Pith Number

pith:TDN3ASGW

pith:2026:TDN3ASGWEAEV4DRHKPLG7JW36H

not attested not anchored not stored refs resolved

A General Framework for Optimal Group Sequential Testing via Mixed-Integer Linear Programming

Dae Woong Ham, Stefanus Jasin, Xuejun Zhao

Mixed-integer linear programming finds optimal rejection boundaries for group sequential tests that allow earlier stopping than standard methods.

arxiv:2605.03406 v2 · 2026-05-05 · stat.ME

Open paper page JSON Open Graph Bundle Merged state Verified badge What is a Pith Number?

Add to your LaTeX paper

\usepackage{pith}
\pithnumber{TDN3ASGWEAEV4DRHKPLG7JW36H}

Prints a linked badge after your title and injects PDF metadata. Compiles on arXiv. Learn more · Embed verified badge

Record completeness

1 Bitcoin timestamp

2 Internet Archive

3 Author claim open · sign in to claim

4 Citations open

5 Replications open

✓ Portable graph bundle live · download bundle · merged state

The bundle contains the canonical record plus signed events. A mirror can host it anywhere and recompute the same current state with the deterministic merge algorithm.

Claims

C1strongest claim

We use a sample average approximation combined with mixed integer linear programming (S-MILP) approach for this problem and show how our S-MILP approach dominates classical GST procedures such as Lan-DeMets, Pocock, and O'Brien-Fleming methods.

C2weakest assumption

The sample average approximation provides a sufficiently accurate representation of the true type-1 and type-2 error probabilities for the optimized boundaries to maintain the desired error control in practice.

C3one line summary

The authors propose an S-MILP framework that optimizes group sequential testing boundaries to achieve faster rejection of the null hypothesis compared to traditional methods while controlling type I and type II errors.

References

179 extracted · 179 resolved · 1 Pith anchors

[1] Eales, J. D. and Jennison, C. , title =. Biometrika , volume =. 1992 , doi = 1992

[2] Hampson, L. V. and Jennison, C. , title =. Journal of the Royal Statistical Society, Series B , volume =. 2013 , doi = 2013

[3] Lectures on stochastic programming: modeling and theory , author=. 2021 , publisher= 2021

[4] Introduction to sample size determination and power analysis for clinical trials. , author=. Controlled clinical trials , year=

[5] Cohen, J. , biburl =

Formal links

2 machine-checked theorem links

Receipt and verification

First computed	2026-05-20T00:01:42.613524Z
Builder	pith-number-builder-2026-05-17-v1
Signature	Pith Ed25519 (`pith-v1-2026-05`) · public key
Schema	pith-number/v1.0

Canonical hash

98dbb048d620095e0e2753d66fa6dbf1c1692ec6eae16f072930b031cad75422

Aliases

arxiv: 2605.03406 · arxiv_version: 2605.03406v2 · doi: 10.48550/arxiv.2605.03406 · pith_short_12: TDN3ASGWEAEV · pith_short_16: TDN3ASGWEAEV4DRH · pith_short_8: TDN3ASGW

Agent API

Resolver JSON Graph JSON Events JSON Schema Signing key

Verify this Pith Number yourself

curl -sH 'Accept: application/ld+json' https://pith.science/pith/TDN3ASGWEAEV4DRHKPLG7JW36H \
  | jq -c '.canonical_record' \
  | python3 -c "import sys,json,hashlib; b=json.dumps(json.loads(sys.stdin.read()), sort_keys=True, separators=(',',':'), ensure_ascii=False).encode(); print(hashlib.sha256(b).hexdigest())"
# expect: 98dbb048d620095e0e2753d66fa6dbf1c1692ec6eae16f072930b031cad75422

Canonical record JSON

{
  "metadata": {
    "abstract_canon_sha256": "b86cc1617bf7f2dc2a31a1a614b162d674c0bfbc553e5a26f0394c3958235a40",
    "cross_cats_sorted": [],
    "license": "http://creativecommons.org/licenses/by/4.0/",
    "primary_cat": "stat.ME",
    "submitted_at": "2026-05-05T06:25:52Z",
    "title_canon_sha256": "adf1c18d83ed7be9b921b162b48cac2988fe566873e4061c4c45289134cd20e9"
  },
  "schema_version": "1.0",
  "source": {
    "id": "2605.03406",
    "kind": "arxiv",
    "version": 2
  }
}