The primary goal of phase I clinical trials is to find the maximum tolerated dose (MTD). To reach this objective, we introduce a new design for phase I clinical trials, the posterior predictive (PoP) design. The PoP design is an innovative model-assisted design that is as simply as the conventional algorithmic designs as its decision rules can be pre-tabulated prior to the onset of trial, but is of more flexibility of selecting diverse target toxicity rates and cohort sizes. The PoP design has desirable properties, such as coherence and consistency. Moreover, the PoP design provides better empirical performance than the BOIN and Keyboard design with respect to high average probabilities of choosing the MTD and slightly lower risk of treating patients at subtherapeutic or overly toxic doses.

Optimal experimental designs for both population and individual
    studies based on nonlinear mixed-effect models. Often this is based on a
    computation of the Fisher Information Matrix. This package was developed
    for pharmacometric problems, and examples and predefined models are available
    for these types of systems. The methods are described in Nyberg et al. 
    (2012) <doi:10.1016/j.cmpb.2012.05.005>, and Foracchia et al. (2004) 
    <doi:10.1016/S0169-2607(03)00073-7>.

Enables computation of epidemiological statistics, including
    those where counts or mortality rates of the reference population are
    used.  Currently supported: excess hazard models (Dickman, Sloggett,
    Hills, and Hakulinen (2012) <doi:10.1002/sim.1597>), rates, mean
    survival times, relative/net survival (in particular the Ederer II
    (Ederer and Heise (1959)) and Pohar Perme (Pohar Perme, Stare, and
    Esteve (2012) <doi:10.1111/j.1541-0420.2011.01640.x>) estimators), and
    standardized incidence and mortality ratios, all of which can be
    easily adjusted for by covariates such as age. Fast splitting and
    aggregation of 'Lexis' objects (from package 'Epi') and other
    computations achieved using 'data.table'.