make_dosing_df: Create Subject-by-Time Dosing Records (Base R) with Optional...

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Create Subject-by-Time Dosing Records (Base R) with Optional Subject-Level Covariates

Description

Creates a dosing/event-style dataset by expanding a set of subject IDs ('ID') across a vector of dosing times ('time'). It assigns a dose value to each subject and time combination, and optionally adds additional columns via '...' that can be constant (length 1) or subject-specific (length equal to 'length(ID)').

This utility is useful for quickly generating dosing records for simulation, exploratory analyses, and creating NONMEM-/nlmixr-/Monolix-friendly input structures where each row represents a dosing event (or event-like record) at a given time.

Usage

make_dosing_df(ID, time, dose, ...)

Arguments

ID	A vector of subject identifiers (numeric, integer, or character). Typically unique.
time	A vector of dosing times. Can be numeric or integer (e.g., hours, days).
dose	Dose value(s). May be: length 1: one common dose applied to all subjects and all times length equal to 'length(ID)': subject-specific doses mapped by the *position* of each subject in 'ID' (i.e., 'dose[i]' corresponds to 'ID[i]'), then repeated across all times for that subject
...	Named additional columns to include in the output. Each argument in '...' must be either: length 1: a constant value repeated for all rows (e.g., 'STUDY = "CNTO1275"') length equal to 'length(ID)': subject-level values mapped by the *position* of each subject in 'ID' (e.g., 'WT = c(30, 45)' with 'ID = c(1, 2)' assigns 'WT=30' to subject 1 and 'WT=45' to subject 2 across all times) All '...' arguments must be named. Names must not conflict with reserved output column names ('ID', 'time', 'dose').

Details

## Mapping rule (important) For any vector argument of length 'length(ID)' (including 'dose' or any '...' column), values are assigned by the *index/position* of 'ID', not by sorted ID or factor level. For example:

ID   = c(10, 20)
VAR5 = c(4,  6)

results in subject '10 -> 4' and subject '20 -> 6' for all time points.

## Duplicate IDs If you supply any subject-level vector (i.e., 'dose' length > 1, or any '...' argument length > 1), 'ID' must be unique. Duplicate IDs create ambiguous mappings and will trigger an error.

## Output structure The output includes:

• 'ID': subject identifier

• 'time': dosing time

• 'dose': assigned dose

• any additional columns provided via '...'

Rows are sorted by 'ID' then 'time'.

## When to use (common scenarios)

1. **Create dosing records for simulation** (e.g., one dose level across many subjects, or subject-specific doses).

2. **Build NONMEM/nlmixr event datasets** where dosing rows (e.g., 'EVID=1') are generated programmatically; you can add 'EVID', 'CMT', 'RATE', 'STUDY', etc. via '...'.

3. **Explore exposure metrics** by generating standardized schedules (e.g., QD times) and attaching covariates like weight, age group, cohort, regimen labels.

4. **Scenario testing** for pediatric dosing cutoffs, titration strategies (subject-specific), or bridging comparisons where regimen differs by subgroup.

## When not to use Use a different approach if you need:

• time-varying covariates with length equal to 'length(time)' (shared across subjects),

• fully specified matrices of values length 'length(ID) * length(time)',

• multiple event types (dose + observations) in the same call (although you can merge the result with an observation dataset after creation).

Value

A 'data.frame' with 'length(ID) * length(time)' rows and columns 'ID', 'time', 'dose', plus any additional columns passed via '...'.

Examples

## Example 1: Simple dataset (10 subjects, dose 40 at times 1:10)
df1 <- make_dosing_df(ID = 1:10, time = 1:10, dose = 40)
head(df1)

## Example 2: Subject-specific dose (one dose per subject)
df2 <- make_dosing_df(ID = c(1, 2, 3), time = 1:5, dose = c(40, 60, 80))
head(df2)

## Example 3: Add constant columns via ...
df3 <- make_dosing_df(
  ID = 1:3, time = c(0, 7, 14), dose = 45,
  STUDY = "CNTO1275JPA3001", ROUTE = "SC", CMT = 1
)
df3

## Example 4: Add subject-level covariates via ...
## VAR5 is mapped by position: ID=1 gets 4, ID=2 gets 6
df4 <- make_dosing_df(
  ID = c(1, 2), time = 1:3, dose = 40,
  VAR5 = c(4, 6), WT = c(35.2, 51.0), SEX = c("M", "F")
)
df4

## Example 5: NONMEM-style dosing rows (add EVID and AMT)
df_nm <- make_dosing_df(ID = 1:2, time = c(0, 28, 56), dose = c(45, 45),
                        EVID = 1, AMT = NA)  # AMT placeholder (optional)
df_nm$AMT <- df_nm$dose
df_nm

quickcode documentation built on April 4, 2026, 9:06 a.m.