pmrm_data: Internal function to prepare data

In pmrm: Progression Models for Repeated Measures

Internal function to prepare data

Description

Clean and annotate the data to prepare it for modeling.

Usage

pmrm_data(
  data,
  outcome,
  time,
  patient,
  visit,
  arm,
  covariates = ~0,
  subset = FALSE
)

Arguments

data	A tibble or data frame with one row per patient visit. See the arguments below for specific requirements for the data.
outcome	Character string, name of the column in the data with the numeric outcome variable on the continuous scale. Could be a clinical measure of healthy or of disease severity. Baseline is part of the model, so the outcome should not already be a change from baseline. The vector of outcomes may have missing values, either with explicit NAs or with rows in the data missing for one or more visits.
time	Character string, name of the column in the data with the numeric time variable on the continuous scale. This time is the time since enrollment/randomization of each patient. A time value of 0 should indicate baseline.
patient	Character string, name of the column in the data with the patient ID. This vector could be a numeric, integer, factor, or character vector. pmrm automatically converts it into an unordered factor.
visit	Character string, name of the column in the data which indicates the study visit of each row. This column could be a numeric, integer, factor, or character vector. An ordered factor is highly recommended because pmrm with levels assumed to be in chronological order. The minimum visit must be baseline.
arm	Character string, name of the column in the data which indicates the study arm of each row. This column could be a numeric, integer, factor, or character vector. An ordered factor is highly recommended because pmrm automatically converts data[[arm]] into an ordered factor anyway. The minimum level is assumed to be the control arm.
covariates	Partial right-sided formula of concomitant terms in the model for covariate adjustment (e.g. by age, gender, biomarker status, etc.). Usually does not include main variables such as the values of outcome, time, patient, visit, or arm. (If you do include any of these variables, be sure to check the fitted model for identifiability problems.) The columns in the data referenced in the formula must not have any missing values. Set covariates to ~ 0 (default) to opt out of covariate adjustment. The intercept term is removed from the model matrix W whether or not the formula begins with '~ 0.
subset	TRUE if data is a subset of data without all levels of visit or arm, FALSE to expect a full dataset when validating.

Value

A tibble of class "pmrm_data" with one row per patient visit. Rows with missing outcomes are removed, variables arm and visit are converted into ordered factors (with minimum values at the control arm and baseline, respectively), and then the rows are sorted by patient and visit. The "labels" attribute is a named list with the values of arguments outcome, time, visit, arm, and covariates.

pmrm documentation built on March 12, 2026, 5:07 p.m.