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  "Title": "Flexible and Efficient Evaluation of Principal\nSurrogates/Treatment Effect Modifiers",
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  "Authors@R": "person(\"Michal\", \"Juraska\", email = \"mjuraska@fredhutch.org\", role = c(\"aut\", \"cre\"))",
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  "Description": "Implements estimation and testing procedures for\nevaluating an intermediate biomarker response as a principal\nsurrogate of a clinical response to treatment (i.e., principal\nstratification effect modification analysis), as described in\nJuraska M, Huang Y, and Gilbert PB (2020), Inference on\ntreatment effect modification by biomarker response in a\nthree-phase sampling design, Biostatistics, 21(3): 545-560\n<doi:10.1093/biostatistics/kxy074>. The methods avoid the\nrestrictive 'placebo structural risk' modeling assumption\ncommon to past methods and further improve robustness by the\nuse of nonparametric kernel smoothing for biomarker density\nestimation. A randomized controlled two-group clinical efficacy\ntrial is assumed with an ordered categorical or continuous\nunivariate biomarker response measured at a fixed timepoint\npost-randomization and with a univariate baseline surrogate\nmeasure allowed to be observed in only a subset of trial\nparticipants with an observed biomarker response (see the\nflexible three-phase sampling design in the paper for details).\nBootstrap-based procedures are available for pointwise and\nsimultaneous confidence intervals and testing of four relevant\nhypotheses. Summary and plotting functions are provided for\nestimation results.",
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  "URL": "https://github.com/mjuraska/pssmooth",
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  "Date/Publication": "2020-11-18 01:09:16 UTC",
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