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>).

Ingredient specific diagnostics for drug exposure records in the Observational Medical Outcomes Partnership (OMOP) common data model.

A comprehensive toolkit for analyzing microscopy data output from 'QuPath' software. 
    Provides functionality for automated data processing, metadata extraction, and statistical 
    analysis of imaging results. The methodology implemented in this package is based on 
    Labrosse et al. (2024) <doi:10.1016/j.xpro.2024.103274> "Protocol for quantifying drug 
    sensitivity in 3D patient-derived ovarian cancer models", which describes the complete 
    workflow for drug sensitivity analysis in patient-derived cancer models.

A systematic biology tool was developed to repurpose drugs via a drug-drug functional similarity network. 'DrugSim2DR' first predict drug-drug functional similarity  in the context of specific disease, and then using the similarity constructed a weighted drug similarity network. Finally, it used a network propagation algorithm on the network to identify drugs with significant target abnormalities as candidate drugs.

Summarise patient-level drug utilisation cohorts using data mapped 
    to the Observational Medical Outcomes Partnership (OMOP) common data model. 
    New users and prevalent users cohorts can be generated and their 
    characteristics, indication and drug use summarised.