DoseFinding

R package DoseFinding: Planning and Analyzing Dose Finding experiments. The DoseFinding package provides functions for the design and analysis of dose-finding experiments (with focus on pharmaceutical Phase II clinical trials). It provides functions for: multiple contrast tests, fitting non-linear dose-response models (using Bayesian and non-Bayesian estimation), calculating optimal designs and an implementation of the MCPMod methodology.

Keywords for this software

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References in zbMATH (referenced in 11 articles )

Showing results 1 to 11 of 11.
Sorted by year (citations)

  1. Flournoy, Nancy; May, Caterina; Tommasi, Chiara: The effects of adaptation on maximum likelihood inference for nonlinear models with normal errors (2021)
  2. Dette, Holger; Möllenhoff, Kathrin; Volgushev, Stanislav; Bretz, Frank: Equivalence of regression curves (2018)
  3. Ning, Jing: Book review of: J. Qin, Biased sampling, over-identified parameter problems and beyond (2018)
  4. Seung Hyun; Weng Wong; Yarong Yang: VNM: An R Package for Finding Multiple-Objective Optimal Designs for the 4-Parameter Logistic Model (2018) not zbMATH
  5. Baayen, C.; Hougaard, P.; Pipper, C. B.: Testing effect of a drug using multiple nested models for the dose-response (2015)
  6. Jones, Byron; Kenward, Michael G.: Design and analysis of cross-over trials (2015)
  7. Young, Walter R. (ed.); Chen, Ding-Geng (Din) (ed.): Clinical trial biostatistics and biopharmaceutical applications (2015)
  8. Bornkamp, Björn: Practical considerations for using functional uniform prior distributions for dose-response estimation in clinical trials (2014)
  9. Köllmann, Claudia; Bornkamp, Björn; Ickstadt, Katja: Unimodal regression using Bernstein-Schoenberg splines and penalties (2014)
  10. Bornkamp, Björn: Functional uniform priors for nonlinear modeling (2012)
  11. Bornkamp, Björn; Bretz, Frank; Dette, Holger; Pinheiro, José: Response-adaptive dose-finding under model uncertainty (2011)