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  "path": "/t/generalizability-vs-transportability-in-trials/28551?page=4#post_68",
  "publishedAt": "2026-06-03T22:57:59.000Z",
  "site": "https://discourse.datamethods.org",
  "tags": [
    "Statistical Process Control and Experimental Design",
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  "textContent": "evidenceinthewild:\n\n> Your linked thread suggests they are often recommended at oncology workshops when there aren’t enough resources to power a comparative trial — randomization retained as a kind of procedural legitimacy device even when comparison is abandoned.\n\nI find it strange, even suspicious, that balanced/minimized designs are not recommended by biostatisticians in these particular cases of scarce resources.\n\nFisher and Gossett debated the merits of balanced vs randomized designs going on 100 years ago. They were more recently discussed in the CONSORT guidelines in 2010. The first minimization algorithms are over 50 years old, with improvements being made more recently. Even if one has a preference for large randomized trials, why don’t controlled allocation designs such as minimization get used more in these resource constrained scenarios?\n\nRelated thread\n\nStatistical Process Control and Experimental Design\n\n> The flavor of research done by OR researchers in Design of Experiments is foreign to what is expected in medical statistics. A synonym of operations research might be applied decision analysis. The following paper by Nathan Kallus from Cornell is a good example: Kallus, N. (2018). Optimal a priori balance in the design of controlled experiments. Journal of the Royal Statistical Society Series B: Statistical Methodology, 80(1), 85-112. link We develop a unified theory of designs for controll…",
  "title": "Generalizability vs. Transportability in Trials"
}