Methods for working with dose-finding clinical trials. We provide 
    implementations of many dose-finding clinical trial designs, including the 
    continual reassessment method (CRM) by O'Quigley et al. (1990) 
    <doi:10.2307/2531628>, the toxicity probability interval (TPI) design by Ji
    et al. (2007) <doi:10.1177/1740774507079442>, the modified TPI (mTPI) design
    by Ji et al. (2010) <doi:10.1177/1740774510382799>, the Bayesian optimal 
    interval design (BOIN) by Liu & Yuan (2015) <doi:10.1111/rssc.12089>, EffTox
    by Thall & Cook (2004) <doi:10.1111/j.0006-341X.2004.00218.x>; the design of
    Wages & Tait (2015) <doi:10.1080/10543406.2014.920873>, and the 3+3 
    described by Korn et al. (1994) <doi:10.1002/sim.4780131802>. All designs
    are implemented with a common interface. We also offer optional additional 
    classes to tailor the behaviour of all designs, including avoiding skipping 
    doses, stopping after n patients have been treated at the recommended dose,  
    stopping when a toxicity condition is met, or demanding that n patients are 
    treated before stopping is allowed. By daisy-chaining together these classes
    using the pipe operator from 'magrittr', it is simple to tailor the 
    behaviour of a dose-finding design so it behaves how the trialist wants.
    Having provided a flexible interface for specifying designs, we then provide
    functions to run simulations and calculate dose-paths for future cohorts of 
    patients.

A collection of functions and 'jamovi' module for the estimation approach to inferential statistics, the approach which emphasizes effect sizes, interval estimates, and meta-analysis. Nearly all functions are based on 'statpsych' and 'metafor'.  This package is still under active development, and breaking changes are likely, especially with the plot and hypothesis test functions.  Data sets are included for all examples from Cumming & Calin-Jageman (2024) <ISBN:9780367531508>.

The experiment selector cross-validated targeted maximum likelihood estimator (ES-CVTMLE) aims to select the experiment that optimizes the bias-variance tradeoff for estimating a causal average treatment effect (ATE) where different experiments may include a randomized controlled trial (RCT) alone or an RCT combined with real-world data. Using cross-validation, the ES-CVTMLE separates the selection of the optimal experiment from the estimation of the ATE for the chosen experiment. The estimated bias term in the selector is a function of the difference in conditional mean outcome under control for the RCT compared to the combined experiment. In order to help include truly unbiased external data in the analysis, the estimated average treatment effect on a negative control outcome may be added to the bias term in the selector. For more details about this method, please see Dang et al. (2022) <arXiv:2210.05802>.