An Implementation of Dimensionality Reduction
    via Regression using Kernel Ridge Regression.

Targeted minimum loss-based estimators of counterfactual means and
    causal effects that are doubly-robust with respect both to consistency and
    asymptotic normality (Benkeser et al (2017), <doi:10.1093/biomet/asx053>; MJ
    van der Laan (2014), <doi:10.1515/ijb-2012-0038>).

Performs drug demand forecasting by modeling drug dispensing data while taking into account predicted enrollment and treatment discontinuation dates. The gap time between randomization and the first drug dispensing visit is modeled using interval-censored exponential, Weibull, log-logistic, or log-normal distributions (Anderson-Bergman (2017) <doi:10.18637/jss.v081.i12>). The number of skipped visits is modeled using Poisson, zero-inflated Poisson, or negative binomial distributions (Zeileis, Kleiber & Jackman (2008) <doi:10.18637/jss.v027.i08>). The gap time between two consecutive drug dispensing visits given the number of skipped visits is modeled using linear regression based on least squares or least absolute deviations (Birkes & Dodge (1993, ISBN:0-471-56881-3)). The number of dispensed doses is modeled using linear or linear mixed-effects models (McCulloch & Searle (2001, ISBN:0-471-19364-X)).

Plan optimal sample size allocation and go/no-go decision rules
  for phase II/III drug development programs with time-to-event, binary or
  normally distributed endpoints when assuming fixed treatment effects or a
  prior distribution for the treatment effect, using methods from Kirchner et al.
  (2016) <doi:10.1002/sim.6624> and Preussler (2020). Optimal is in the sense of
  maximal expected utility, where the utility is a function taking into account
  the expected cost and benefit of the program. It is possible to extend to more
  complex settings with bias correction (Preussler S et al. (2020)
  <doi:10.1186/s12874-020-01093-w>),  multiple phase III trials (Preussler et
  al. (2019) <doi:10.1002/bimj.201700241>), multi-arm trials (Preussler et al.
  (2019) <doi:10.1080/19466315.2019.1702092>), and multiple endpoints
  (Kieser et al. (2018) <doi:10.1002/pst.1861>).