Extended Progression Models for Repeated Measures

Extended progression models for repeated measures in progressive disease clinical trials. Provides nonlinear mixed-effects progression models (linear, plateau, Emax, exponential decay), delayed treatment onset estimation by profile likelihood, joint longitudinal-survival models for informative dropout, competing-risks extensions, biomarker-informed adaptive dosing rules, and simulation-based power and sample-size calculation. The progression-model view of treatment effects, in which effects are characterised on the underlying disease trajectory rather than as additive shifts on the outcome scale, follows Raket (2022) . The nonlinear mixed-effects machinery is built on the 'nlme' implementation described by Pinheiro and Bates (2000, ISBN 978-0-387-98957-0), and the joint modelling of longitudinal and time-to-event data follows Rizopoulos (2012, ISBN 978-1-4398-7286-4).

progme

Extended Progression Models for Repeated Measures.

Tools for progressive-disease clinical trials: nonlinear mixed-effects progression models, delayed treatment onset estimation via profile likelihood, joint longitudinal-survival models for informative dropout, competing risks, biomarker-informed adaptive dosing, and simulation-based power/sample-size calculation.

Install

# install.packages("remotes")
remotes::install_github("MukulBijalwan/progme")

Quick start

library(progme)

# Simulate a trial with a 6-month delayed treatment effect
sim <- simulate_progression_trial(n_control = 60, n_treatment = 60,
                                  effect_size = -0.5, delay = 6,
                                  times = seq(0, 24, by = 3))

# Estimate the delay by profile likelihood
fit <- pmrm_delayed(y ~ time * trt, data = sim,
                    delay_estimation = "profile",
                    delay_grid = seq(0, 18, by = 3))
print(fit)
plot(fit, type = "profile_likelihood")

# Simulation-based power
p <- power_pmrm(n_per_arm = 100, effect_size = -0.5, delay = 6, n_sims = 100)
p$power