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  "Title": "Extended Progression Models for Repeated Measures",
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  "Description": "Extended progression models for repeated measures in\nprogressive disease clinical trials. Provides nonlinear\nmixed-effects progression models (linear, plateau, Emax,\nexponential decay), delayed treatment onset estimation by\nprofile likelihood, joint longitudinal-survival models for\ninformative dropout, competing-risks extensions,\nbiomarker-informed adaptive dosing rules, and simulation-based\npower and sample-size calculation. The progression-model view\nof treatment effects, in which effects are characterised on the\nunderlying disease trajectory rather than as additive shifts on\nthe outcome scale, follows Raket (2022) <doi:10.1002/sim.9581>.\nThe nonlinear mixed-effects machinery is built on the 'nlme'\nimplementation described by Pinheiro and Bates (2000, ISBN\n978-0-387-98957-0), and the joint modelling of longitudinal and\ntime-to-event data follows Rizopoulos (2012, ISBN\n978-1-4398-7286-4).",
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  "_help": [
    {
      "page": "adni_simulated",
      "title": "Simulated ADNI-style Alzheimer's trial data",
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      ]
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      "title": "Biomarker-informed adaptive dosing rule",
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    {
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      "title": "Default priors for Bayesian progression models",
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    },
    {
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      "title": "Delayed treatment effect function",
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    {
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      "title": "Emax biomarker model",
      "topics": [
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