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simtrial \u003cimg src=\"man/figures/logo.png\" align=\"right\" width=\"120\" /\u003e\n\n\u003c!-- badges: start --\u003e\n[![R-CMD-check](https://github.com/Merck/simtrial/actions/workflows/R-CMD-check.yaml/badge.svg)](https://github.com/Merck/simtrial/actions/workflows/R-CMD-check.yaml)\n[![Codecov test coverage](https://codecov.io/gh/Merck/simtrial/branch/main/graph/badge.svg)](https://app.codecov.io/gh/Merck/simtrial?branch=main)\n[![CRAN status](https://www.r-pkg.org/badges/version/simtrial)](https://cran.r-project.org/package=simtrial)\n[![CRAN Downloads](https://cranlogs.r-pkg.org/badges/simtrial)](https://cran.r-project.org/package=simtrial)\n\u003c!-- badges: end --\u003e\n\nsimtrial is a fast and extensible clinical trial simulation framework\nfor time-to-event endpoints.\n\n## Installation\n\nThe easiest way to get simtrial is to install from CRAN:\n\n```r\ninstall.packages(\"simtrial\")\n```\n\nAlternatively, to use a new feature or get a bug fix,\nyou can install the development version of simtrial from GitHub:\n\n```r\n# install.packages(\"remotes\")\nremotes::install_github(\"Merck/simtrial\")\n```\n\n## Overview\n\nsimtrial is intended to be a general purpose tool for simulating fixed, group sequential or adaptive clinical trials.\nIt allows stratified populations and flexible parameters for generating enrollment, event times, dropout times.\nIt takes care of bookkeeping to enable easily going from data generation to creating analysis datasets for evaluation of standard or innovative designs and testing procedures.\nFor a single endpoint, it will easily generate trials with multiple arms (e.g., a single or multiple experimental arms versus a common control) and multiple study populations (e.g., overall population and biomarker positive).\nWhile tools are built into the package for logrank and weighted logrank tests, arbitrary testing and estimation procedures are easily applied.\nIn addition to weighted logrank tests, we support combinations of weighted logrank tests (e.g., the MaxCombo test).\nThe package used piecewise constant enrollment, failure and dropout rates as a simple model\nable to approximate arbitrary distributions easily.\nThis model also enables simulating non-proportional hazards assumptions that are transparent for users to explain to non-statistical collaborators.\n\nsimtrial is designed with a core philosophy of basing most computations on\nefficient table transformations and to have a package that is easy to qualify\nfor use in regulated environments.\nIt utilizes the blazingly fast data.table for tabular data processing,\nenhanced by C++ implementations to ensure optimal performance.\nHowever, it does not require the user to be a data.table or C++ user.\n\nInitial areas of focus are:\n\n- Generating time-to-event data for stratified trials using piecewise constant\n  enrollment and piecewise exponential failure rates.\n  Both proportional and non-proportional hazards are supported.\n  Under proportional hazards, the assumptions are along the lines of those\n  used by Lachin and Foulkes as implemented in\n  [gsDesign](https://keaven.github.io/gsDesign/) for deriving\n  group sequential designs.\n- Setting up data cutoffs for (interim and final) analyses.\n- Support for weighted logrank tests with arbitrary weighting schemes,\n  specifically supporting the Fleming-Harrington set of tests,\n  including the logrank test.\n\n## Future developments\n\nExpectations for future development include:\n\n- Provide a test suite to document that the package is fit for use in a\n  regulatory environment.\n- Further examples.\n","funding_links":[],"categories":[],"sub_categories":[],"project_url":"https://awesome.ecosyste.ms/api/v1/projects/github.com%2Fmerck%2Fsimtrial","html_url":"https://awesome.ecosyste.ms/projects/github.com%2Fmerck%2Fsimtrial","lists_url":"https://awesome.ecosyste.ms/api/v1/projects/github.com%2Fmerck%2Fsimtrial/lists"}