A"]),
mean_B = mean(enrolled$data[enrolled$arm == "B"])
)
})
trial <- Trial$new(name = "demo", seed = 7, timer = tm,
population = list(pop_a, pop_b))
trial$run()
# Inspect results
prettify_results(trial$results)
#> time cal_time_15.n cal_time_15.mean_A cal_time_15.mean_B
#> 1 15 20 0.1039757 1.282517Analysis triggers in depth
Triggers are the mechanism by which rxsim knows when to analyse the data and what to compute. Understanding how they are stored and evaluated is key to writing correct and flexible simulation code.
Why rlang::exprs()?
A trigger condition needs to be a stored expression, not an
immediately evaluated one. When you write
sum(!is.na(enroll_time)) >= 20, R would evaluate it at
the point of definition — before any data exists.
rlang::exprs() wraps the expression in a quoted form so it
is only evaluated later, inside check_conditions(), against
the actual snapshot:
The !! (bang-bang) operator
When you want to inject a value from the current R environment into a
stored expression, use !!. Without it, the variable name is
treated as a column name in the snapshot data frame — almost certainly
not what you want:
target_n <- 40
# CORRECT: !! injects the value 40 at definition time
trigger <- rlang::exprs(sum(!is.na(enroll_time)) >= !!target_n)
# WRONG: "target_n" would be looked for as a column name in the snapshot
trigger_bad <- rlang::exprs(sum(!is.na(enroll_time)) >= target_n)This distinction matters when you loop over scenarios with different
sample sizes: each iteration should bake in its own
target_n value via !!.
Columns available in a trigger expression
The trigger expression is evaluated against the snapshot data frame,
which contains all columns from the Population’s
data plus the four columns appended by
Trial:
-
enroll_time— calendar enrollment time (NAif not enrolled) -
drop_time— calendar dropout time (NAif not dropped) -
measurement_time—enroll_time + readout_time -
time— current clock time -
arm— arm label
Any user-defined endpoint column (e.g., data,
response, tte) is also available.
trigger_by_calendar() and trigger_by_fraction()
These two helpers wrap the most common trigger patterns:
trigger_by_calendar(cal_time, timer, analysis) fires at
a specific calendar time — useful for a pre-planned final analysis or a
scheduled interim review:
trigger_by_calendar(24, timer = tm, analysis = function(df, current_time) {
data.frame(n_enrolled = sum(!is.na(df$enroll_time)))
})trigger_by_fraction(fraction, timer, sample_size, analysis)
fires once a given fraction of the total target sample has enrolled —
natural for information-fraction-based interim rules:
# Interim at 50% enrollment
trigger_by_fraction(0.5, timer = tm, sample_size = 100,
analysis = function(df, current_time) {
enrolled <- subset(df, !is.na(enroll_time))
data.frame(n = nrow(enrolled), time = current_time)
})Custom conditions
You can condition on any expression involving the snapshot columns. For time-to-event endpoints this commonly means waiting for a target number of events rather than a target enrollment count:
# Fire when 30 events have been observed (event = 1, censored = 0)
timer$add_condition(
sum(event == 1 & !is.na(enroll_time)) >= !!n_events,
analysis = my_tte_analysis,
name = "event_driven_interim"
)Multiple conditions can be combined with commas (they are ANDed
together, just as in dplyr::filter()):
The analysis function signature
Every analysis function receives two arguments:
-
df: the full snapshot — all arms, all currently enrolled subjects. The condition filter determined whether to fire; the analysis function decides what to compute from the full data. -
current_time: the numeric clock time at which the condition fired.
my_analysis <- function(df, current_time) {
enrolled <- subset(df, !is.na(enroll_time))
data.frame(
fired_at = current_time,
n_ctrl = sum(enrolled$arm == "control"),
n_trt = sum(enrolled$arm == "treatment"),
mean_diff = mean(enrolled$data[enrolled$arm == "treatment"]) -
mean(enrolled$data[enrolled$arm == "control"])
)
}Return values
The analysis function’s return value is stored verbatim in
results. collect_results() handles three
cases:
-
data.frame(recommended) — the standard pattern; one row per trigger event is the convention. -
named
list— coerced to a single-row data frame. -
NULL— silently skipped bycollect_results().
Returning NULL is useful for side-effects-only analyses
(e.g., writing a log entry) or for marking that a trigger condition was
not yet satisfied.
Putting it together: replicate_trial() and run_trials()
Building a single Trial by hand is useful for
exploration, but the purpose of rxsim is operating-characteristic
simulation across many replicates. replicate_trial() +
run_trials() handle this ergonomically.
How replicate_trial() works
For each of the n replicates,
replicate_trial():
- Calls
gen_plan()with yourenrollmentanddropoutfunctions to generate a fresh, stochastic enrollment/dropout schedule. - Builds a new
Timer, loads the schedule viaadd_timepoints(), and registers all analysis triggers. - Calls each population generator function to draw fresh subject-level endpoint data.
- Constructs a
Trial$new()wiring the newTimerandPopulationobjects together.
Each replicate therefore has independent endpoint data and independent enrollment timing — both sources of variability that operating characteristic simulations are designed to characterise.
A complete six-step example
set.seed(42)
# Step 1 — design parameters
sample_size <- 30
arms <- c("control", "treatment")
allocation <- c(1, 1)
true_delta <- 0.5
scenario <- data.frame(sample_size = sample_size, true_delta = true_delta)
# Step 2 — population generators (called fresh per replicate)
population_generators <- list(
control = function(n) vector_to_dataframe(rnorm(n)),
treatment = function(n) vector_to_dataframe(rnorm(n, mean = true_delta))
)
# Step 3 — enrollment and dropout (inter-arrival functions)
enrollment <- function(n) rexp(n, rate = 2)
dropout <- function(n) rexp(n, rate = 0.02)
# Step 4 — analysis trigger: fire when full enrollment is reached
analysis_generators <- list(
final = list(
trigger = rlang::exprs(
sum(!is.na(enroll_time)) >= !!sample_size
),
analysis = function(df, current_time) {
enrolled <- subset(df, !is.na(enroll_time))
data.frame(
scenario,
fired_at = current_time,
n_ctrl = sum(enrolled$arm == "control"),
n_trt = sum(enrolled$arm == "treatment"),
mean_ctrl = mean(enrolled$data[enrolled$arm == "control"]),
mean_trt = mean(enrolled$data[enrolled$arm == "treatment"])
)
}
)
)
# Step 5 — create and run replicates
trials <- replicate_trial(
trial_name = "concepts_demo",
sample_size = sample_size,
arms = arms,
allocation = allocation,
enrollment = enrollment,
dropout = dropout,
analysis_generators = analysis_generators,
population_generators = population_generators,
n = 10
)
run_trials(trials)
#> [[1]]
#> <Trial>
#> Public:
#> clone: function (deep = FALSE)
#> initialize: function (name, seed = NULL, timer = NULL, population = list(),
#> locked_data: list
#> name: concepts_demo_1
#> population: list
#> results: list
#> run: function ()
#> seed: NULL
#> timer: Timer, R6
#>
#> [[2]]
#> <Trial>
#> Public:
#> clone: function (deep = FALSE)
#> initialize: function (name, seed = NULL, timer = NULL, population = list(),
#> locked_data: list
#> name: concepts_demo_2
#> population: list
#> results: list
#> run: function ()
#> seed: NULL
#> timer: Timer, R6
#>
#> [[3]]
#> <Trial>
#> Public:
#> clone: function (deep = FALSE)
#> initialize: function (name, seed = NULL, timer = NULL, population = list(),
#> locked_data: list
#> name: concepts_demo_3
#> population: list
#> results: list
#> run: function ()
#> seed: NULL
#> timer: Timer, R6
#>
#> [[4]]
#> <Trial>
#> Public:
#> clone: function (deep = FALSE)
#> initialize: function (name, seed = NULL, timer = NULL, population = list(),
#> locked_data: list
#> name: concepts_demo_4
#> population: list
#> results: list
#> run: function ()
#> seed: NULL
#> timer: Timer, R6
#>
#> [[5]]
#> <Trial>
#> Public:
#> clone: function (deep = FALSE)
#> initialize: function (name, seed = NULL, timer = NULL, population = list(),
#> locked_data: list
#> name: concepts_demo_5
#> population: list
#> results: list
#> run: function ()
#> seed: NULL
#> timer: Timer, R6
#>
#> [[6]]
#> <Trial>
#> Public:
#> clone: function (deep = FALSE)
#> initialize: function (name, seed = NULL, timer = NULL, population = list(),
#> locked_data: list
#> name: concepts_demo_6
#> population: list
#> results: list
#> run: function ()
#> seed: NULL
#> timer: Timer, R6
#>
#> [[7]]
#> <Trial>
#> Public:
#> clone: function (deep = FALSE)
#> initialize: function (name, seed = NULL, timer = NULL, population = list(),
#> locked_data: list
#> name: concepts_demo_7
#> population: list
#> results: list
#> run: function ()
#> seed: NULL
#> timer: Timer, R6
#>
#> [[8]]
#> <Trial>
#> Public:
#> clone: function (deep = FALSE)
#> initialize: function (name, seed = NULL, timer = NULL, population = list(),
#> locked_data: list
#> name: concepts_demo_8
#> population: list
#> results: list
#> run: function ()
#> seed: NULL
#> timer: Timer, R6
#>
#> [[9]]
#> <Trial>
#> Public:
#> clone: function (deep = FALSE)
#> initialize: function (name, seed = NULL, timer = NULL, population = list(),
#> locked_data: list
#> name: concepts_demo_9
#> population: list
#> results: list
#> run: function ()
#> seed: NULL
#> timer: Timer, R6
#>
#> [[10]]
#> <Trial>
#> Public:
#> clone: function (deep = FALSE)
#> initialize: function (name, seed = NULL, timer = NULL, population = list(),
#> locked_data: list
#> name: concepts_demo_10
#> population: list
#> results: list
#> run: function ()
#> seed: NULL
#> timer: Timer, R6
# Step 6 — collect and inspect
results <- collect_results(trials)
results
#> replicate timepoint analysis sample_size true_delta fired_at n_ctrl
#> 1 1 14.85864 final 30 0.5 14.85864 15
#> 2 1 28.46928 final 30 0.5 28.46928 15
#> 3 1 179.39710 final 30 0.5 179.39710 15
#> 4 1 204.27664 final 30 0.5 204.27664 15
#> 5 1 222.00432 final 30 0.5 222.00432 15
#> 6 1 309.82477 final 30 0.5 309.82477 15
#> 7 1 346.69972 final 30 0.5 346.69972 15
#> 8 1 348.14102 final 30 0.5 348.14102 15
#> 9 1 365.70174 final 30 0.5 365.70174 15
#> 10 1 385.33474 final 30 0.5 385.33474 15
#> 11 1 480.43903 final 30 0.5 480.43903 15
#> 12 1 519.35371 final 30 0.5 519.35371 15
#> 13 1 552.16956 final 30 0.5 552.16956 15
#> 14 1 571.49004 final 30 0.5 571.49004 15
#> 15 1 642.29464 final 30 0.5 642.29464 15
#> 16 1 718.45108 final 30 0.5 718.45108 15
#> 17 1 740.38666 final 30 0.5 740.38666 15
#> 18 1 948.79314 final 30 0.5 948.79314 15
#> 19 1 1008.54849 final 30 0.5 1008.54849 15
#> 20 1 1009.98927 final 30 0.5 1009.98927 15
#> 21 1 1352.31787 final 30 0.5 1352.31787 15
#> 22 1 1360.47827 final 30 0.5 1360.47827 15
#> 23 1 1442.95507 final 30 0.5 1442.95507 15
#> 24 1 1499.41809 final 30 0.5 1499.41809 15
#> 25 1 1513.98128 final 30 0.5 1513.98128 15
#> 26 1 1541.56362 final 30 0.5 1541.56362 15
#> 27 1 1547.92830 final 30 0.5 1547.92830 15
#> 28 1 1620.21011 final 30 0.5 1620.21011 15
#> 29 1 1665.91114 final 30 0.5 1665.91114 15
#> 30 1 1682.65379 final 30 0.5 1682.65379 15
#> 31 1 2105.77059 final 30 0.5 2105.77059 15
#> 32 2 13.65704 final 30 0.5 13.65704 15
#> 33 2 40.12907 final 30 0.5 40.12907 15
#> 34 2 48.87861 final 30 0.5 48.87861 15
#> 35 2 53.08020 final 30 0.5 53.08020 15
#> 36 2 68.06779 final 30 0.5 68.06779 15
#> 37 2 120.00897 final 30 0.5 120.00897 15
#> 38 2 274.27965 final 30 0.5 274.27965 15
#> 39 2 329.60709 final 30 0.5 329.60709 15
#> 40 2 334.34969 final 30 0.5 334.34969 15
#> 41 2 340.29801 final 30 0.5 340.29801 15
#> 42 2 344.60111 final 30 0.5 344.60111 15
#> 43 2 355.47256 final 30 0.5 355.47256 15
#> 44 2 401.54080 final 30 0.5 401.54080 15
#> 45 2 495.03695 final 30 0.5 495.03695 15
#> 46 2 661.02123 final 30 0.5 661.02123 15
#> 47 2 735.84724 final 30 0.5 735.84724 15
#> 48 2 915.28868 final 30 0.5 915.28868 15
#> 49 2 918.28933 final 30 0.5 918.28933 15
#> 50 2 987.22238 final 30 0.5 987.22238 15
#> 51 2 1095.30391 final 30 0.5 1095.30391 15
#> 52 2 1101.44770 final 30 0.5 1101.44770 15
#> 53 2 1211.99730 final 30 0.5 1211.99730 15
#> 54 2 1315.86879 final 30 0.5 1315.86879 15
#> 55 2 1320.83083 final 30 0.5 1320.83083 15
#> 56 2 1360.67220 final 30 0.5 1360.67220 15
#> 57 2 1401.86020 final 30 0.5 1401.86020 15
#> 58 2 1404.93461 final 30 0.5 1404.93461 15
#> 59 2 1596.86213 final 30 0.5 1596.86213 15
#> 60 2 1626.73816 final 30 0.5 1626.73816 15
#> 61 2 1749.41822 final 30 0.5 1749.41822 15
#> 62 2 1761.31338 final 30 0.5 1761.31338 15
#> 63 3 17.64924 final 30 0.5 17.64924 15
#> 64 3 41.82855 final 30 0.5 41.82855 15
#> 65 3 174.06645 final 30 0.5 174.06645 15
#> 66 3 177.04987 final 30 0.5 177.04987 15
#> 67 3 195.23773 final 30 0.5 195.23773 15
#> 68 3 281.56056 final 30 0.5 281.56056 15
#> 69 3 300.66545 final 30 0.5 300.66545 15
#> 70 3 415.11680 final 30 0.5 415.11680 15
#> 71 3 611.04363 final 30 0.5 611.04363 15
#> 72 3 612.36759 final 30 0.5 612.36759 15
#> 73 3 625.44020 final 30 0.5 625.44020 15
#> 74 3 693.85189 final 30 0.5 693.85189 15
#> 75 3 697.59443 final 30 0.5 697.59443 15
#> 76 3 735.24512 final 30 0.5 735.24512 15
#> 77 3 856.63921 final 30 0.5 856.63921 15
#> 78 3 918.47801 final 30 0.5 918.47801 15
#> 79 3 1027.50759 final 30 0.5 1027.50759 15
#> 80 3 1107.95781 final 30 0.5 1107.95781 15
#> 81 3 1144.45971 final 30 0.5 1144.45971 15
#> 82 3 1172.24833 final 30 0.5 1172.24833 15
#> 83 3 1208.98681 final 30 0.5 1208.98681 15
#> 84 3 1271.76920 final 30 0.5 1271.76920 15
#> 85 3 1276.06048 final 30 0.5 1276.06048 15
#> 86 3 1387.92622 final 30 0.5 1387.92622 15
#> 87 3 1434.36382 final 30 0.5 1434.36382 15
#> 88 3 1435.77955 final 30 0.5 1435.77955 15
#> 89 3 1437.27185 final 30 0.5 1437.27185 15
#> 90 3 1441.82329 final 30 0.5 1441.82329 15
#> 91 3 1479.38788 final 30 0.5 1479.38788 15
#> 92 3 1655.30887 final 30 0.5 1655.30887 15
#> 93 4 11.80808 final 30 0.5 11.80808 15
#> 94 4 74.32182 final 30 0.5 74.32182 15
#> 95 4 181.51216 final 30 0.5 181.51216 15
#> 96 4 221.58161 final 30 0.5 221.58161 15
#> 97 4 248.36705 final 30 0.5 248.36705 15
#> 98 4 349.95121 final 30 0.5 349.95121 15
#> 99 4 515.68996 final 30 0.5 515.68996 15
#> 100 4 666.42489 final 30 0.5 666.42489 15
#> 101 4 707.99707 final 30 0.5 707.99707 15
#> 102 4 762.41640 final 30 0.5 762.41640 15
#> 103 4 786.16173 final 30 0.5 786.16173 15
#> 104 4 821.15181 final 30 0.5 821.15181 15
#> 105 4 822.89551 final 30 0.5 822.89551 15
#> 106 4 848.83998 final 30 0.5 848.83998 15
#> 107 4 862.44843 final 30 0.5 862.44843 15
#> 108 4 963.33942 final 30 0.5 963.33942 15
#> 109 4 963.35346 final 30 0.5 963.35346 15
#> 110 4 1141.97410 final 30 0.5 1141.97410 15
#> 111 4 1335.03435 final 30 0.5 1335.03435 15
#> 112 4 1426.71867 final 30 0.5 1426.71867 15
#> 113 4 1529.34757 final 30 0.5 1529.34757 15
#> 114 4 1598.19506 final 30 0.5 1598.19506 15
#> 115 4 1643.82361 final 30 0.5 1643.82361 15
#> 116 4 1677.19237 final 30 0.5 1677.19237 15
#> 117 4 1739.77701 final 30 0.5 1739.77701 15
#> 118 4 1776.23710 final 30 0.5 1776.23710 15
#> 119 4 1820.69432 final 30 0.5 1820.69432 15
#> 120 4 1848.33883 final 30 0.5 1848.33883 15
#> 121 4 1893.76674 final 30 0.5 1893.76674 15
#> 122 4 1895.32186 final 30 0.5 1895.32186 15
#> 123 4 1935.66056 final 30 0.5 1935.66056 15
#> 124 5 12.91792 final 30 0.5 12.91792 15
#> 125 5 59.27306 final 30 0.5 59.27306 15
#> 126 5 106.74839 final 30 0.5 106.74839 15
#> 127 5 138.26688 final 30 0.5 138.26688 15
#> 128 5 196.58786 final 30 0.5 196.58786 15
#> 129 5 246.46181 final 30 0.5 246.46181 15
#> 130 5 311.90055 final 30 0.5 311.90055 15
#> 131 5 343.59568 final 30 0.5 343.59568 15
#> 132 5 463.86672 final 30 0.5 463.86672 15
#> 133 5 510.27762 final 30 0.5 510.27762 15
#> 134 5 523.92621 final 30 0.5 523.92621 15
#> 135 5 543.81839 final 30 0.5 543.81839 15
#> 136 5 547.34530 final 30 0.5 547.34530 15
#> 137 5 561.56543 final 30 0.5 561.56543 15
#> 138 5 730.24406 final 30 0.5 730.24406 15
#> 139 5 758.07099 final 30 0.5 758.07099 15
#> 140 5 766.55931 final 30 0.5 766.55931 15
#> 141 5 890.64011 final 30 0.5 890.64011 15
#> 142 5 953.51917 final 30 0.5 953.51917 15
#> 143 5 1045.85846 final 30 0.5 1045.85846 15
#> 144 5 1118.62309 final 30 0.5 1118.62309 15
#> 145 5 1154.77303 final 30 0.5 1154.77303 15
#> 146 5 1160.09774 final 30 0.5 1160.09774 15
#> 147 5 1238.64017 final 30 0.5 1238.64017 15
#> 148 5 1271.37483 final 30 0.5 1271.37483 15
#> 149 5 1291.99589 final 30 0.5 1291.99589 15
#> 150 5 1367.62195 final 30 0.5 1367.62195 15
#> 151 5 1583.05490 final 30 0.5 1583.05490 15
#> 152 5 1601.70445 final 30 0.5 1601.70445 15
#> 153 5 1619.82627 final 30 0.5 1619.82627 15
#> 154 5 1633.98107 final 30 0.5 1633.98107 15
#> 155 6 15.97475 final 30 0.5 15.97475 15
#> 156 6 30.58640 final 30 0.5 30.58640 15
#> 157 6 71.09775 final 30 0.5 71.09775 15
#> 158 6 118.61027 final 30 0.5 118.61027 15
#> 159 6 179.73142 final 30 0.5 179.73142 15
#> 160 6 182.16761 final 30 0.5 182.16761 15
#> 161 6 202.91827 final 30 0.5 202.91827 15
#> 162 6 222.21796 final 30 0.5 222.21796 15
#> 163 6 353.10786 final 30 0.5 353.10786 15
#> 164 6 500.78247 final 30 0.5 500.78247 15
#> 165 6 565.62785 final 30 0.5 565.62785 15
#> 166 6 797.41025 final 30 0.5 797.41025 15
#> 167 6 804.19935 final 30 0.5 804.19935 15
#> 168 6 812.67301 final 30 0.5 812.67301 15
#> 169 6 816.84071 final 30 0.5 816.84071 15
#> 170 6 932.16346 final 30 0.5 932.16346 15
#> 171 6 958.58778 final 30 0.5 958.58778 15
#> 172 6 984.39527 final 30 0.5 984.39527 15
#> 173 6 989.02383 final 30 0.5 989.02383 15
#> 174 6 993.37137 final 30 0.5 993.37137 15
#> 175 6 998.20217 final 30 0.5 998.20217 15
#> 176 6 1080.36898 final 30 0.5 1080.36898 15
#> 177 6 1131.86547 final 30 0.5 1131.86547 15
#> 178 6 1158.67270 final 30 0.5 1158.67270 15
#> 179 6 1286.41357 final 30 0.5 1286.41357 15
#> 180 6 1461.67934 final 30 0.5 1461.67934 15
#> 181 6 1513.03861 final 30 0.5 1513.03861 15
#> 182 6 1599.55679 final 30 0.5 1599.55679 15
#> 183 6 1711.45037 final 30 0.5 1711.45037 15
#> 184 6 1765.13555 final 30 0.5 1765.13555 15
#> 185 6 1790.76805 final 30 0.5 1790.76805 15
#> 186 7 14.76681 final 30 0.5 14.76681 15
#> 187 7 17.22987 final 30 0.5 17.22987 15
#> 188 7 191.74989 final 30 0.5 191.74989 15
#> 189 7 193.53281 final 30 0.5 193.53281 15
#> 190 7 250.97947 final 30 0.5 250.97947 15
#> 191 7 270.52755 final 30 0.5 270.52755 15
#> 192 7 278.99352 final 30 0.5 278.99352 15
#> 193 7 291.00901 final 30 0.5 291.00901 15
#> 194 7 297.45871 final 30 0.5 297.45871 15
#> 195 7 452.33066 final 30 0.5 452.33066 15
#> 196 7 495.31727 final 30 0.5 495.31727 15
#> 197 7 513.13144 final 30 0.5 513.13144 15
#> 198 7 514.71117 final 30 0.5 514.71117 15
#> 199 7 648.96664 final 30 0.5 648.96664 15
#> 200 7 715.55492 final 30 0.5 715.55492 15
#> 201 7 728.16967 final 30 0.5 728.16967 15
#> 202 7 930.86318 final 30 0.5 930.86318 15
#> 203 7 949.35259 final 30 0.5 949.35259 15
#> 204 7 1110.39403 final 30 0.5 1110.39403 15
#> 205 7 1111.53232 final 30 0.5 1111.53232 15
#> 206 7 1114.14650 final 30 0.5 1114.14650 15
#> 207 7 1152.02824 final 30 0.5 1152.02824 15
#> 208 7 1152.52184 final 30 0.5 1152.52184 15
#> 209 7 1155.77137 final 30 0.5 1155.77137 15
#> 210 7 1279.23964 final 30 0.5 1279.23964 15
#> 211 7 1281.29241 final 30 0.5 1281.29241 15
#> 212 7 1298.56498 final 30 0.5 1298.56498 15
#> 213 7 1311.29725 final 30 0.5 1311.29725 15
#> 214 7 1423.55366 final 30 0.5 1423.55366 15
#> 215 7 1480.44513 final 30 0.5 1480.44513 15
#> 216 8 13.66482 final 30 0.5 13.66482 15
#> 217 8 13.79376 final 30 0.5 13.79376 15
#> 218 8 51.92968 final 30 0.5 51.92968 15
#> 219 8 117.08581 final 30 0.5 117.08581 15
#> 220 8 144.34627 final 30 0.5 144.34627 15
#> 221 8 153.54673 final 30 0.5 153.54673 15
#> 222 8 168.91284 final 30 0.5 168.91284 15
#> 223 8 198.76434 final 30 0.5 198.76434 15
#> 224 8 292.53964 final 30 0.5 292.53964 15
#> 225 8 318.01040 final 30 0.5 318.01040 15
#> 226 8 341.60244 final 30 0.5 341.60244 15
#> 227 8 366.78101 final 30 0.5 366.78101 15
#> 228 8 417.96127 final 30 0.5 417.96127 15
#> 229 8 558.03142 final 30 0.5 558.03142 15
#> 230 8 572.38772 final 30 0.5 572.38772 15
#> 231 8 616.96598 final 30 0.5 616.96598 15
#> 232 8 680.30956 final 30 0.5 680.30956 15
#> 233 8 703.84945 final 30 0.5 703.84945 15
#> 234 8 725.56311 final 30 0.5 725.56311 15
#> 235 8 809.04486 final 30 0.5 809.04486 15
#> 236 8 829.65483 final 30 0.5 829.65483 15
#> 237 8 884.58893 final 30 0.5 884.58893 15
#> 238 8 920.04397 final 30 0.5 920.04397 15
#> 239 8 941.45513 final 30 0.5 941.45513 15
#> 240 8 1026.13573 final 30 0.5 1026.13573 15
#> 241 8 1043.48614 final 30 0.5 1043.48614 15
#> 242 8 1135.01653 final 30 0.5 1135.01653 15
#> 243 8 1158.19105 final 30 0.5 1158.19105 15
#> 244 8 1208.16100 final 30 0.5 1208.16100 15
#> 245 8 1263.30328 final 30 0.5 1263.30328 15
#> 246 8 1292.20260 final 30 0.5 1292.20260 15
#> 247 9 18.81521 final 30 0.5 18.81521 15
#> 248 9 45.25489 final 30 0.5 45.25489 15
#> 249 9 149.20340 final 30 0.5 149.20340 15
#> 250 9 151.30597 final 30 0.5 151.30597 15
#> 251 9 372.61226 final 30 0.5 372.61226 15
#> 252 9 713.06154 final 30 0.5 713.06154 15
#> 253 9 719.69989 final 30 0.5 719.69989 15
#> 254 9 756.09751 final 30 0.5 756.09751 15
#> 255 9 771.37198 final 30 0.5 771.37198 15
#> 256 9 820.63185 final 30 0.5 820.63185 15
#> 257 9 903.32705 final 30 0.5 903.32705 15
#> 258 9 985.54524 final 30 0.5 985.54524 15
#> 259 9 1067.51736 final 30 0.5 1067.51736 15
#> 260 9 1132.92803 final 30 0.5 1132.92803 15
#> 261 9 1170.10010 final 30 0.5 1170.10010 15
#> 262 9 1224.67732 final 30 0.5 1224.67732 15
#> 263 9 1333.22805 final 30 0.5 1333.22805 15
#> 264 9 1369.75168 final 30 0.5 1369.75168 15
#> 265 9 1432.93113 final 30 0.5 1432.93113 15
#> 266 9 1467.08644 final 30 0.5 1467.08644 15
#> 267 9 1531.31958 final 30 0.5 1531.31958 15
#> 268 9 1592.86738 final 30 0.5 1592.86738 15
#> 269 9 1637.76986 final 30 0.5 1637.76986 15
#> 270 9 1729.62034 final 30 0.5 1729.62034 15
#> 271 9 1756.87582 final 30 0.5 1756.87582 15
#> 272 9 1767.72488 final 30 0.5 1767.72488 15
#> 273 9 1779.95328 final 30 0.5 1779.95328 15
#> 274 9 1797.51059 final 30 0.5 1797.51059 15
#> 275 9 1835.94733 final 30 0.5 1835.94733 15
#> 276 9 1840.15397 final 30 0.5 1840.15397 15
#> 277 9 1853.03260 final 30 0.5 1853.03260 15
#> 278 10 11.84402 final 30 0.5 11.84402 15
#> 279 10 65.93802 final 30 0.5 65.93802 15
#> 280 10 114.79415 final 30 0.5 114.79415 15
#> 281 10 118.56286 final 30 0.5 118.56286 15
#> 282 10 249.50260 final 30 0.5 249.50260 15
#> 283 10 315.79657 final 30 0.5 315.79657 15
#> 284 10 356.10059 final 30 0.5 356.10059 15
#> 285 10 360.26315 final 30 0.5 360.26315 15
#> 286 10 364.76216 final 30 0.5 364.76216 15
#> 287 10 382.11500 final 30 0.5 382.11500 15
#> 288 10 441.63897 final 30 0.5 441.63897 15
#> 289 10 519.80005 final 30 0.5 519.80005 15
#> 290 10 522.19961 final 30 0.5 522.19961 15
#> 291 10 528.61700 final 30 0.5 528.61700 15
#> 292 10 606.97257 final 30 0.5 606.97257 15
#> 293 10 639.38183 final 30 0.5 639.38183 15
#> 294 10 689.94437 final 30 0.5 689.94437 15
#> 295 10 731.17913 final 30 0.5 731.17913 15
#> 296 10 766.21511 final 30 0.5 766.21511 15
#> 297 10 856.71461 final 30 0.5 856.71461 15
#> 298 10 979.28740 final 30 0.5 979.28740 15
#> 299 10 1169.52332 final 30 0.5 1169.52332 15
#> 300 10 1249.13102 final 30 0.5 1249.13102 15
#> 301 10 1254.76477 final 30 0.5 1254.76477 15
#> 302 10 1280.21756 final 30 0.5 1280.21756 15
#> 303 10 1296.35175 final 30 0.5 1296.35175 15
#> 304 10 1322.93170 final 30 0.5 1322.93170 15
#> 305 10 1399.38323 final 30 0.5 1399.38323 15
#> 306 10 1407.98763 final 30 0.5 1407.98763 15
#> 307 10 1428.71451 final 30 0.5 1428.71451 15
#> 308 10 1497.39038 final 30 0.5 1497.39038 15
#> n_trt mean_ctrl mean_trt
#> 1 15 -0.2426125 0.73924372
#> 2 15 -0.2426125 0.73924372
#> 3 15 -0.2426125 0.73924372
#> 4 15 -0.2426125 0.73924372
#> 5 15 -0.2426125 0.73924372
#> 6 15 -0.2426125 0.73924372
#> 7 15 -0.2426125 0.73924372
#> 8 15 -0.2426125 0.73924372
#> 9 15 -0.2426125 0.73924372
#> 10 15 -0.2426125 0.73924372
#> 11 15 -0.2426125 0.73924372
#> 12 15 -0.2426125 0.73924372
#> 13 15 -0.2426125 0.73924372
#> 14 15 -0.2426125 0.73924372
#> 15 15 -0.2426125 0.73924372
#> 16 15 -0.2426125 0.73924372
#> 17 15 -0.2426125 0.73924372
#> 18 15 -0.2426125 0.73924372
#> 19 15 -0.2426125 0.73924372
#> 20 15 -0.2426125 0.73924372
#> 21 15 -0.2426125 0.73924372
#> 22 15 -0.2426125 0.73924372
#> 23 15 -0.2426125 0.73924372
#> 24 15 -0.2426125 0.73924372
#> 25 15 -0.2426125 0.73924372
#> 26 15 -0.2426125 0.73924372
#> 27 15 -0.2426125 0.73924372
#> 28 15 -0.2426125 0.73924372
#> 29 15 -0.2426125 0.73924372
#> 30 15 -0.2426125 0.73924372
#> 31 15 -0.2426125 0.73924372
#> 32 15 0.1855949 0.77785489
#> 33 15 0.1855949 0.77785489
#> 34 15 0.1855949 0.77785489
#> 35 15 0.1855949 0.77785489
#> 36 15 0.1855949 0.77785489
#> 37 15 0.1855949 0.77785489
#> 38 15 0.1855949 0.77785489
#> 39 15 0.1855949 0.77785489
#> 40 15 0.1855949 0.77785489
#> 41 15 0.1855949 0.77785489
#> 42 15 0.1855949 0.77785489
#> 43 15 0.1855949 0.77785489
#> 44 15 0.1855949 0.77785489
#> 45 15 0.1855949 0.77785489
#> 46 15 0.1855949 0.77785489
#> 47 15 0.1855949 0.77785489
#> 48 15 0.1855949 0.77785489
#> 49 15 0.1855949 0.77785489
#> 50 15 0.1855949 0.77785489
#> 51 15 0.1855949 0.77785489
#> 52 15 0.1855949 0.77785489
#> 53 15 0.1855949 0.77785489
#> 54 15 0.1855949 0.77785489
#> 55 15 0.1855949 0.77785489
#> 56 15 0.1855949 0.77785489
#> 57 15 0.1855949 0.77785489
#> 58 15 0.1855949 0.77785489
#> 59 15 0.1855949 0.77785489
#> 60 15 0.1855949 0.77785489
#> 61 15 0.1855949 0.77785489
#> 62 15 0.1855949 0.77785489
#> 63 15 0.2729714 0.52646154
#> 64 15 0.2729714 0.52646154
#> 65 15 0.2729714 0.52646154
#> 66 15 0.2729714 0.52646154
#> 67 15 0.2729714 0.52646154
#> 68 15 0.2729714 0.52646154
#> 69 15 0.2729714 0.52646154
#> 70 15 0.2729714 0.52646154
#> 71 15 0.2729714 0.52646154
#> 72 15 0.2729714 0.52646154
#> 73 15 0.2729714 0.52646154
#> 74 15 0.2729714 0.52646154
#> 75 15 0.2729714 0.52646154
#> 76 15 0.2729714 0.52646154
#> 77 15 0.2729714 0.52646154
#> 78 15 0.2729714 0.52646154
#> 79 15 0.2729714 0.52646154
#> 80 15 0.2729714 0.52646154
#> 81 15 0.2729714 0.52646154
#> 82 15 0.2729714 0.52646154
#> 83 15 0.2729714 0.52646154
#> 84 15 0.2729714 0.52646154
#> 85 15 0.2729714 0.52646154
#> 86 15 0.2729714 0.52646154
#> 87 15 0.2729714 0.52646154
#> 88 15 0.2729714 0.52646154
#> 89 15 0.2729714 0.52646154
#> 90 15 0.2729714 0.52646154
#> 91 15 0.2729714 0.52646154
#> 92 15 0.2729714 0.52646154
#> 93 15 -0.2590078 0.50676828
#> 94 15 -0.2590078 0.50676828
#> 95 15 -0.2590078 0.50676828
#> 96 15 -0.2590078 0.50676828
#> 97 15 -0.2590078 0.50676828
#> 98 15 -0.2590078 0.50676828
#> 99 15 -0.2590078 0.50676828
#> 100 15 -0.2590078 0.50676828
#> 101 15 -0.2590078 0.50676828
#> 102 15 -0.2590078 0.50676828
#> 103 15 -0.2590078 0.50676828
#> 104 15 -0.2590078 0.50676828
#> 105 15 -0.2590078 0.50676828
#> 106 15 -0.2590078 0.50676828
#> 107 15 -0.2590078 0.50676828
#> 108 15 -0.2590078 0.50676828
#> 109 15 -0.2590078 0.50676828
#> 110 15 -0.2590078 0.50676828
#> 111 15 -0.2590078 0.50676828
#> 112 15 -0.2590078 0.50676828
#> 113 15 -0.2590078 0.50676828
#> 114 15 -0.2590078 0.50676828
#> 115 15 -0.2590078 0.50676828
#> 116 15 -0.2590078 0.50676828
#> 117 15 -0.2590078 0.50676828
#> 118 15 -0.2590078 0.50676828
#> 119 15 -0.2590078 0.50676828
#> 120 15 -0.2590078 0.50676828
#> 121 15 -0.2590078 0.50676828
#> 122 15 -0.2590078 0.50676828
#> 123 15 -0.2590078 0.50676828
#> 124 15 0.1494857 0.90866496
#> 125 15 0.1494857 0.90866496
#> 126 15 0.1494857 0.90866496
#> 127 15 0.1494857 0.90866496
#> 128 15 0.1494857 0.90866496
#> 129 15 0.1494857 0.90866496
#> 130 15 0.1494857 0.90866496
#> 131 15 0.1494857 0.90866496
#> 132 15 0.1494857 0.90866496
#> 133 15 0.1494857 0.90866496
#> 134 15 0.1494857 0.90866496
#> 135 15 0.1494857 0.90866496
#> 136 15 0.1494857 0.90866496
#> 137 15 0.1494857 0.90866496
#> 138 15 0.1494857 0.90866496
#> 139 15 0.1494857 0.90866496
#> 140 15 0.1494857 0.90866496
#> 141 15 0.1494857 0.90866496
#> 142 15 0.1494857 0.90866496
#> 143 15 0.1494857 0.90866496
#> 144 15 0.1494857 0.90866496
#> 145 15 0.1494857 0.90866496
#> 146 15 0.1494857 0.90866496
#> 147 15 0.1494857 0.90866496
#> 148 15 0.1494857 0.90866496
#> 149 15 0.1494857 0.90866496
#> 150 15 0.1494857 0.90866496
#> 151 15 0.1494857 0.90866496
#> 152 15 0.1494857 0.90866496
#> 153 15 0.1494857 0.90866496
#> 154 15 0.1494857 0.90866496
#> 155 15 0.4779952 -0.03207007
#> 156 15 0.4779952 -0.03207007
#> 157 15 0.4779952 -0.03207007
#> 158 15 0.4779952 -0.03207007
#> 159 15 0.4779952 -0.03207007
#> 160 15 0.4779952 -0.03207007
#> 161 15 0.4779952 -0.03207007
#> 162 15 0.4779952 -0.03207007
#> 163 15 0.4779952 -0.03207007
#> 164 15 0.4779952 -0.03207007
#> 165 15 0.4779952 -0.03207007
#> 166 15 0.4779952 -0.03207007
#> 167 15 0.4779952 -0.03207007
#> 168 15 0.4779952 -0.03207007
#> 169 15 0.4779952 -0.03207007
#> 170 15 0.4779952 -0.03207007
#> 171 15 0.4779952 -0.03207007
#> 172 15 0.4779952 -0.03207007
#> 173 15 0.4779952 -0.03207007
#> 174 15 0.4779952 -0.03207007
#> 175 15 0.4779952 -0.03207007
#> 176 15 0.4779952 -0.03207007
#> 177 15 0.4779952 -0.03207007
#> 178 15 0.4779952 -0.03207007
#> 179 15 0.4779952 -0.03207007
#> 180 15 0.4779952 -0.03207007
#> 181 15 0.4779952 -0.03207007
#> 182 15 0.4779952 -0.03207007
#> 183 15 0.4779952 -0.03207007
#> 184 15 0.4779952 -0.03207007
#> 185 15 0.4779952 -0.03207007
#> 186 15 0.1935956 0.05437164
#> 187 15 0.1935956 0.05437164
#> 188 15 0.1935956 0.05437164
#> 189 15 0.1935956 0.05437164
#> 190 15 0.1935956 0.05437164
#> 191 15 0.1935956 0.05437164
#> 192 15 0.1935956 0.05437164
#> 193 15 0.1935956 0.05437164
#> 194 15 0.1935956 0.05437164
#> 195 15 0.1935956 0.05437164
#> 196 15 0.1935956 0.05437164
#> 197 15 0.1935956 0.05437164
#> 198 15 0.1935956 0.05437164
#> 199 15 0.1935956 0.05437164
#> 200 15 0.1935956 0.05437164
#> 201 15 0.1935956 0.05437164
#> 202 15 0.1935956 0.05437164
#> 203 15 0.1935956 0.05437164
#> 204 15 0.1935956 0.05437164
#> 205 15 0.1935956 0.05437164
#> 206 15 0.1935956 0.05437164
#> 207 15 0.1935956 0.05437164
#> 208 15 0.1935956 0.05437164
#> 209 15 0.1935956 0.05437164
#> 210 15 0.1935956 0.05437164
#> 211 15 0.1935956 0.05437164
#> 212 15 0.1935956 0.05437164
#> 213 15 0.1935956 0.05437164
#> 214 15 0.1935956 0.05437164
#> 215 15 0.1935956 0.05437164
#> 216 15 0.2760705 0.16748723
#> 217 15 0.2760705 0.16748723
#> 218 15 0.2760705 0.16748723
#> 219 15 0.2760705 0.16748723
#> 220 15 0.2760705 0.16748723
#> 221 15 0.2760705 0.16748723
#> 222 15 0.2760705 0.16748723
#> 223 15 0.2760705 0.16748723
#> 224 15 0.2760705 0.16748723
#> 225 15 0.2760705 0.16748723
#> 226 15 0.2760705 0.16748723
#> 227 15 0.2760705 0.16748723
#> 228 15 0.2760705 0.16748723
#> 229 15 0.2760705 0.16748723
#> 230 15 0.2760705 0.16748723
#> 231 15 0.2760705 0.16748723
#> 232 15 0.2760705 0.16748723
#> 233 15 0.2760705 0.16748723
#> 234 15 0.2760705 0.16748723
#> 235 15 0.2760705 0.16748723
#> 236 15 0.2760705 0.16748723
#> 237 15 0.2760705 0.16748723
#> 238 15 0.2760705 0.16748723
#> 239 15 0.2760705 0.16748723
#> 240 15 0.2760705 0.16748723
#> 241 15 0.2760705 0.16748723
#> 242 15 0.2760705 0.16748723
#> 243 15 0.2760705 0.16748723
#> 244 15 0.2760705 0.16748723
#> 245 15 0.2760705 0.16748723
#> 246 15 0.2760705 0.16748723
#> 247 15 -0.5170736 0.59219044
#> 248 15 -0.5170736 0.59219044
#> 249 15 -0.5170736 0.59219044
#> 250 15 -0.5170736 0.59219044
#> 251 15 -0.5170736 0.59219044
#> 252 15 -0.5170736 0.59219044
#> 253 15 -0.5170736 0.59219044
#> 254 15 -0.5170736 0.59219044
#> 255 15 -0.5170736 0.59219044
#> 256 15 -0.5170736 0.59219044
#> 257 15 -0.5170736 0.59219044
#> 258 15 -0.5170736 0.59219044
#> 259 15 -0.5170736 0.59219044
#> 260 15 -0.5170736 0.59219044
#> 261 15 -0.5170736 0.59219044
#> 262 15 -0.5170736 0.59219044
#> 263 15 -0.5170736 0.59219044
#> 264 15 -0.5170736 0.59219044
#> 265 15 -0.5170736 0.59219044
#> 266 15 -0.5170736 0.59219044
#> 267 15 -0.5170736 0.59219044
#> 268 15 -0.5170736 0.59219044
#> 269 15 -0.5170736 0.59219044
#> 270 15 -0.5170736 0.59219044
#> 271 15 -0.5170736 0.59219044
#> 272 15 -0.5170736 0.59219044
#> 273 15 -0.5170736 0.59219044
#> 274 15 -0.5170736 0.59219044
#> 275 15 -0.5170736 0.59219044
#> 276 15 -0.5170736 0.59219044
#> 277 15 -0.5170736 0.59219044
#> 278 15 0.4187062 0.42788315
#> 279 15 0.4187062 0.42788315
#> 280 15 0.4187062 0.42788315
#> 281 15 0.4187062 0.42788315
#> 282 15 0.4187062 0.42788315
#> 283 15 0.4187062 0.42788315
#> 284 15 0.4187062 0.42788315
#> 285 15 0.4187062 0.42788315
#> 286 15 0.4187062 0.42788315
#> 287 15 0.4187062 0.42788315
#> 288 15 0.4187062 0.42788315
#> 289 15 0.4187062 0.42788315
#> 290 15 0.4187062 0.42788315
#> 291 15 0.4187062 0.42788315
#> 292 15 0.4187062 0.42788315
#> 293 15 0.4187062 0.42788315
#> 294 15 0.4187062 0.42788315
#> 295 15 0.4187062 0.42788315
#> 296 15 0.4187062 0.42788315
#> 297 15 0.4187062 0.42788315
#> 298 15 0.4187062 0.42788315
#> 299 15 0.4187062 0.42788315
#> 300 15 0.4187062 0.42788315
#> 301 15 0.4187062 0.42788315
#> 302 15 0.4187062 0.42788315
#> 303 15 0.4187062 0.42788315
#> 304 15 0.4187062 0.42788315
#> 305 15 0.4187062 0.42788315
#> 306 15 0.4187062 0.42788315
#> 307 15 0.4187062 0.42788315
#> 308 15 0.4187062 0.42788315collect_results() and the analysis= filter
When a trial has multiple named analyses (e.g., an interim and a
final), collect_results() returns rows for all of them. Use
the analysis argument to restrict to a specific name:
# Only the final analysis rows
collect_results(trials, analysis = "final")Each row in the output corresponds to one replicate firing one named
analysis. The replicate, timepoint, and
analysis columns identify the provenance of every result
row, making it straightforward to compare interim and final results
within the same replicate or aggregate across replicates.
Next steps
- Getting Started — end-to-end walkthrough of the six-step pattern shown above, with commentary on each design choice.
-
Enrollment & Dropout
Modeling — choose between stochastic (
gen_plan) and piecewise-constant (gen_timepoints) schedules. - Example 1 through Example 8 — progressively complex designs: correlated endpoints, time-to-event, multi-arm dose-finding, subgroup analyses, and Bayesian Go/No-Go rules.