rbmi1.3.1 package

Add a class

Creates a draws object

Create an imputation object

Convert indicator to index

Extract imputed dataset

Extract imputed datasets

Extract parameters from a MMRM model

Fit the base imputation model using a Bayesian approach

Von Hippel and Bartlett pooling of BMLMI method

Create object of sample_single class

Simulate a realistic example dataset

Creates a stack object populated with jackknife samples

Fit MMRM and returns parameter estimates

Get Pattern Summary

Determine patients missingness group

Expected Pool Components

Get session hash

Get Compiled Stan Object

Derive visit distribution parameters

Internal Pool Methods

Convert object to dataframe

Create and validate a sample_list object

Diagnostics of the MCMC

Compute covariance matrix for some reference-based methods (JR, CIR)

Convert list of imputation_list_single() objects to an `imputation_l...

Calculate delta from a lagged scale coefficient

Create a delta data.frame template

Sample random values from the multivariate normal distribution

Validate draws object

Validate is_mar for a given subject

Validate inputs for vars

Generic validation method

Validate user supplied references

Validate sample_list object

Validate sample_single object

Validate a simul_pars object

Validate a stan_data object

Set Class

as_cropped_char

Adjust trajectory of a subject's outcome due to the intercurrent event...

Adjust trajectories due to the intercurrent event (ICE)

Analyse Multiple Imputed Datasets

Implements an Analysis of Covariance (ANCOVA)

Analysis of Covariance

Applies delta adjustment

Construct an analysis object

as_ascii_table

Creates a "MMRM" ready dataset

Create MMRM formula

Expand data.frame into a design matrix

Creates a simple formula object from a string

As array

Create vector of Stratas

Assert that all variables exist within a dataset

Convert character variables to factor

Diagnostics of the MCMC based on ESS

Diagnostics of the MCMC based on HMC-related measures.

Fit the base imputation model and get parameter estimates

Ensure rstan exists

Evaluate a call to mmrm

Expand and fill in missing data.frame rows

Extract Variables from string vector

Set to NA outcome values that would be MNAR if they were missing (i.e....

Extract draws from a stanfit object

Fit a MMRM model

Generate data for a single group

Creates a stack object populated with bootstrapped samples

Derive conditional multivariate normal parameters

Get delta utility variables

Fit the base imputation model on bootstrap samples

Extract the Effective Sample Size (ESS) from a stanfit object

Get imputation strategies

Does object have a class ?

if else

Create a valid imputation_df object

List of imputations_df

A collection of imputation_singles() grouped by a single subjid ID

Create a valid imputation_single object

Impute data for a single subject

Construct random effects formula

Create imputed datasets

Sample outcome value

Create imputed datasets

Invert and derive indexes

invert

Is value absent

Is character or factor

Is single character

Is package in development mode?

Is character, factor or numeric

Last Observation Carried Forward

R6 Class for Storing / Accessing & Sampling Longitudinal Data

Calculate design vector for the lsmeans

Least Square Means

Create a rbmi ready cluster

Set the multiple imputation methodology

Parallelise Lapply

Calculate parametric confidence intervals

Bootstrap Pooling via normal approximation

Bootstrap Pooling via Percentiles

Sample Patient Ids

Pool analysis results obtained from the imputed datasets

Prepare input data to run the Stan model

Print analysis object

Print draws object

Print imputation object

R6 Class for printing current sampling progress

P-value of percentile bootstrap

QR decomposition

rbmi: Reference Based Multiple Imputation

rbmi settings

Capture all Output

recursive_reduce

Remove subjects from dataset if they have no observed values

Barnard and Rubin degrees of freedom adjustment

Combine estimates using Rubin's rules

R6 Class for scaling (and un-scaling) design matrices

Set simulation parameters of a study group.

Set key variables

Generate data

Simulate drop-out

Simulate intercurrent event

Create simulated datasets

Sort data.frame

Transform array into list of arrays

Split a flat list of imputation_single() into multiple `imputation_d...

R6 Class for a FIFO stack

Does a string contain a substring

Strategies

string_pad

Transpose imputations

Transpose results object

Transpose samples

Validate analysis results

Validate a longdata object

Validate user specified strategies

Validate analysis objects