What's Changed
- sweet_spot_plot() added to make it easier to graphically summarise simulation results
- The following simulation functions added to make it easier to simulate binary and time to event data:
- bootstrap_cov() Bootstrap Covariate Data
- calc_cond_binary() and calc_cond_weibull() to convert a vector of drift and treatment effects for the conditional to the marginal scale
- inv_logit()
- sim_accrual() to simulate participant accrual times
- sim_weib_ph() to simulate event times for each participant from a Weibull proportional hazards regression model
- sim_pw_const_haz() to simulate event times for each individual from a piecewise constant hazard model
- calc_cond_weibull() to calculate conditional drift and treatment effect for time-to-event outcome models
- calc_study_duration() to calculate the analysis time based on a target number of events and/or target follow-up time
- trim_ps() and rescale_ps() functions added to trim and re-scale the propensity score object
- prop_scr_cloud() function added to visualise propensity scores
In addition we have added template simulation code into inst/templates. These additions should make it significantly easier to not just do IPW BDB, but to simulate how the addition of historical data will affect the operating characteristics of your trial.