c(
person("Pavel", "Fiser (MSD)", email = "pavel.fiser@merck.com", role = c("aut")),
person("Tomas", "Bartonek (MSD)", role = "aut"),
person("Julie", "Dudasova (MSD)", email = "julie.dudasova@merck.com", role = c("aut", "cre")),
person("Regina", "Laube (MSD)", role = "aut")
)
Set of functions that implement the PoDBAY method, described in the publication 'A method to estimate probability of disease and vaccine efficacy from clinical trial immunogenicity data' by Julie Dudasova, Regina Laube, Chandni Valiathan, Matthew C. Wiener, Ferdous Gheyas, Pavel Fiser, Justina Ivanauskaite, Frank Liu and Jeffrey R. Sachs (NPJ Vaccines, 2021), <doi:10.1038/s41541-021-00377-6>.
2021 Merck Sharp & Dohme Corp. a subsidiary of Merck & Co.,
Inc., Kenilworth, NJ, USA
Pavel Fiser (MSD) [aut],
Tomas Bartonek (MSD) [aut],
Julie Dudasova (MSD) [aut, cre],
Regina Laube (MSD) [aut]

Polynomial (ordinary differential equation) Model Search
PoMoS-package aims to determine from a set of N time
series the optimal polynomial structure of a model built on
first-order ordinary differential equations. The core of the
package is based on the poMoS function: an evolutionary
algorithm combined with a least square fitting. Optimality is
estimated with AIC (Akaike, 1974) or AIC-like criterions.
Although efficient in its selection, the identification of the
optimal structure cannot be guaranteed. Therefore, both
selected and rejected models are reconsidered after optimal
solutions are obtained from the evolutionary algorithm for
another analysis. This analysis is based on a statistical
evaluation of the regressors' quality.

Posterior Predictive (PoP) Design for Phase I Clinical Trials
c(person(given = "Chenqi",
family = "Fu",
role = "aut"),
person(given = "Xinying",
family = "Fang",
role = "cre",
email = "fxy950225@gmail.com"),
person(given = "Shouhao",
family = "Zhou",
role = "aut"))
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 easy to implement in a manner similar to the traditional 3+3 design, 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 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. Reference: Brunk, H., Barlow, R. E., Bartholomew, D. J. & Bremner, J. M (1972, ISBN-13: 978-0471049708).
Chenqi Fu [aut],
Xinying Fang [cre],
Shouhao Zhou [aut]