multiarm: Design and analysis of fixed-sample multi-arm clinical trials
Welcome to the R Shiny graphical user interface (GUI) to the R package multiarm, which is currently available from:
https://github.com/mjg211/multiarmWithin R, multiarm provides functionality to assist with the design and analysis of fixed-sample multi-arm clinical trial utilising one of several supported multiple comparison corrections, when the outcome data is assumed to be either normally or Bernoulli distributed. Available functions allow for sample size determination (including for A-, D-, and E-optimal designs), trial simulation, analytical operating characteristic calculation (including the conjunctive power, disjunctive power, family-wise error-rate, and false discovery rate), and the production of several plots.
At present, this GUI supports execution of the commands for design determination and plot production. Additional functionality will be added over time.
See the 'Design' tab on the sidebar for code execution, or the 'About' tab for further information on the GUI.
Design parameters
Design summary
Operating characteristics summary: Key
Operating characteristics summary: Error-rates
Operating characteristics summary: Power & other quantities
Equal treatment effects: Error-rates
Equal treatment effects: Power
Equal treatment effects: Other
Shifted treatment effects
Session Information
Design parameters
Design summary
Operating characteristics summary: Key
Operating characteristics summary: Error-rates
Operating characteristics summary: Power & other quantities
Equal treatment effects: Error-rates
Equal treatment effects: Power
Equal treatment effects: Other
Shifted treatment effects
Session Information
About
This graphical user interface (GUI) is built upon (and in to) v.0.10 of the R package multiarm, written by Michael Grayling (Newcastle University).
The first-line response to a possible bug should be to submit it as a 'New issue' at:
https://github.com/mjg211/multiarm/issuesIf the issue is more complex, or a patch is not provided in reasonable time, please contact Michael Grayling at michael.grayling@newcastle.ac.uk. Similarly, please feel free to contact with suggestions for new features, or for further support with using the package or GUI.
If you use multiarm, please cite it with:
Grayling MJ (2019) multiarm: Design and analysis of fixed-sample multi-arm clinical trials. URL: http://www.github.com/mjg211/multiarm/.
A selection of references related to the methodology used in multiarm are given below.
References
Benjamini Y, Hochberg Y (1995) Controlling the false discovery rate: a practical and powerful approach to multiple testing. J R Stat Soc B 57(1):289-300.
Benjamini Y, Yekutieli D (2001) The control of the false discovery rate in multiple testing under dependency. Ann Stat 29(4):1165–88.
Bonferroni CE (1936) Teoria statistica delle classi e calcolo delle probabilita. Pubblicazioni del R Istituto Superiore di Scienze Economiche e Commerciali di Firenze.
Dunnett CW (1955) A multiple comparison procedure for comparing several treatments with a control. J Am Stat Assoc 50(272):1096-121.
Hochberg Y (1988) A sharper bonferroni procedure for multiple tests of significance. Biometrika 75(4):800-2.
Holm S (1979) A simple sequentially rejective multiple test procedure. Scand J Stat 6(2):65-70.
Sidak Z (1967) Rectangular confidence regions for the means of multivariate normal distributions. J Am Stat Assoc 62(318):626-33.