# Re-estimating the Sample Size from Interim Data

#### Gosuke Homma

#### 2026-08-20

    library(bbssr)

## Two questions, two functions

`BinaryPowerBSSR()` answers a design question. Given a re-estimation rule, what is the power and the expected sample size across the scenarios that might occur? It averages over every possible interim outcome, and it is used before the trial starts.

`BinaryBSSR()` answers an operational question. The interim analysis has produced one specific number, so how many more patients must be enrolled? It is used once, while the trial is running.

This vignette follows a single trial through both.

## Planning

A trial compares an experimental treatment against control with a binary response endpoint. The clinical team assumes a response probability of 0.45 under treatment and 0.20 under control, so a risk difference of 0.25. Randomization is one to one, the one-sided level is 0.025 and the target power is 0.80. The Z-pooled exact unconditional test is chosen.

    Delta.A <- 0.25
    plan <- BinarySampleSize(p1 = 0.45, p2 = 0.20, r = 1, alpha = 0.025,
                             tar.power = 0.8, Test = 'Z-pool')
    plan
    #> Sample size for a two-arm trial with a binary endpoint
    #>
    #>   Test             : Z-pool
    #>   Alternative      : greater
    #>   Response rates   : p1 = 0.45, p2 = 0.2
    #>   Allocation ratio : 1 to 1
    #>   Alpha            : 0.025
    #>   Target power     : 0.8
    #>
    #>   Required sample size: N1 = 54, N2 = 54, total N = 108
    #>   Attained power      : 0.8038

The assumed pooled response probability is 0.325. Its value drives the required sample size much more strongly than the risk difference alone, which is what makes it worth re-estimating.

    pooled <- seq(0.20, 0.45, by = 0.05)
    sens <- data.frame(
      pooled = pooled,
      p1 = pooled + Delta.A / 2,
      p2 = pooled - Delta.A / 2
    )
    sens$N <- vapply(seq_len(nrow(sens)), function(i) {
      BinarySampleSize(sens$p1[i], sens$p2[i], 1, 0.025, 0.8, 'Chisq')$N
    }, numeric(1))
    sens
    #>   pooled    p1    p2   N
    #> 1   0.20 0.325 0.075  74
    #> 2   0.25 0.375 0.125  88
    #> 3   0.30 0.425 0.175 102
    #> 4   0.35 0.475 0.225 110
    #> 5   0.40 0.525 0.275 120
    #> 6   0.45 0.575 0.325 120

The required total sample size rises by about 60 percent across this range, while the risk difference stays fixed at 0.25. The reason is that the variance of a binary endpoint is largest at one half, so a pooled probability closer to that value costs patients. An error in the assumed pooled probability is therefore expensive, and it is exactly the quantity that blinded interim data can estimate.

## Evaluating the re-estimation rule

Before committing to the design, check what the re-estimation buys. The interim analysis is placed at half of the planned sample size.

    design <- BinaryPowerBSSR(
      p = seq(0.20, 0.45, by = 0.05),
      Delta.A = Delta.A, Delta.T = Delta.A,
      N1 = plan$N1, N2 = plan$N2, omega = 0.5, r = 1,
      alpha = 0.025, tar.power = 0.8, Test = 'Chisq',
      restricted = TRUE
    )
    design
    #> Blinded sample size re-estimation for a binary endpoint
    #>
    #>   Test            : Chisq
    #>   Alternative     : greater
    #>   Design rule     : restricted
    #>   Initial size    : N1 = 54, N2 = 54
    #>   Interim fraction: 0.5, giving n1 = 27 and n2 = 27
    #>   Treatment effect: assumed 0.25, true 0.25
    #>   Alpha           : 0.025, target power 0.8
    #>
    #>     p    p1    p2 power.BSSR power.TRAD   E.N
    #>  0.20 0.325 0.075     0.9284     0.9283 108.0
    #>  0.25 0.375 0.125     0.8710     0.8703 108.3
    #>  0.30 0.425 0.175     0.8263     0.8222 109.6
    #>  0.35 0.475 0.225     0.8058     0.7937 112.5
    #>  0.40 0.525 0.275     0.7975     0.7630 116.0
    #>  0.45 0.575 0.325     0.7971     0.7361 118.5

    plot(design)

The fixed-sample design was sized at a pooled probability of 0.325. It keeps its power at and below that value and loses it above, falling further behind as the pooled probability approaches one half. Under the restricted rule the re-estimation never goes below the planned sample size, so it leaves the power alone in the over-powered scenarios and adds patients in the under-powered ones. The `E.N` column shows what that costs.

## The interim analysis

Enrolment reaches the interim milestone. The unblinded statistician is not involved. The only number extracted from the database is the total count of responders across both groups.

    n1 <- ceiling(plan$N1 / 2)
    n2 <- ceiling(plan$N2 / 2)
    S <- round(0.40 * (n1 + n2))
    data.frame(n1 = n1, n2 = n2, S = S, pooled.rate = round(S / (n1 + n2), 3))
    #>   n1 n2  S pooled.rate
    #> 1 27 27 22       0.407

The observed pooled rate is above the assumed 0.325. By the argument of the previous section a higher pooled rate at the same risk difference calls for more patients, so the trial has to grow.

    re <- BinaryBSSR(
      n1 = n1, n2 = n2, S = S,
      Delta.A = Delta.A, r = 1,
      alpha = 0.025, tar.power = 0.8, Test = 'Z-pool',
      restricted = TRUE, N1 = plan$N1, N2 = plan$N2
    )
    re
    #> Blinded sample size re-estimation from interim data
    #>
    #>   Test             : Z-pool
    #>   Alternative      : greater
    #>   Design rule      : restricted
    #>   Assumed effect   : 0.25
    #>   Alpha            : 0.025, target power 0.8
    #>
    #> Interim data
    #>   Patients         : n1 = 27, n2 = 27, total n = 54
    #>   Responders       : S = 22, blinded pooled rate = 0.4074
    #>   Recovered rates  : hat.p1 = 0.5324, hat.p2 = 0.2824
    #>
    #> Re-estimation
    #>   Required total   : N1 = 60, N2 = 60, total N = 120
    #>   Still to enrol   : group 1 = 33, group 2 = 33, total = 66
    #>   Final size       : N1 = 60, N2 = 60, total N = 120
    #>   Power at final N : 0.8029

The output separates three quantities that are easy to confuse. `N1.re` and `N2.re` are the total sample sizes the re-estimation calls for. `n1.stage2` and `n2.stage2` are the additional patients still to enrol, which is what the enrolment plan needs. `N1.final` and `N2.final` are what the trial will end up with, and they can exceed the re-estimated totals when the restricted rule applies or when the interim sample size already exceeds the requirement.

## What the decision looks like across interim outcomes

Running the same call over the possible values of \(S\) shows the shape of the rule.

    S.grid <- round(seq(0.15, 0.55, by = 0.05) * (n1 + n2))
    decision <- data.frame(S = S.grid, pooled = round(S.grid / (n1 + n2), 3))
    decision$N.restricted <- vapply(S.grid, function(s) {
      BinaryBSSR(n1, n2, s, Delta.A, 1, 0.025, 0.8, 'Chisq',
                 restricted = TRUE, N1 = plan$N1, N2 = plan$N2)$N.final
    }, numeric(1))
    decision$N.unrestricted <- vapply(S.grid, function(s) {
      BinaryBSSR(n1, n2, s, Delta.A, 1, 0.025, 0.8, 'Chisq')$N.final
    }, numeric(1))
    decision
    #>    S pooled N.restricted N.unrestricted
    #> 1  8  0.148          108             54
    #> 2 11  0.204          108             76
    #> 3 14  0.259          108             94
    #> 4 16  0.296          108            102
    #> 5 19  0.352          110            110
    #> 6 22  0.407          120            120
    #> 7 24  0.444          120            120
    #> 8 27  0.500          120            120
    #> 9 30  0.556          120            120

The restricted curve is flat at the planned total until the interim data are unfavourable enough to trigger an increase. The unrestricted curve dips below the plan when the interim data are favourable. The two curves meet once the required sample size exceeds the plan.

## Truncation at the boundary

When the blinded pooled rate is extreme, the recovered group probabilities can fall outside the unit interval and are truncated.

    edge <- BinaryBSSR(n1 = 20, n2 = 20, S = 2, Delta.A = 0.4, r = 1,
                       alpha = 0.025, tar.power = 0.8, Test = 'Chisq')
    data.frame(hat.p = edge$hat.p, hat.p1 = edge$hat.p1,
               hat.p2 = edge$hat.p2, N.re = edge$N.re)
    #>   hat.p hat.p1 hat.p2 N.re
    #> 1  0.05   0.25      0   42

With a pooled rate of 0.05 and an assumed effect of 0.4, the control probability would be negative, so it is set to zero while the treatment probability stays at \(\hat{p} + \Delta_{A} / 2\). The recovered risk difference is then only 0.25 rather than 0.4, and the re-estimated sample size is correspondingly larger. An assumed effect that the observed pooled rate cannot accommodate therefore produces conservative re-estimation, which is worth checking when the response rate is low.

## Unequal allocation

With an allocation ratio of \(r\) to 1, the recovered probabilities are weighted accordingly and the second-stage enrolment follows the same ratio.

    BinaryBSSR(n1 = 40, n2 = 20, S = 18, Delta.A = 0.25, r = 2,
               alpha = 0.025, tar.power = 0.8, Test = 'Chisq')
    #> Blinded sample size re-estimation from interim data
    #>
    #>   Test             : Chisq
    #>   Alternative      : greater
    #>   Design rule      : unrestricted
    #>   Assumed effect   : 0.25
    #>   Alpha            : 0.025, target power 0.8
    #>
    #> Interim data
    #>   Patients         : n1 = 40, n2 = 20, total n = 60
    #>   Responders       : S = 18, blinded pooled rate = 0.3
    #>   Recovered rates  : hat.p1 = 0.3833, hat.p2 = 0.1333
    #>
    #> Re-estimation
    #>   Required total   : N1 = 70, N2 = 35, total N = 105
    #>   Still to enrol   : group 1 = 30, group 2 = 15, total = 45
    #>   Final size       : N1 = 70, N2 = 35, total N = 105
    #>   Power at final N : 0.802

## Practical notes

The interim total of responders is the only quantity that leaves the database, so the analysis stays blinded and no alpha is spent. The re-estimated sample size still depends on \(\Delta_{A}\), which is fixed at the design stage and must not be revised in the light of the interim data.

Fix the timing of the interim analysis, the value of \(\Delta_{A}\), the test and the choice between the restricted and unrestricted rules in the protocol before the trial starts. Report the operating characteristics from `BinaryPowerBSSR()` alongside them, since the type I error rate of the procedure is a property of the whole rule rather than of the final test alone.

Setting `Delta.T` to zero makes the two groups identical, so `power.BSSR` and `power.TRAD` become rejection probabilities under the null hypothesis. The function evaluates them only at the values passed through `p` and does not search over the unit interval, so the largest type I error rate has to be read off a grid.

    null <- BinaryPowerBSSR(
      p = seq(0.02, 0.98, by = 0.02),
      Delta.A = Delta.A, Delta.T = 0,
      N1 = plan$N1, N2 = plan$N2, omega = 0.5, r = 1,
      alpha = 0.025, tar.power = 0.8, Test = 'Chisq', restricted = TRUE
    )
    data.frame(
      largest.type1.BSSR = round(max(null$power.BSSR), 5),
      attained.at.p = null$p[which.max(null$power.BSSR)],
      largest.type1.fixed = round(max(null$power.TRAD), 5)
    )
    #>   largest.type1.BSSR attained.at.p largest.type1.fixed
    #> 1            0.02713           0.5             0.02592

The rejection region and the re-estimated sample size are shared across the elements of `p`, so a grid of this size costs little more than a handful of scenarios.

Both figures sit above the nominal 0.025, for two separate reasons. The chi-squared test is not exact, which is why the fixed-sample design alone already overshoots, and the re-estimation then pushes the maximum higher still. Substituting an exact test such as `Boschloo` removes the first source but not the second, because the type I error rate of an adaptive rule is not inherited from the test it applies at the end. Running this check is therefore worthwhile whichever test is chosen.