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Preface to the themed issue on 'Early phase clinical trial design methodology'.
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- Journal of the Royal Statistical Society: Series C (Applied Statistics), 2019, v. 68, n. 2, p. 267, doi. 10.1111/rssc.12329
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Treatment and dose prioritization in early phase platform trials of targeted cancer therapies.
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- Journal of the Royal Statistical Society: Series C (Applied Statistics), 2019, v. 68, n. 2, p. 475, doi. 10.1111/rssc.12324
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Phase I–II trial design for biologic agents using conditional auto‐regressive models for toxicity and efficacy.
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- Journal of the Royal Statistical Society: Series C (Applied Statistics), 2019, v. 68, n. 2, p. 331, doi. 10.1111/rssc.12314
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Dose individualization and variable selection by using the Bayesian lasso in early phase dose finding trials.
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- Journal of the Royal Statistical Society: Series C (Applied Statistics), 2019, v. 68, n. 2, p. 445, doi. 10.1111/rssc.12326
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A Bayesian model‐free approach to combination therapy phase I trials using censored time‐to‐toxicity data.
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- Journal of the Royal Statistical Society: Series C (Applied Statistics), 2019, v. 68, n. 2, p. 309, doi. 10.1111/rssc.12323
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- Article
Semiparametric dose finding methods: special cases.
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- Journal of the Royal Statistical Society: Series C (Applied Statistics), 2019, v. 68, n. 2, p. 271, doi. 10.1111/rssc.12308
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Joint modelling of a binary and a continuous outcome measured at two cycles to determine the optimal dose.
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- Journal of the Royal Statistical Society: Series C (Applied Statistics), 2019, v. 68, n. 2, p. 369, doi. 10.1111/rssc.12305
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- Article
Issue Information.
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- Journal of the Royal Statistical Society: Series C (Applied Statistics), 2019, v. 68, n. 2, p. 265, doi. 10.1111/rssc.12301
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- Article
A utility‐based Bayesian phase I–II design for immunotherapy trials with progression‐free survival end point.
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- Journal of the Royal Statistical Society: Series C (Applied Statistics), 2019, v. 68, n. 2, p. 411, doi. 10.1111/rssc.12288
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An information theoretic phase I–II design for molecularly targeted agents that does not require an assumption of monotonicity.
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- Journal of the Royal Statistical Society: Series C (Applied Statistics), 2019, v. 68, n. 2, p. 347, doi. 10.1111/rssc.12293
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AAA: triple adaptive Bayesian designs for the identification of optimal dose combinations in dual‐agent dose finding trials.
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- Journal of the Royal Statistical Society: Series C (Applied Statistics), 2019, v. 68, n. 2, p. 385, doi. 10.1111/rssc.12291
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- Article
A dose finding design for seizure reduction in neonates.
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- Journal of the Royal Statistical Society: Series C (Applied Statistics), 2019, v. 68, n. 2, p. 427, doi. 10.1111/rssc.12289
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- Article
Optimizing natural killer cell doses for heterogeneous cancer patients on the basis of multiple event times.
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- Journal of the Royal Statistical Society: Series C (Applied Statistics), 2019, v. 68, n. 2, p. 461, doi. 10.1111/rssc.12271
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- Article
gBOIN: a unified model‐assisted phase I trial design accounting for toxicity grades, and binary or continuous end points.
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- Journal of the Royal Statistical Society: Series C (Applied Statistics), 2019, v. 68, n. 2, p. 289, doi. 10.1111/rssc.12263
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- Article