---
title: "Power Computation for Group Sequential Survival Designs"
output: rmarkdown::html_vignette
vignette: >
  %\VignetteIndexEntry{Power Computation for Group Sequential Survival Designs}
  %\VignetteEngine{knitr::rmarkdown}
  %\VignetteEncoding{UTF-8}
---

```{r, include=FALSE}
library(gsDesign)
```

## Motivation

`gsSurv()` and `gsSurvCalendar()` derive sample sizes and enrollment rates
to achieve a target power for a given hazard ratio. In practice, we often
want to answer the reverse question: *given a specified hazard ratio, fixed enrollment, dropout, and
analysis timing assumptions, what power does the design achieve?*

Common scenarios include:

- **Sensitivity analysis**: What happens to power if the true hazard ratio
  is 0.75 instead of the design assumption of 0.65?
- **Changing alpha**: What if the multiplicity scheme initially allocates
  $\alpha = 0.0125$ and later allows $\alpha = 0.025$?
- **Modified enrollment**: What if enrollment is slower than planned?
- **Different analysis times**: What if interim analyses occur at calendar
  times that differ from the original design?

`gsSurvPower()` addresses these questions by computing power for a group
sequential survival design under user-specified assumptions.
For deriving the initial survival sample size design, see
`vignette("gsSurvBasicExamples")`. For reproducing SAS PROC SEQDESIGN survival
sample size output, see `vignette("SeqDesignSurvival")`.

## Quick start

If you already have a design object, the most common workflow is to reuse its
defaults and override only the assumptions you want to stress-test.

```{r gssurvpower_quick_start}
design <- gsSurv(
  k = 3, test.type = 4, alpha = 0.025, sided = 1, beta = 0.1,
  sfu = sfHSD, sfupar = -4, sfl = sfHSD, sflpar = -2,
  lambdaC = log(2) / 12, hr = 0.7, hr0 = 1,
  eta = 0.01, gamma = 10, R = 16, minfup = 12, T = 28
)

pwr_design <- gsSurvPower(x = design, plannedCalendarTime = design$T)
pwr_design$power
```

The returned object contains the same design-style structure as `gsSurv()`, but
with `variable = "Power"`. The most useful components to inspect first are:

- `pwr_design$power` for overall power.
- `pwr_design$n.I` for expected events at each analysis.
- `pwr_design$T` for analysis times.
- `pwr_design$timing` for information fractions.
- `pwr_design$upper$bound` and `pwr_design$lower$bound` for the bounds being
  applied.

## How gsSurvPower uses your inputs

`gsSurvPower()` accepts an optional `gsSurv`-class object `x`, including output
from `gsSurv()` and `gsSurvCalendar()`, that provides defaults for all
parameters. Any parameter the user explicitly specifies overrides the
corresponding value from `x`. When `x` is not provided, all design parameters
must be specified directly.

### Hazard ratio roles

Two distinct hazard ratios play different roles:

- **`hr`**: The assumed hazard ratio under which power is computed. This is the
  "what-if" treatment effect.
- **`hr1`**: The design hazard ratio used to calibrate futility bounds
  (`test.type` 3, 4, 7, 8 only; not used for `test.type` 5, 6 or harm bounds).
  When `x` is provided, `hr1` defaults to `x$hr` (the effect size the trial was
  originally designed for). Futility bounds remain calibrated to the design
  assumption even when power is evaluated under a different `hr`.

### Analysis timing: calendar time vs. event-driven

Analysis times can be specified by calendar time, by target event counts, or
by a combination of criteria. The choice has an important consequence for
sensitivity analyses:

- **`plannedCalendarTime`** fixes the calendar time of each analysis.
  Expected events are then recomputed under the assumed HR.
  A worse HR (closer to 1) produces *more* expected events at the same
  calendar time because the experimental arm fails faster.
  This gives an "unconditional" power that reflects how the assumed
  treatment effect influences event accrual.

- **`targetEvents`** fixes the event count at each analysis. The calendar
  time is the time until expected events reach the target under the
  assumed HR. Since the event counts are held
  constant, the information fractions do not change with HR, and the
  resulting power matches the `gsDesign` power plot (`plot(x, plottype = 2)`)
  to numerical precision.

Both modes are useful. Calendar-time analyses are natural when the protocol
specifies analysis dates; event-driven analyses are natural when the protocol
specifies event targets.

### Quick decision guide

| If the protocol fixes... | Use... | What changes in a sensitivity analysis |
| --- | --- | --- |
| Analysis dates | `plannedCalendarTime` | Expected events and information fractions |
| Event targets | `targetEvents` | Time until expected events reach those targets |

Additional criteria can be combined per-analysis, each specified as a scalar
(recycled to all `k` analyses) or a vector of length `k` with `NA` for
"not applicable":

- `maxExtension`: Maximum time beyond the floor to wait for target events.
- `minTimeFromPreviousAnalysis`: Minimum elapsed time since the previous analysis.
- `minN`: Minimum sample size enrolled before analysis.
- `minFollowUp`: Minimum follow-up after `minN` is reached.

When multiple criteria apply to a single analysis, the analysis time is the
maximum of all floor criteria, with `targetEvents` potentially extending
beyond the floor. `maxExtension` acts as a hard cap: the analysis time never
exceeds `plannedCalendarTime + maxExtension` (or `T[i-1] + maxExtension`
when no calendar time is specified), even if other criteria such as
`minTimeFromPreviousAnalysis` or `minN + minFollowUp` would push it later.

### Spending and method

- **`spending`**: One of `"information"` (default) or `"calendar"`.
  Information-based spending tracks the fraction of statistical information
  accumulated; calendar-based spending sets `usTime = lsTime = T / max(T)`.
  Custom spending times can also be passed via `usTime` and `lsTime`, but they
  are ignored when `spending = "calendar"`.

- **`informationRates`**: Planned information fractions (vector of
  length `k`). When provided, spending fractions are
  `pmin(informationRates, actual_timing)` at each analysis,
  preventing over-spending when events arrive faster than planned
  and under-spending when behind. This planned-vs-actual
  information scale takes precedence over `spending`, `usTime`,
  and `lsTime`; upper and lower spending both use the same capped vector.

- **`fullSpendingAtFinal`**: When `TRUE`, the final element of the
  spending-time vector is forced to 1 after applying
  `informationRates`, calendar spending, or user-supplied `usTime` /
  `lsTime`. This is useful when the selected spending-time vector would
  otherwise end below 1.

- **`method`**: One of `"LachinFoulkes"` (default), `"Schoenfeld"`,
  `"Freedman"`, or `"BernsteinLagakos"`. Controls how fixed-design events
  (`n.fix`) and drift parameter $\theta$ are computed when `x` is not provided.
  When `x` is provided, `x$n.fix` and, implicitly, $\theta$ are used directly for
  exact consistency with the design.

### Stratified targetEvents

`targetEvents` accepts a scalar (recycled), a vector of length `k`
(one overall target per analysis), or a matrix with `k` rows and
`nstrata` columns (per-stratum targets). A vector of length `k` is
always interpreted as overall targets; to specify per-stratum targets
for a single analysis, use a 1-row matrix.

## Power under alternative assumptions

Passing the design's analysis times with the same hazard ratio exactly
reproduces the design power (90%). Internally, `gsSurvPower()` uses `x$n.fix`
(when `x` is provided) so that the drift parameter $\theta$ and bounds are
normalized identically to `gsSurv()`. The assumed HR's drift is obtained
by method-specific scaling:

- For Schoenfeld, LachinFoulkes, and BernsteinLagakos:
  $\theta_{\text{assumed}} = \theta_{\text{design}} \times |\log(\text{hr}/\text{hr}_0)| / |\log(\text{hr}_1/\text{hr}_0)|$
- For Freedman:
  $\theta_{\text{assumed}} = \theta_{\text{design}} \times |\delta(\text{hr})| / |\delta(\text{hr}_1)|$
  where $\delta(\text{hr}) = (\text{hr} - 1) / (\text{hr} + 1/\text{ratio})$

This reproduces the design power exactly at `hr = hr1` and scales
correctly for other hazard ratios, matching `rpact::getPowerSurvival()`
to within 0.5% across methods.

```{r gssurvpower_at_design}
cat("Design power:", round((1 - design$beta) * 100, 1), "%\n")
cat("gsSurvPower:  ", round(pwr_design$power * 100, 1), "%\n")
```

### Power under a different hazard ratio

Suppose the true treatment effect is HR = 0.8 instead of the design
assumption of 0.7:

```{r gssurvpower_worse_hr}
pwr_worse <- gsSurvPower(x = design, hr = 0.8, plannedCalendarTime = design$T)
cat("Power at HR = 0.8:", round(pwr_worse$power * 100, 1), "%\n")
```

The futility bounds remain calibrated to the design HR (0.7), but power
is evaluated under the assumed HR (0.8).

### Power over a range of hazard ratios

```{r gssurvpower_sensitivity}
hr_grid <- seq(0.55, 0.90, by = 0.05)
power_vals <- sapply(hr_grid, function(h) {
  p <- gsSurvPower(x = design, hr = h, plannedCalendarTime = design$T)
  p$power
})
results <- data.frame(HR = hr_grid, Power = round(power_vals * 100, 1))
results
```

## Multiple timing criteria

Real trials often use multiple criteria for analysis timing. In practice,
a protocol may specify target event counts, planned calendar times,
minimum follow-up after enrollment completes, and caps on how long
analyses can be delayed. `gsSurvPower()` lets you combine all of these
in a single call. Approximations are based on expected event accumulation under the assumed HR.
Thus, computations do not take into account the stochastic variability in event accrual.
While `gsSurvPower()` should be adequate for most purposes, verification
for key scenarios should consider simulation using the **simtrial** R
package.

### Setup

The design enrolls at rate `r round(design$gamma, 1)` patients/month for
`r sum(design$R)` months (`r floor(sum(design$gamma * design$R))` patients total),
with a 12-month control median and HR = 0.7.
The three analyses are planned at months
`r paste(round(design$T, 1), collapse = ", ")`,
so IA1 occurs *before* planned enrollment completion and IA2/FA occur *after*.

We layer four criteria:

| Criterion | IA1 | IA2 | FA |
|-----------|-----|-----|-----|
| `plannedCalendarTime` | `r round(design$T[1],1)` | `r round(design$T[2],1)` | `r round(design$T[3],1)` |
| `targetEvents` | `r round(design$n.I[1],1)` | `r round(design$n.I[2],1)` | `r round(design$n.I[3],1)` |
| `minN + minFollowUp` | — | all enrolled + 2 mo | all enrolled + 12 mo |
| `maxExtension` | 3 mo | 12 mo | 20 mo |

The floor for each analysis is the *latest* of the applicable
`plannedCalendarTime`, `minN + minFollowUp`, and previous-analysis
constraints. If expected events have not reached the target at the
floor, the analysis can be extended until the time that expected
events reach the target, up to `plannedCalendarTime + maxExtension`.

```{r timing_setup}
total_N <- floor(sum(design$gamma * design$R))
```

### Baseline: design assumptions

Under design assumptions the criteria reproduce the planned timing
exactly. The `minN + minFollowUp` floor for IA2 is
`r sum(design$R)` + 2 = `r sum(design$R) + 2` months, which is below
the planned time of `r round(design$T[2], 1)` months, so the planned
calendar time drives IA2. The FA floor from `minN + minFollowUp`
is `r sum(design$R)` + 12 = 28, which coincides with the planned time.
Since the design's expected event counts match the targets,
the `maxExtension` is not needed and the result matches the design power.

```{r timing_baseline}
pwr_multi <- gsSurvPower(
  x = design,
  targetEvents = design$n.I,
  plannedCalendarTime = design$T,
  minN = c(NA, total_N, total_N),
  minFollowUp = c(NA, 2, 12),
  maxExtension = c(3, 12, 20)
)

data.frame(
  Analysis = 1:design$k,
  Planned_Time = round(design$T, 1),
  Actual_Time = round(pwr_multi$T, 1),
  Target_Events = round(design$n.I, 1),
  Actual_Events = round(pwr_multi$n.I, 1)
)
cat("Power:", round(pwr_multi$power * 100, 1), "%\n")
```

### Scenario 1: slower enrollment

If enrollment proceeds at half the planned rate, it takes
`r 2 * sum(design$R)` months to reach `r total_N` patients.
If we simply target the same sample size (double the duration of enrollment) 
and event targets, we will extend the expected timing of analyses from 
`r round(design$T, 1)` months but have the same power and bounds.

```{r timing_slow_enrollment_simple}
pwr_slow_simple <- gsSurvPower(
  x = design,
  gamma = design$gamma / 2,
  targetN = total_N,
  targetEvents = design$n.I
)


pwr_slow_simple |> gsBoundSummary()
```


With `maxExtension = c(3, 12, 20)`, each analysis can extend
beyond `plannedCalendarTime` by the specified amount while waiting
for expected events to reach the target—but never past the cap.
The `minN + minFollowUp` floor for IA2 is
`r 2 * sum(design$R)` + 2 = `r 2 * sum(design$R) + 2` months,
far beyond `plannedCalendarTime[2] + maxExtension[2]`, so the cap
overrides. Note that `maxExtension` is always measured from
`plannedCalendarTime`, not from the floor, so it acts as a hard
deadline on how long the sponsor is willing to wait.

```{r timing_slow_enrollment}
pwr_slow <- gsSurvPower(
  x = design,
  gamma = design$gamma / 2,
  targetEvents = design$n.I,
  plannedCalendarTime = design$T,
  minN = c(NA, total_N, total_N),
  minFollowUp = c(NA, 2, 12),
  maxExtension = c(3, 12, 20)
)

data.frame(
  Analysis = 1:design$k,
  Planned_Time = round(design$T, 1),
  Actual_Time = round(pwr_slow$T, 1),
  Target_Events = round(design$n.I, 1),
  Actual_Events = round(pwr_slow$n.I, 1)
)
cat("Power:", round(pwr_slow$power * 100, 1), "%\n")
```

Each analysis is capped by `plannedCalendarTime + maxExtension`.
The final analysis achieves
only `r round(pwr_slow$n.I[design$k], 0)` of the targeted
`r round(design$n.I[design$k], 0)` events, and power drops from 90%
to `r round(pwr_slow$power * 100, 1)`%.

### Scenario 2: higher control failure rate

If the control median is 8 months instead of 12, events accumulate
faster. Expected events reach the target well before the planned
calendar times, so the `plannedCalendarTime` floor determines the
analysis schedule. The trial over-runs its event targets
substantially, yielding higher-than-planned power.

```{r timing_fast_failure}
pwr_fast <- gsSurvPower(
  x = design,
  lambdaC = log(2) / 8,
  targetEvents = design$n.I,
  plannedCalendarTime = design$T,
  minN = c(NA, total_N, total_N),
  minFollowUp = c(NA, 2, 12),
  maxExtension = c(3, 12, 20)
)

data.frame(
  Analysis = 1:design$k,
  Planned_Time = round(design$T, 1),
  Actual_Time = round(pwr_fast$T, 1),
  Target_Events = round(design$n.I, 1),
  Actual_Events = round(pwr_fast$n.I, 1)
)
cat("Power:", round(pwr_fast$power * 100, 1), "%\n")
```

The final analysis collects `r round(pwr_fast$n.I[design$k], 0)` events
vs. the target of `r round(design$n.I[design$k], 0)`, and power rises to
`r round(pwr_fast$power * 100, 1)`%.

### Controlling spending with informationRates

When events fall short of the target (as in Scenario 1), the actual
information fraction at each analysis may be lower than planned.
By default, bounds are computed at the actual information fractions.
The `informationRates` parameter lets you cap spending at
`pmin(informationRates, actual_timing)` — preventing over-spending
if events arrive faster than planned, and under-spending if they
arrive slower. This is useful when the protocol pre-specifies
spending based on planned information fractions. If `informationRates`
is supplied, this information-based cap takes precedence over
`spending = "calendar"` and over manual `usTime` / `lsTime` overrides.

Setting `fullSpendingAtFinal = TRUE` forces the spending fraction
at the final analysis to 1 after the capped spending fractions are
computed. The example below uses a final planned spending fraction of
0.95 to show the effect explicitly. Without `fullSpendingAtFinal`,
the final spending fraction would remain 0.95 rather than 1.

```{r informationRates_demo}
# Scenario 1 with informationRates and fullSpendingAtFinal
planned_info_rates <- c(design$timing[-design$k], 0.95)

pwr_slow_ir <- gsSurvPower(
  x = design,
  gamma = design$gamma / 2,
  targetEvents = design$n.I,
  plannedCalendarTime = design$T,
  minN = c(NA, total_N, total_N),
  minFollowUp = c(NA, 2, 12),
  maxExtension = c(3, 12, 20),
  informationRates = planned_info_rates,
  fullSpendingAtFinal = TRUE
)

spending_frac_used <- pmin(planned_info_rates, pwr_slow_ir$timing)
spending_frac_used[design$k] <- 1

data.frame(
  Analysis = 1:design$k,
  Actual_Events = round(pwr_slow_ir$n.I, 1),
  Actual_InfoFrac = round(pwr_slow_ir$timing, 3),
  Planned_InfoFrac = round(planned_info_rates, 3),
  Spending_Frac = round(spending_frac_used, 3)
)
cat("Power (default spending):       ", round(pwr_slow$power * 100, 1), "%\n")
cat("Power (capped + full final):    ", round(pwr_slow_ir$power * 100, 1), "%\n")
```

With `fullSpendingAtFinal = TRUE`, the final spending fraction is 1
even though the capped planned-vs-actual fraction would otherwise be
0.95. This produces slightly different final bounds compared to the
same `informationRates` specification with `fullSpendingAtFinal = FALSE`.

## Comparison with gsDesign power plots

The `gsDesign` package provides power plots via `plot(design, plottype = 2)`.
These hold event counts fixed at the design values and vary only the drift
parameter $\theta$. The table below compares three approaches across a range
of hazard ratios:

- **gsDesign**: `gsProbability()` with design bounds, design events,
  scaled $\theta$. This is what `plot(design, plottype = 2)` computes.
- **gsSurvPower (fixed events)**: `gsSurvPower()` with
  `targetEvents = design_events`. Events are held constant; calendar
  times adjust.
- **gsSurvPower (fixed calendar)**: `gsSurvPower()` with
  `plannedCalendarTime = design$T`. Calendar times are held constant;
  events change with the assumed HR.

```{r gssurvpower_vs_gsdesign}
design_events <- design$n.I
hr_grid <- seq(0.55, 0.95, by = 0.05)

comparison <- data.frame(
  HR = hr_grid,
  gsDesign_plot = sapply(hr_grid, function(h) {
    delta_ratio <- abs(log(h)) / abs(log(design$hr))
    theta_h <- design$delta * delta_ratio
    gsp <- gsDesign::gsProbability(
      k = design$k, theta = theta_h,
      n.I = design$n.I,
      a = design$lower$bound, b = design$upper$bound, r = 18)
    sum(gsp$upper$prob)
  }),
  fixed_events = sapply(hr_grid, function(h) {
    gsSurvPower(x = design, hr = h, targetEvents = design_events)$power
  }),
  fixed_calendar = sapply(hr_grid, function(h) {
    gsSurvPower(x = design, hr = h, plannedCalendarTime = design$T)$power
  })
)
comparison[, -1] <- round(comparison[, -1] * 100, 2)
comparison
```

**Key observations:**

- The `gsDesign_plot` and `fixed_events` columns match to numerical
  precision because both condition on the same event counts at each
  analysis. When using `targetEvents`, `gsSurvPower()` reproduces the
  `gsDesign` power plot exactly.

- The `fixed_calendar` column differs modestly because fixing calendar
  times allows the expected event count to change with the assumed HR.
  A worse HR (closer to 1) produces more expected events at the same
  calendar time, since the experimental arm has a higher failure rate.
  This slightly changes the statistical information at each analysis,
  producing an "unconditional" power that accounts for the interplay
  between treatment effect and event accrual.

### Bounds stability

When using `targetEvents`, the efficacy and futility bounds do not change
with the assumed HR. The bounds are determined entirely by the design
parameters (alpha/beta spending, information fractions, `n.fix`) and are
reused directly from the input design `x` when the timing matches:

```{r gssurvpower_bounds}
design_events <- design$n.I
cat("Design bounds (Z-scale):\n")
cat("  Efficacy:", round(design$upper$bound, 4), "\n")
cat("  Futility:", round(design$lower$bound, 4), "\n\n")

for (h in c(0.5, 0.7, 0.8, 1.0)) {
  pwr <- gsSurvPower(x = design, hr = h, targetEvents = design_events)
  cat(sprintf("HR=%.1f  Efficacy: %s  Futility: %s  (identical: %s)\n",
    h,
    paste(round(pwr$upper$bound, 4), collapse = ", "),
    paste(round(pwr$lower$bound, 4), collapse = ", "),
    identical(pwr$upper$bound, design$upper$bound) &&
      identical(pwr$lower$bound, design$lower$bound)))
}
```

With `plannedCalendarTime`, different assumed HRs produce different expected
event counts and therefore different information fractions, so the bounds
are appropriately recomputed via `gsDesign::gsDesign()`.

## Changing alpha

A common use case is evaluating power at a different one-sided alpha level —
for example, when a graphical multiplicity procedure initially allocates
$\alpha = 0.0125$ to one hypothesis and later, after another hypothesis is
rejected, propagates alpha so that $\alpha = 0.025$ is available.

Here we design at $\alpha = 0.0125$ and then ask: what is the power if
we can test at $\alpha = 0.025$?

When `x` is provided and the information fractions (timing) match the
original design, `gsSurvPower()` recalculates **efficacy bounds** at the
new alpha using `gsDesign(test.type = 1)` (efficacy-only) while
**preserving the original futility bounds** from `x`. This follows the
same convention as `gsBoundSummary()`. Any futility bound that would
exceed the new efficacy bound is clipped.

When timing changes (e.g., different `targetEvents`), both bounds are
recomputed from scratch using the full `test.type` and spending functions.

```{r alpha_change}
# Design at one-sided alpha = 0.0125
design_a0125 <- gsSurv(
  k = 3, test.type = 4, alpha = 0.0125, sided = 1, beta = 0.1,
  sfu = sfHSD, sfupar = -4, sfl = sfHSD, sflpar = -2,
  lambdaC = log(2) / 12, hr = 0.7, hr0 = 1,
  eta = 0.01, gamma = 10, R = 16, minfup = 12, T = 28
)

cat("=== Original design (alpha = 0.0125) ===\n")
cat("Efficacy bounds:", round(design_a0125$upper$bound, 4), "\n")
cat("Futility bounds:", round(design_a0125$lower$bound, 4), "\n\n")

# Power at alpha = 0.025 with same event counts (timing preserved)
events_a0125 <- design_a0125$n.I
pwr_a025 <- gsSurvPower(x = design_a0125, alpha = 0.025, targetEvents = events_a0125)

cat("=== gsSurvPower at alpha = 0.025 ===\n")
cat("Efficacy bounds:", round(pwr_a025$upper$bound, 4), "\n")
cat("Futility bounds:", round(pwr_a025$lower$bound, 4), "\n")
cat("Power:          ", round(pwr_a025$power * 100, 1), "%\n\n")

# Cross-check: gsBoundSummary at the same alternate alpha
# (only test.type 1, 4, 6, 7, 8 are supported)
cat("=== gsBoundSummary (alpha = 0.025) ===\n")
print(gsBoundSummary(design_a0125, alpha = 0.025))
```

Note that `gsBoundSummary()` adds an $\alpha = 0.025$ column for
`test.type` 1, 4, 6, 7, and 8. For binding types (3, 5),
`gsBoundSummary()` does not support alternate alpha, but
`gsSurvPower()` handles them using the same approach: recompute efficacy
with `test.type = 1` at the new alpha and keep original futility bounds.

### Binding type example (test.type = 3)

```{r alpha_binding}
design3 <- gsSurv(
  k = 3, test.type = 3, alpha = 0.0125, sided = 1, beta = 0.1,
  sfu = sfHSD, sfupar = -4, sfl = sfHSD, sflpar = -2,
  lambdaC = log(2) / 12, hr = 0.7, eta = 0.01,
  gamma = 10, R = 16, minfup = 12, T = 28
)
events3 <- design3$n.I

pwr3_a025 <- gsSurvPower(x = design3, alpha = 0.025, targetEvents = events3)

cat("=== Binding futility (test.type=3) at alpha = 0.025 ===\n")
cat("Original efficacy:", round(design3$upper$bound, 4), "\n")
cat("New efficacy:     ", round(pwr3_a025$upper$bound, 4), "\n")
cat("Original futility:", round(design3$lower$bound, 4), "\n")
cat("New futility:     ", round(pwr3_a025$lower$bound, 4), "\n")
cat("Power:            ", round(pwr3_a025$power * 100, 1), "%\n")
```

The futility bounds are preserved from the original design. At the final
analysis where the original futility bound equals the original efficacy
bound, the new efficacy bound (which is lower) becomes the clip point.

The output retains the original `test.type` and records the new `alpha`:

```{r alpha_output_fields}
cat("test.type:", pwr3_a025$test.type, "(same as input design)\n")
cat("alpha:    ", pwr3_a025$alpha, "(updated to new value)\n")
```

## Example: event-based timing

Instead of calendar times, analyses can be triggered by target event counts:

```{r gssurvpower_events}
pwr_events <- gsSurvPower(
  x = design,
  targetEvents = c(75, 150, 225)
)
cat("Analysis times:", round(pwr_events$T, 1), "\n")
cat("Events at each analysis:",
    round(pwr_events$n.I, 1), "\n")
cat("Power:", round(pwr_events$power * 100, 1), "%\n")
```

## Example: slower enrollment at fixed analysis times

Another common sensitivity analysis is slower-than-planned enrollment. When
calendar analysis times are fixed, slower enrollment reduces the expected number
of events available at each look and therefore reduces power.

```{r gssurvpower_slower_enrollment}
pwr_slow_enroll <- gsSurvPower(
  x = design,
  gamma = design$gamma / 2,
  plannedCalendarTime = design$T
)

cat("Original final expected events:", round(pwr_design$n.I[design$k], 1), "\n")
cat("Slower-enrollment final events:", round(pwr_slow_enroll$n.I[design$k], 1), "\n")
cat("Original power:", round(pwr_design$power * 100, 1), "%\n")
cat("Slower-enrollment power:", round(pwr_slow_enroll$power * 100, 1), "%\n")
```

## Example: calendar-based spending

By default, alpha and beta spending track statistical **information fractions**
(`n.I / max(n.I)`). Setting `spending = "calendar"` instead ties spending to
calendar time fractions (`T / max(T)`).
This distinction matters when analysis times are unevenly spaced:
event accrual is slow early in the trial (enrollment is ongoing),
so by the time one-third of the statistical information has accumulated
the trial is already well past one-third of its calendar duration.
The first analysis in this design occurs about 12.4 months into a
28-month trial—an information fraction of 0.333 but a calendar fraction
of 0.444. Calendar spending therefore spends more alpha early,
producing a less conservative interim efficacy bound and a slightly
more conservative final bound.

When `spending = "calendar"`, any user-supplied `usTime` and `lsTime`
overrides are **ignored**; the realized analysis times determine spending
fractions automatically.

```{r calendar_spending}
# Information-based spending (default)
pwr_info <- gsSurvPower(
  x = design,
  plannedCalendarTime = design$T,
  spending = "information"
)

# Calendar-based spending
pwr_cal <- gsSurvPower(
  x = design,
  plannedCalendarTime = design$T,
  spending = "calendar"
)

# Compare spending fractions and bounds
data.frame(
  Analysis = 1:design$k,
  Calendar_Time = round(pwr_info$T, 1),
  InfoFraction = round(pwr_info$timing, 3),
  CalendarFraction = round(pwr_cal$T / max(pwr_cal$T), 3),
  Bound_Info = round(pwr_info$upper$bound, 4),
  Bound_Calendar = round(pwr_cal$upper$bound, 4)
)
```

At analysis 1 (month ~12), the calendar fraction (0.444) substantially
exceeds the information fraction (0.333). Calendar spending allocates
more alpha to this look, so the efficacy bound drops from 3.01 to 2.84—
a meaningful difference for interim decision-making. By the final
analysis the bounds nearly converge because both fractions equal 1.

Note that passing `usTime` or `lsTime` with `spending = "calendar"`
has no effect—the calendar fractions override them:

```{r calendar_ignores_usTime}
pwr_cal_override <- gsSurvPower(
  x = design,
  plannedCalendarTime = design$T,
  spending = "calendar",
  usTime = c(0.2, 0.6, 1),
  lsTime = c(0.3, 0.8, 1)
)

# Bounds are identical regardless of usTime/lsTime
identical(pwr_cal$upper$bound, pwr_cal_override$upper$bound)
```

## Example: stratified event targets

When a trial enrolls patients from multiple strata with different event
rates, you may want to specify per-stratum event targets rather than a
single overall number. `targetEvents` accepts a matrix with `k` rows
(analyses) and `nstrata` columns (strata). Row sums give the overall
target used to solve each analysis time.

Consider a two-stratum design where stratum 1 has median survival of
6 months and stratum 2 has 12 months. We target 30 events (20 + 10) at
the interim and 60 events (40 + 20) at the final analysis:

```{r stratified_events}
# Per-stratum event targets: rows = analyses, columns = strata
event_matrix <- matrix(
  c(20, 10,   # interim: 20 from stratum 1, 10 from stratum 2
    40, 20),  # final:   40 from stratum 1, 20 from stratum 2
  nrow = 2, byrow = TRUE
)

pwr_strat <- gsSurvPower(
  k = 2, test.type = 1, alpha = 0.025, sided = 1,
  lambdaC = matrix(log(2) / c(6, 12), ncol = 2),
  hr = 0.7, eta = 0.01,
  gamma = matrix(c(5, 5), ncol = 2), R = 12, ratio = 1,
  targetEvents = event_matrix
)

# The analysis times are solved so that total expected events
# match the row sums of the target matrix
data.frame(
  Analysis = 1:2,
  Target_Stratum1 = event_matrix[, 1],
  Target_Stratum2 = event_matrix[, 2],
  Target_Total = rowSums(event_matrix),
  Expected_Events = round(pwr_strat$n.I, 1),
  Calendar_Time = round(pwr_strat$T, 1)
)

cat("Power:", round(pwr_strat$power * 100, 1), "%\n")
```

The matrix format is also used in the biomarker example below,
where `lambdaC` and `gamma` vary by stratum but `plannedCalendarTime`
drives the timing.

## Example: biomarker subgroup to stratified design

A common scenario is designing a trial for a biomarker-defined subgroup,
then assessing what power the same enrollment provides for the overall
(stratified) population under a more conservative treatment effect.
Note that the `gsDesign2` package could be used to design with different
hazard ratios in the biomarker-positive and biomarker-negative populations
simultaneously; here we illustrate the simpler approach using `gsSurvPower()`.

### Step 1: Design for the biomarker-positive subgroup

Suppose 60% of the population is biomarker-positive (prevalence = 0.6),
the control median survival in this subgroup is 12 months,
the hazard ratio is 0.65, and we target 90% power at one-sided
$\alpha = 0.0125$ (e.g., from a graphical multiplicity allocation):

```{r biomarker_design}
prevalence <- 0.6
median_bm_pos <- 12    # control median in biomarker+ (months)
median_bm_neg <- 10    # control median in biomarker- (shorter prognosis)

bm_design <- gsSurvCalendar(
  test.type = 4, alpha = 0.0125, beta = 0.1,
  sfu = sfHSD, sfupar = -4, sfl = sfHSD, sflpar = -2,
  calendarTime = c(12, 24, 36),
  lambdaC = log(2) / median_bm_pos, hr = 0.65, eta = 0.01,
  gamma = 10, R = 18, minfup = 18, ratio = 1
)

summary(bm_design)
gsBoundSummary(bm_design)
```

### Step 2: Power for the overall (stratified) population

Now consider enrolling the entire population using the same enrollment
duration and analysis calendar times. The biomarker-positive enrollment
rate comes from the subgroup design. The biomarker-negative enrollment
rate is proportionate based on prevalence: if biomarker-positive patients
enroll at rate $\gamma_{+}$, biomarker-negative patients enroll at rate
$\gamma_{-} = \gamma_{+} \times (1 - p) / p$.

We use a **stratified** approach: `lambdaC` and `gamma` are specified as
matrices with two columns (one per stratum), allowing different control
hazard rates and enrollment rates in each biomarker subgroup.
The overall hazard ratio assumed is 0.75 (attenuated because the
biomarker-negative subgroup has a weaker treatment effect).

```{r stratified_power}
# Control hazard rates by stratum
lambdaC_pos <- log(2) / median_bm_pos
lambdaC_neg <- log(2) / median_bm_neg

# Enrollment rates by stratum (proportionate to prevalence)
gamma_pos <- bm_design$gamma
gamma_neg <- bm_design$gamma * (1 - prevalence) / prevalence

# Stratified inputs: matrix with columns = strata
lambdaC_strat <- matrix(c(lambdaC_pos, lambdaC_neg), ncol = 2)
gamma_strat <- matrix(c(gamma_pos, gamma_neg), ncol = 2)

pwr_overall <- gsSurvPower(
  k = 3, test.type = 4, alpha = 0.0125, sided = 1,
  sfu = sfHSD, sfupar = -4, sfl = sfHSD, sflpar = -2,
  lambdaC = lambdaC_strat, hr = 0.75, eta = 0.01,
  gamma = gamma_strat, R = 18, ratio = 1,
  plannedCalendarTime = c(12, 24, 36)
)

summary(pwr_overall)
gsBoundSummary(pwr_overall)
```

The overall design enrolls more patients (the full population rather than
just the 60% biomarker-positive subgroup) and has a higher event rate
in the biomarker-negative stratum (shorter control median), but assumes a
weaker overall treatment effect (HR = 0.75 vs. 0.65).