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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.

These binaries (installable software) and packages are in development.
They may not be fully stable and should be used with caution. We make no claims about them.
Health stats visible at Monitor.