mmrm - Mixed Models for Repeated Measures

Mixed models for repeated measures (MMRM) are a popular choice for analyzing longitudinal continuous outcomes in randomized clinical trials and beyond; see Cnaan, Laird and Slasor (1997) <doi:10.1002/(SICI)1097-0258(19971030)16:20%3C2349::AID-SIM667%3E3.0.CO;2-E> for a tutorial and Mallinckrodt, Lane, Schnell, Peng and Mancuso (2008) <doi:10.1177/009286150804200402> for a review. This package implements MMRM based on the marginal linear model without random effects using Template Model Builder ('TMB') which enables fast and robust model fitting. Users can specify a variety of covariance matrices, weight observations, fit models with restricted or standard maximum likelihood inference, perform hypothesis testing with Satterthwaite or Kenward-Roger adjustment, and extract least square means estimates by using 'emmeans'.

Last updated

cpp

13.32 score 157 stars 9 dependents 185 scripts 8.2k downloads

DoseFinding - Planning and Analyzing Dose Finding Experiments

The DoseFinding package provides functions for the design and analysis of dose-finding experiments (with focus on pharmaceutical Phase II clinical trials). It provides functions for: multiple contrast tests, fitting non-linear dose-response models (using Bayesian and non-Bayesian estimation), calculating optimal designs and an implementation of the MCPMod methodology (Pinheiro et al. (2014) <doi:10.1002/sim.6052>).

Last updated

openblas

11.25 score 14 stars 9 dependents 111 scripts 5.7k downloads

crmPack - Object-Oriented Implementation of Dose Escalation Designs

Implements a wide range of dose escalation designs. The focus is on model-based designs, ranging from classical and modern continual reassessment methods (CRMs) based on dose-limiting toxicity endpoints to dual-endpoint designs taking into account a biomarker/efficacy outcome. Bayesian inference is performed via MCMC sampling in JAGS, and it is easy to setup a new design with custom JAGS code. However, it is also possible to implement 3+3 designs for comparison or models with non-Bayesian estimation. The whole package is written in a modular form in the S4 class system, making it very flexible for adaptation to new models, escalation or stopping rules. Further details are presented in Sabanés Bové et al. (2019) <doi:10.18637/jss.v089.i10>.

Last updated

jagscpp

10.11 score 22 stars 232 scripts 360 downloads

rbmi - Reference Based Multiple Imputation

Implements standard and reference based multiple imputation methods for continuous longitudinal endpoints (Gower-Page et al. (2022) <doi:10.21105/joss.04251>). In particular, this package supports deterministic conditional mean imputation and jackknifing as described in Wolbers et al. (2022) <doi:10.1002/pst.2234>, Bayesian multiple imputation as described in Carpenter et al. (2013) <doi:10.1080/10543406.2013.834911>, and bootstrapped maximum likelihood imputation as described in von Hippel and Bartlett (2021) <doi:10.1214/20-STS793>.

Last updated

10.00 score 21 stars 4 dependents 104 scripts 424 downloads

brms.mmrm - Bayesian MMRMs using 'brms'

The mixed model for repeated measures (MMRM) is a popular model for longitudinal clinical trial data with continuous endpoints, and 'brms' is a powerful and versatile package for fitting Bayesian regression models. The 'brms.mmrm' R package leverages 'brms' to run MMRMs, and it supports a simplified interfaced to reduce difficulty and align with the best practices of the life sciences. References: Bürkner (2017) <doi:10.18637/jss.v080.i01>, Mallinckrodt (2008) <doi:10.1177/009286150804200402>.

Last updated

brmslife-sciencesmc-stanmmrmstanstatistics

8.26 score 24 stars 17 scripts 722 downloads

RobinCar2 - ROBust INference for Covariate Adjustment in Randomized Clinical Trials

Performs robust estimation and inference when using covariate adjustment and/or covariate-adaptive randomization in randomized controlled trials. This package is trimmed to reduce the dependencies and validated to be used across industry. See "FDA's final guidance on covariate adjustment"<https://www.regulations.gov/docket/FDA-2019-D-0934>, Tsiatis (2008) <doi:10.1002/sim.3113>, Bugni et al. (2018) <doi:10.1080/01621459.2017.1375934>, Ye, Shao, Yi, and Zhao (2023)<doi:10.1080/01621459.2022.2049278>, Ye, Shao, and Yi (2022)<doi:10.1093/biomet/asab015>, Rosenblum and van der Laan (2010)<doi:10.2202/1557-4679.1138>, Wang et al. (2021)<doi:10.1080/01621459.2021.1981338>, Ye, Bannick, Yi, and Shao (2023)<doi:10.1080/24754269.2023.2205802>, and Bannick, Shao, Liu, Du, Yi, and Ye (2024)<doi:10.48550/arXiv.2306.10213>.

Last updated

6.74 score 14 stars 8 scripts 551 downloads

graphicalMCP - Graphical Multiple Comparison Procedures

Multiple comparison procedures (MCPs) control the familywise error rate in clinical trials. Graphical MCPs include many commonly used procedures as special cases; see Bretz et al. (2011) <doi:10.1002/bimj.201000239>, Lu (2016) <doi:10.1002/sim.6985>, and Xi et al. (2017) <doi:10.1002/bimj.201600233>. This package is a low-dependency implementation of graphical MCPs which allow mixed types of tests. It also includes power simulations and visualization of graphical MCPs.

Last updated

6.72 score 19 stars 31 scripts 806 downloads

rbmiUtils - Utility Functions to Support and Extend the 'rbmi' Package

Provides utility functions that extend the capabilities of the reference-based multiple imputation package 'rbmi'. It supports clinical trial analysis workflows with functions for managing imputed datasets, applying analysis methods across imputations, and tidying results for reporting.

Last updated

multiple-imputationrbmiutils

6.04 score 4 stars 23 scripts 210 downloads

pmrm - Progression Models for Repeated Measures

A progression model for repeated measures (PMRM) is a continuous-time nonlinear mixed-effects model for longitudinal clinical trials in progressive diseases. Unlike mixed models for repeated measures (MMRMs), which estimate treatment effects as linear combinations of additive effects on the outcome scale, PMRMs characterize treatment effects in terms of the underlying disease trajectory. This framing yields clinically interpretable quantities such as average time saved and percent reduction in decline due to treatment. This package implements frequentist PMRMs by Raket (2022) <doi:10.1002/sim.9581> using 'RTMB' by Kristensen (2016) <doi:10.18637/jss.v070.i05>.

Last updated

adcompdisease-progression-modelmmrmpmrmrtmbtmb

5.80 score 6 stars 2 scripts 507 downloads

simaerep - Detect Clinical Trial Sites Over- or Under-Reporting Clinical Events

Monitoring reporting rates of subject-level clinical events (e.g. adverse events, protocol deviations) reported by clinical trial sites is an important aspect of risk-based quality monitoring strategy. Sites that are under-reporting or over-reporting events can be detected using bootstrap simulations during which patients are redistributed between sites. Site-specific distributions of event reporting rates are generated that are used to assign probabilities to the observed reporting rates. (Koneswarakantha 2024 <doi:10.1007/s43441-024-00631-8>).

Last updated

ae-reportingclinical-trials

5.39 score 27 stars 20 scripts 209 downloads

beeca - Binary Endpoint Estimation with Covariate Adjustment

Performs estimation of marginal treatment effects for binary outcomes when using logistic regression working models with covariate adjustment (see discussions in Magirr et al (2024) <https://osf.io/9mp58/>). Implements the variance estimators of Ge et al (2011) <doi:10.1177/009286151104500409> and Ye et al (2023) <doi:10.1080/24754269.2023.2205802>.

Last updated

ardcovariate-adjustmentdelta-methodmarginal-effectsmarginal-estimand

5.36 score 7 stars 1 dependents 11 scripts 645 downloads

savvyr - Survival Analysis for AdVerse Events with VarYing Follow-Up Times

The SAVVY (Survival Analysis for AdVerse Events with VarYing Follow-Up Times) project is a consortium of academic and pharmaceutical industry partners that aims to improve the analyses of adverse event (AE) data in clinical trials through the use of survival techniques appropriately dealing with varying follow-up times and competing events, see Stegherr, Schmoor, Beyersmann, et al. (2021) <doi:10.1186/s13063-021-05354-x>. Although statistical methodologies have advanced, in AE analyses often the incidence proportion, the incidence density or a non-parametric Kaplan-Meier estimator are used, which either ignore censoring or competing events. This package contains functions to easily conduct the proposed improved AE analyses.

Last updated

5.08 score 6 stars 1 scripts 553 downloads

elaborator - A 'shiny' Application for Exploring Laboratory Data

A novel concept for generating knowledge and gaining insights into laboratory data. You will be able to efficiently and easily explore your laboratory data from different perspectives. Janitza, S., Majumder, M., Mendolia, F., Jeske, S., & Kulmann, H. (2021) <doi:10.1007/s43441-021-00318-4>.

Last updated

clinical-trialsdata-insightslaboratory-dataqualitative-trend-analysisreference-valuesshiny-appsvisualization

5.03 score 9 stars 200 downloads

bfboin - Operating Characteristics for the Bayesian Optimal Interval Design with Back Filling

Calculate the operating characteristics of the Bayesian Optimal Interval with Back Filling Design for dose escalation in early-phase oncology trials.

Last updated

4.78 score 2 stars 7 scripts 170 downloads

roxylint - Lint 'roxygen2'-Generated Documentation

Provides formatting linting to 'roxygen2' tags. Linters report 'roxygen2' tags that do not conform to a standard style. These linters can be a helpful check for building more consistent documentation and to provide reminders about best practices or checks for typos. Default linting suites are provided for common style guides such as the one followed by the 'tidyverse', though custom linters can be registered by other packages or be custom-tailored to a specific package.

Last updated

linterroxygen2

3.93 score 17 stars 175 downloads

roxytypes - Typed Parameter Tags for Integration with 'roxygen2'

Provides typed parameter documentation tags for integration with 'roxygen2'. Typed parameter tags provide a consistent interface for annotating expected types for parameters and returned values. Tools for converting from existing styles are also provided to easily adapt projects which implement typed documentation by convention rather than tag. Use the default format or provide your own.

Last updated

roxygen2type-hints

3.60 score 8 stars 1 scripts 187 downloads

mtdesign - Mander and Thompson Designs

Implements Mander & Thompson's (2010) <doi:10.1016/j.cct.2010.07.008> methods for two-stage designs optimal under the alternative hypothesis for phase II [cancer] trials. Also provides an implementation of Simon's (1989) <doi:10.1016/0197-2456(89)90015-9> original methodology and allows exploration of the operating characteristics of sub-optimal designs.

Last updated

cpp

3.48 score 3 stars 4 scripts 592 downloads