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Changelog

Source: NEWS.md

postcard 1.0.0

Major overhaul of package. Still focuses on analysing data with the use of prognostic scores, but takes a more general approach that allows any distribution of response and covariates within the scope of generalised linear models (GLMs) and does not necessarily run on a number of data sets created by simulation.

The package provides novel methods for:

  • rctglm: Finding any marginal effect estimand and estimating the standard error using influence functions to avoid inflation of type 1 error
  • rctglm_with_prognosticscore: Do the above, but leveraging historical data to increase precision with prognostic scores.

Additionally, the package includes functionalities for

  • fitting a discrete super learner in fit_best_learner, which is leveraged in rctglm_with_prognosticscore
  • approximating power using
    • standard methods for ANCOVA models (see help topic power_linear)
    • a novel method for any model estimating marginal effects (power_marginaleffect)
  • generating data from a GLM (glm_data)

postcard 0.2.1

Features

  • Added function simulate_collection that takes function arguments for how to simulate covariates and model the outcome in the historical and “current” data to give the user full flexibility (previously a multivariate normal distribution was assumed)

    • sim.lm which simulates data from a multivariate normal distribution and models the outcome with a linear model is now a wrapper of the new - more general - simulate_collection.

postcard 0.2.0

Features

  • Added option to use sandwich HC estimators for the covariance matrix in sim.lm

  • Updated default value of ATE_shift in sim.lm

Code cleanup

  • Modularised code. Fx. split lm.hist into lm.procova and lm.psm

  • Renamed some functionalities

  • Correcting errors in documentation

Best practices package

  • Updated DESCRIPTION

  • Created README

  • Added explicit package imports in form of foo::xx

  • Added a few tests

postcard 0.1.0

Initial package created from local files. Package contains functionalities to create simulation study for a specific purpose related to an article. Functionalities include generation of a collection of data sets and a way to analyse these data sets assuming a special case of multivariate normal distribution of covariates with a linear model of the response. In addition, functionalities to estimate the power of certain parameter tests based on the results.