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

TL;DR

This paper introduces a novel optimization framework using mixed-integer linear programming to improve the efficiency of group sequential hypothesis testing, outperforming classical methods in early decision-making.

Contribution

It develops a general S-MILP approach to optimize rejection criteria in GST, demonstrating superiority over traditional alpha-spending methods and providing new insights into alpha-budget allocation.

Findings

S-MILP approach dominates classical GST procedures

Optimal solutions tend to spend alpha-budget early

Application to kidney injury study shows faster conclusions

Abstract

Sequential hypothesis tests are widely adopted as a principled way to perform multiple tests on data that arrives over time. In particular, researchers frequently utilize group sequential hypothesis tests (GST) to test the same hypotheses at K times or "groups" while data arrives sequentially. In this setting, many methods have been proposed to allow researchers to uniformly control type-1 error across K checks (often known as various alpha-spending budgets). Although these methods are all successfully valid in controlling uniform type-1 error, it is not clear which of these methods are optimal when trying to reject the null as soon as possible. In this paper, we directly optimize the rejection criterion in the GST setting under the same constraints of controlling type-1 and type-2 errors. 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. We also find that the optimal solution typically aggressively spends the alpha-budget early, shedding insight to the long-standing debate of which alpha-spending budgets are more efficient. We finally apply our optimal S-MILP approach to a recent study on acute kidney injury interventions and find our optimal S-MILP approach can reach the same statistically significant conclusion faster than the original study and other GST methods.