Preface to the themed issue on 'Early phase clinical trial design methodology'.
- Published in:
- Journal of the Royal Statistical Society: Series C (Applied Statistics), 2019, v. 68, n. 2, p. 267, doi. 10.1111/rssc.12329
- By:
- Publication type:
- Article
Works matching IS 00359254 AND DT 2019 AND VI 68 AND IP 2
Results: 14
1
2
Dose individualization and variable selection by using the Bayesian lasso in early phase dose finding trials.
- Published in:
- Journal of the Royal Statistical Society: Series C (Applied Statistics), 2019, v. 68, n. 2, p. 445, doi. 10.1111/rssc.12326
- By:
- Publication type:
- Article
3
Treatment and dose prioritization in early phase platform trials of targeted cancer therapies.
- Published in:
- Journal of the Royal Statistical Society: Series C (Applied Statistics), 2019, v. 68, n. 2, p. 475, doi. 10.1111/rssc.12324
- By:
- Publication type:
- Article
4
A Bayesian model‐free approach to combination therapy phase I trials using censored time‐to‐toxicity data.
- Published in:
- Journal of the Royal Statistical Society: Series C (Applied Statistics), 2019, v. 68, n. 2, p. 309, doi. 10.1111/rssc.12323
- By:
- Publication type:
- Article
5
Phase I–II trial design for biologic agents using conditional auto‐regressive models for toxicity and efficacy.
- Published in:
- Journal of the Royal Statistical Society: Series C (Applied Statistics), 2019, v. 68, n. 2, p. 331, doi. 10.1111/rssc.12314
- By:
- Publication type:
- Article
6
A utility‐based Bayesian phase I–II design for immunotherapy trials with progression‐free survival end point.
- Published in:
- Journal of the Royal Statistical Society: Series C (Applied Statistics), 2019, v. 68, n. 2, p. 411, doi. 10.1111/rssc.12288
- By:
- Publication type:
- Article
7
Semiparametric dose finding methods: special cases.
- Published in:
- Journal of the Royal Statistical Society: Series C (Applied Statistics), 2019, v. 68, n. 2, p. 271, doi. 10.1111/rssc.12308
- By:
- Publication type:
- Article
8
Issue Information.
- Published in:
- Journal of the Royal Statistical Society: Series C (Applied Statistics), 2019, v. 68, n. 2, p. 265, doi. 10.1111/rssc.12301
- Publication type:
- Article
9
An information theoretic phase I–II design for molecularly targeted agents that does not require an assumption of monotonicity.
- Published in:
- Journal of the Royal Statistical Society: Series C (Applied Statistics), 2019, v. 68, n. 2, p. 347, doi. 10.1111/rssc.12293
- By:
- Publication type:
- Article
10
AAA: triple adaptive Bayesian designs for the identification of optimal dose combinations in dual‐agent dose finding trials.
- Published in:
- Journal of the Royal Statistical Society: Series C (Applied Statistics), 2019, v. 68, n. 2, p. 385, doi. 10.1111/rssc.12291
- By:
- Publication type:
- Article
11
A dose finding design for seizure reduction in neonates.
- Published in:
- Journal of the Royal Statistical Society: Series C (Applied Statistics), 2019, v. 68, n. 2, p. 427, doi. 10.1111/rssc.12289
- By:
- Publication type:
- Article
12
Joint modelling of a binary and a continuous outcome measured at two cycles to determine the optimal dose.
- Published in:
- Journal of the Royal Statistical Society: Series C (Applied Statistics), 2019, v. 68, n. 2, p. 369, doi. 10.1111/rssc.12305
- By:
- Publication type:
- Article
13
Optimizing natural killer cell doses for heterogeneous cancer patients on the basis of multiple event times.
- Published in:
- Journal of the Royal Statistical Society: Series C (Applied Statistics), 2019, v. 68, n. 2, p. 461, doi. 10.1111/rssc.12271
- By:
- Publication type:
- Article
14
gBOIN: a unified model‐assisted phase I trial design accounting for toxicity grades, and binary or continuous end points.
- Published in:
- Journal of the Royal Statistical Society: Series C (Applied Statistics), 2019, v. 68, n. 2, p. 289, doi. 10.1111/rssc.12263
- By:
- Publication type:
- Article