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TrialSimulator - Clinical Trial Simulator

Simulate phase II and/or phase III clinical trials. It supports various types of endpoints and adaptive strategies. Tools for carrying out graphical testing procedure and combination test under group sequential design are also provided.

Last updated

cpp

7.73 score 7 stars 34 scripts 628 downloads

multipleOutcomes - Joint Covariance and Treatment-Effect Tests for Multiple Outcomes

Fits generalized linear models, Cox proportional-hazards models, log-rank tests, generalized estimating equations, mixed models with repeated measures, Kaplan-Meier curves, quantile differences, and hierarchical net-benefit (win-difference) statistics jointly across multiple endpoints, and returns the full asymptotic covariance matrix linking them. Implements PATED (Prognostic Assisted Treatment Effect Detection), a randomized-trial method that exploits balanced prognostic covariates to tighten standard errors and increase statistical power without introducing bias.

Last updated

openblas

3.74 score 6 scripts 297 downloads

shinyStep - User-Editable R Functions in 'Shiny' Apps with a Step Debugger

A pair of 'Shiny' modules that let end users of a 'Shiny' application author their own R functions directly in the browser. Host apps can expose these modules as extension points where user-supplied code augments or replaces built-in logic, without requiring users to modify the app's source. Each module embeds an 'Ace' editor with a structured argument table, an in-frame R console rooted in the paused function's local environment, and a step debugger that handles for, while, repeat, and if/else blocks at any nesting depth. Two module flavours are provided: solo editors for testing a function in isolation with literal argument values, and embedded editors for pausing a function mid-execution inside a larger host program.

Last updated

3.30 score

gim - Generalized Integration Model

Implements the generalized integration model, which integrates individual-level data and summary statistics under a generalized linear model framework. It supports continuous and binary outcomes to be modeled by the linear and logistic regression models. For binary outcome, data can be sampled in prospective cohort studies or case-control studies. Described in Zhang et al. (2020)<doi:10.1093/biomet/asaa014>.

Last updated

3.00 score 5 scripts 136 downloads