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pj
019d608f65
Steps to first violation with clean runs right-censored at the budget, since per-run yield is a binary at 11 to 45 percent and separating two arms on it would need roughly 80 runs per arm. Kaplan-Meier, log-rank, Wilcoxon rank-sum with Vargha-Delaney A12, Holm within each family. A hand-rolled log-rank that is subtly wrong is a silent-wrong-number generator and would be believed, so every statistic is validated against a published worked example with the source named in the test: R survdiff on aml, Freireich 6-MP, Hollander and Wolfe 1973 for the rank sum, printed p.adjust output for Holm. Two could not be: the k>2 log-rank, guarded by calibration instead, and the tie-corrected variance, checked against an exact permutation variance. Failed and timed-out runs are excluded as missing data and counted by reason, never treated as censored observations, which would bias the result. Claude-Session: https://claude.ai/code/session_01A5KmftdEJ49A9z5mF5ESrX
37 lines
924 B
Go
37 lines
924 B
Go
package main
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import (
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"math"
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"slices"
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)
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// holm applies the Holm (1979) step-down correction within one family of
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// comparisons, enforcing monotonicity across the sorted p-values the way R's
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// p.adjust does. Holm, "A Simple Sequentially Rejective Multiple Test
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// Procedure", Scandinavian Journal of Statistics 6(2), 65-70.
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func holm(pValues []float64) []float64 {
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count := len(pValues)
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adjusted := make([]float64, count)
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order := make([]int, count)
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for index := range order {
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order[index] = index
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}
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slices.SortStableFunc(order, func(left, right int) int {
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switch {
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case pValues[left] < pValues[right]:
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return -1
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case pValues[left] > pValues[right]:
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return 1
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default:
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return 0
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}
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})
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running := 0.0
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for position, index := range order {
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scaled := float64(count-position) * pValues[index]
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running = math.Max(running, scaled)
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adjusted[index] = math.Min(running, 1)
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}
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return adjusted
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}
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