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  "Description": "Implements a two-stage, design-informed framework for\nselecting the truncation time in time-to-event trials analyzed\nwith the average hazard estimand, motivated by pediatric\noncology settings with small samples, slow accrual, and limited\nfollow-up. Stage 1 validates a clinically proposed truncation\ntime against the planned design using simulation-based\ndiagnostics for risk-set support, follow-up coverage,\nestimability, and estimator stability, classifying it as Pass,\nBorderline, or Fail. Stage 2 performs constrained optimization\nover a grid of candidate truncation times within a\nclinical-distance window, maximizing a utility subject to\nfeasibility constraints, with an independent evaluation run to\nassess the selected time. Supports proportional-hazards,\nearly-, and delayed-effect patterns, uniform accrual with\nadministrative censoring, and calibrated exponential random\ncensoring.",
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      "title": "Assess the statistical feasibility of a proposed truncation time",
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      "title": "Design-informed truncation time selection",
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        "Introduction",
        "Quick start: a runnable toy example",
        "The planned design",
        "Stage 1: assessing the clinically proposed truncation time",
        "How can ($\\tau = 24$) months be made feasible?",
        "Approach 1: increase the additional follow-up period",
        "Approach 2: select an earlier truncation time",
        "Approach 3: increase the sample size",
        "Customizing the hard-failure rules",
        "Function \"optimize_tau()\": feasibility-constrained optimization",
        "The complete two-stage workflow",
        "Why is one table called the independent evaluation table?"
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