# Data Preparation and Validation

#### 2026-02-16

# 1 Introduction

This vignette demonstrates how to prepare and validate data before running multiple imputation with `{rbmi}`. The `{rbmiUtils}` package provides three key functions for this workflow:

- `validate_data()`: Pre-flight validation to catch common data issues
- `prepare_data_ice()`: Build intercurrent event data from flag columns
- `summarise_missingness()`: Understand missing data patterns

Using these functions helps ensure your imputation will run successfully and gives you insight into the structure of your missing data.

# 2 Setup

    library(dplyr)
    library(rbmi)
    library(rbmiUtils)

# 3 Example Data

We’ll create a small example dataset to demonstrate the functions:

    set.seed(42)

    dat <- data.frame(
     USUBJID = factor(rep(paste0("SUBJ-", 1:20), each = 4)),
     AVISIT = factor(
       rep(c("Week 4", "Week 8", "Week 12", "Week 16"), 20),
       levels = c("Week 4", "Week 8", "Week 12", "Week 16")
     ),
     TRT = factor(rep(c("Placebo", "Drug A"), each = 40)),
     BASE = rep(round(rnorm(20, 50, 10), 1), each = 4),
     STRATA = factor(rep(sample(c("Low", "High"), 20, replace = TRUE), each = 4))
    )

    # Generate CHG with some missing values
    dat$CHG <- round(rnorm(80, mean = -2, sd = 3), 1)

    # Create missing data patterns:
    # - Subjects 3, 8: monotone dropout at Week 12
    # - Subject 15: intermittent missing at Week 8
    # - Subject 18: monotone dropout at Week 16
    dat$CHG[dat$USUBJID == "SUBJ-3" & dat$AVISIT %in% c("Week 12", "Week 16")] <- NA
    dat$CHG[dat$USUBJID == "SUBJ-8" & dat$AVISIT %in% c("Week 12", "Week 16")] <- NA
    dat$CHG[dat$USUBJID == "SUBJ-15" & dat$AVISIT == "Week 8"] <- NA
    dat$CHG[dat$USUBJID == "SUBJ-18" & dat$AVISIT == "Week 16"] <- NA

    # Add discontinuation flag
    dat$DISCFL <- ifelse(
     dat$USUBJID %in% c("SUBJ-3", "SUBJ-8") & dat$AVISIT == "Week 12",
     "Y",
     ifelse(
       dat$USUBJID == "SUBJ-18" & dat$AVISIT == "Week 16",
       "Y",
       "N"
     )
    )

    head(dat, 12)
    #>    USUBJID  AVISIT     TRT BASE STRATA  CHG DISCFL
    #> 1   SUBJ-1  Week 4 Placebo 63.7    Low -0.6      N
    #> 2   SUBJ-1  Week 8 Placebo 63.7    Low  0.1      N
    #> 3   SUBJ-1 Week 12 Placebo 63.7    Low  1.1      N
    #> 4   SUBJ-1 Week 16 Placebo 63.7    Low -3.8      N
    #> 5   SUBJ-2  Week 4 Placebo 44.4    Low -0.5      N
    #> 6   SUBJ-2  Week 8 Placebo 44.4    Low -7.2      N
    #> 7   SUBJ-2 Week 12 Placebo 44.4    Low -4.4      N
    #> 8   SUBJ-2 Week 16 Placebo 44.4    Low -4.6      N
    #> 9   SUBJ-3  Week 4 Placebo 53.6   High -9.2      N
    #> 10  SUBJ-3  Week 8 Placebo 53.6   High -1.9      N
    #> 11  SUBJ-3 Week 12 Placebo 53.6   High   NA      Y
    #> 12  SUBJ-3 Week 16 Placebo 53.6   High   NA      N

# 4 Define Variables

    vars <- set_vars(
     subjid = "USUBJID",
     visit = "AVISIT",
     group = "TRT",
     outcome = "CHG",
     covariates = c("BASE", "STRATA"),
     strategy = "STRATEGY"
    )

# 5 Validating Data

The `validate_data()` function performs comprehensive checks on your data before imputation:

    # This will pass validation
    validate_data(dat, vars)

The function checks:

- Data is a data.frame
- All required columns exist (subjid, visit, group, outcome, covariates)
- Factor columns are properly typed
- Outcome column is numeric
- Covariates have no missing values
- No duplicate subject-visit combinations
- If `data_ice` is provided: valid subjects, visits, and strategies

## 5.1 Catching Validation Errors

Here’s an example of how validation catches issues:

    # Create problematic data
    bad_dat <- dat
    bad_dat$TRT <- as.character(bad_dat$TRT)  # Should be factor
    bad_dat$BASE[1] <- NA  # Covariate with missing value

    # This will report all issues at once
    tryCatch(
     validate_data(bad_dat, vars),
     error = function(e) cat(e$message)
    )
    #> Warning: 1 column is character instead of factor.
    #> ℹ Column: TRT.
    #> ℹ `rbmi::draws()` will auto-coerce, but explicit conversion gives you control
    #>   over level ordering.
    #> ℹ Example: `data$TRT <- factor(data$TRT)`
    #> Data validation failed.

# 6 Summarising Missing Data

Before imputation, it’s important to understand your missing data patterns:

    miss <- summarise_missingness(dat, vars)

## 6.1 Missing by Visit

    print(miss$by_visit)
    #> # A tibble: 8 × 5
    #>   visit   group       n n_miss pct_miss
    #>   <fct>   <fct>   <int>  <int>    <dbl>
    #> 1 Week 4  Drug A     10      0        0
    #> 2 Week 4  Placebo    10      0        0
    #> 3 Week 8  Drug A     10      1       10
    #> 4 Week 8  Placebo    10      0        0
    #> 5 Week 12 Drug A     10      0        0
    #> 6 Week 12 Placebo    10      2       20
    #> 7 Week 16 Drug A     10      1       10
    #> 8 Week 16 Placebo    10      2       20

## 6.2 Subject Patterns

    print(miss$patterns)
    #> # A tibble: 20 × 4
    #>    USUBJID TRT     pattern      dropout_visit
    #>    <fct>   <chr>   <chr>        <chr>
    #>  1 SUBJ-1  Placebo complete     <NA>
    #>  2 SUBJ-2  Placebo complete     <NA>
    #>  3 SUBJ-3  Placebo monotone     Week 12
    #>  4 SUBJ-4  Placebo complete     <NA>
    #>  5 SUBJ-5  Placebo complete     <NA>
    #>  6 SUBJ-6  Placebo complete     <NA>
    #>  7 SUBJ-7  Placebo complete     <NA>
    #>  8 SUBJ-8  Placebo monotone     Week 12
    #>  9 SUBJ-9  Placebo complete     <NA>
    #> 10 SUBJ-10 Placebo complete     <NA>
    #> 11 SUBJ-11 Drug A  complete     <NA>
    #> 12 SUBJ-12 Drug A  complete     <NA>
    #> 13 SUBJ-13 Drug A  complete     <NA>
    #> 14 SUBJ-14 Drug A  complete     <NA>
    #> 15 SUBJ-15 Drug A  intermittent <NA>
    #> 16 SUBJ-16 Drug A  complete     <NA>
    #> 17 SUBJ-17 Drug A  complete     <NA>
    #> 18 SUBJ-18 Drug A  monotone     Week 16
    #> 19 SUBJ-19 Drug A  complete     <NA>
    #> 20 SUBJ-20 Drug A  complete     <NA>

## 6.3 Summary by Treatment Group

    print(miss$summary)
    #> # A tibble: 2 × 5
    #>   group   n_subjects n_complete n_monotone n_intermittent
    #>   <chr>        <int>      <int>      <int>          <int>
    #> 1 Drug A          10          8          1              1
    #> 2 Placebo         10          8          2              0

The three pattern types are:

- complete: No missing outcome values
- monotone: Once a value is missing, all subsequent visits are also missing (dropout)
- intermittent: Missing values with observed values both before and after

# 7 Preparing ICE Data

When subjects discontinue treatment, you may want to apply reference-based imputation strategies (see the `{rbmi}` documentation for details on intercurrent event handling). The `prepare_data_ice()` function builds the required `data_ice` data.frame from a discontinuation flag:

    data_ice <- prepare_data_ice(
     data = dat,
     vars = vars,
     ice_col = "DISCFL",
     strategy = "JR"  # Jump to Reference
    )

    print(data_ice)
    #>   USUBJID  AVISIT STRATEGY
    #> 1 SUBJ-18 Week 16       JR
    #> 2  SUBJ-3 Week 12       JR
    #> 3  SUBJ-8 Week 12       JR

The function:

- Identifies subjects with ICE flags (`"Y"`, `TRUE`, or `1`)
- Takes the first flagged visit per subject
- Assigns the specified imputation strategy

Available strategies are:

- `"MAR"`: Missing at Random
- `"CR"`: Copy Reference
- `"JR"`: Jump to Reference
- `"CIR"`: Copy Increment from Reference
- `"LMCF"`: Last Mean Carried Forward

# 8 Complete Workflow

Here’s how these functions fit into a typical `{rbmi}` workflow:

    library(rbmi)
    library(rbmiUtils)

    # 1. Validate data
    validate_data(dat, vars)

    # 2. Understand missing patterns
    miss <- summarise_missingness(dat, vars)
    print(miss$summary)

    # 3. Prepare ICE data if needed
    data_ice <- prepare_data_ice(dat, vars, ice_col = "DISCFL", strategy = "JR")

    # 4. Define method
    method <- method_bayes(
     n_samples = 100,
     control = control_bayes(warmup = 200, thin = 2)
    )

    # 5. Run imputation
    draws_obj <- draws(
     data = dat,
     vars = vars,
     data_ice = data_ice,
     method = method
    )

    # 6. Continue with impute() and analyse()

# 9 Summary

The data preparation functions in `{rbmiUtils}` help you:

1. Catch issues early with `validate_data()` before running time-consuming imputations
2. Understand your data with `summarise_missingness()` to characterize missing data patterns
3. Simplify ICE handling with `prepare_data_ice()` to build `data_ice` from flag columns

These utilities complement the core `{rbmi}` package and support reproducible, well-documented analysis workflows.

After data preparation, see `vignette('pipeline')` for the complete analysis workflow from imputation through to regulatory tables.