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The component check$checks is a data frame with the
following columns:
| Column | Description |
|---|---|
check |
The unique name of the validation check. |
passed |
Indicates whether the dataset passed the corresponding check. |
severity |
Classifies the result as "error",
"warning", or "information". |
standardize_can_fix |
Indicates whether the issue can be handled
automatically by DataStandard(), potentially with
drop = TRUE. |
requires_manual_resolution |
Indicates whether the issue must be corrected or reviewed manually before standardization or analysis. |
analysis_blocking |
Indicates whether failure of the check prevents the dataset from being considered analysis-ready. |
details |
Provides a concise summary of the observed result, including relevant counts, values, rows, subjects, or time points. |
recommendation |
Describes the recommended action for resolving or interpreting the result. |
| Check | What is evaluated | Corresponding report and diagnostics |
|---|---|---|
required_columns |
Verifies that all structural variables and mapped covariates are present in the input dataset. | Reports the number of required columns or lists the missing columns. Missing columns require manual correction of the data or mapping. |
nonempty_data |
Verifies that the input dataset contains at least one row. | Reports the number of detected rows. An empty dataset requires manual resolution. |
missing_id_or_time |
Identifies records with missing subject identifiers or time values. | Reports the affected original row numbers and stores them in
diagnostics$missing_id_time_rows. These rows may be removed
using drop = TRUE when appropriate. |
time_encoding |
Determines whether the time variable is numeric or can be converted unambiguously to numeric values. | Reports the original class and any invalid rows. Unambiguous character or factor encodings can be converted during standardization; ambiguous values require manual correction. |
mapping_time_endpoints |
Verifies that baseline_time and
cutoff_time are observed in the data and that
baseline_time <= cutoff_time. |
Reports the mapped endpoints and all observed time values. Invalid endpoints require correction of the mapping or time coding. |
analysis_time_grid |
Identifies all observed time points within the mapped baseline-to-cutoff window and verifies that both endpoints are included. | Reports the complete retained analysis-time grid. Every observed visit within the mapped window is included in the standardized grid. |
duplicate_id_time_records |
Detects duplicated subject–time combinations. | Reports the number of duplicated rows and affected subjects.
Detailed values are stored in diagnostics$duplicate_rows
and diagnostics$duplicate_subjects. Duplicates require a
manually specified aggregation or record-selection rule. |
complete_longitudinal_structure |
Determines whether each subject has a usable record at every retained analysis time. Duplicate records are evaluated separately. | Reports the number and percentage of complete subjects, the number
of incomplete subjects, and missing-subject counts by time. Details are
stored in diagnostics$incomplete_subjects and
diagnostics$missing_by_time. |
treatment_encoding |
Verifies that treatment is completely observed and encoded as binary
0/1, or through an explicitly convertible binary
representation. |
Reports the variable class, observed values, missing rows, invalid
rows, invalid values, and affected subjects. Detailed results are stored
in diagnostics$treatment_invalid_rows and
diagnostics$treatment_invalid_subjects. |
survival_encoding |
Verifies that survival status is completely observed and encoded as
binary 0/1, or through an explicitly convertible binary
representation. |
Reports the variable class, observed values, missing rows, invalid
rows, and invalid values. Invalid-row indices are stored in
diagnostics$survival_invalid_rows. |
treatment_consistency_within_subject |
Verifies that baseline treatment assignment remains constant within each subject over follow-up. | Reports the number and identifiers of subjects whose treatment value
changes. Affected identifiers are stored in
diagnostics$treatment_changes. |
survival_consistency_within_subject |
Verifies that a subject does not transition from S = 0
back to S = 1 at a later time. |
Reports the number and identifiers of subjects with impossible
survival transitions. Affected identifiers are stored in
diagnostics$impossible_survival_transitions. |
outcome_type_and_encoding |
Verifies that the outcome agrees with mapping$y_type:
binary outcomes must use valid binary values, whereas continuous
outcomes must contain finite numeric values. |
Reports the original class, observed values, and invalid or
non-finite rows. Invalid-row indices are stored in
diagnostics$outcome_invalid_rows. Unambiguous conversions
are performed by DataStandard(). |
structural_outcome_missingness_after_death |
Counts records for which S = 0 and
Y = NA. |
Returns an informational result reporting the number and percentage of structurally missing outcomes. These outcomes are expected and should not be imputed or replaced with observed zeros. |
outcome_observed_after_death |
Identifies records for which S = 0 but the outcome
remains observed. |
Reports the number and original row numbers of such records. Row
indices are stored in
diagnostics$outcome_observed_after_death_rows. Manual
verification is required. |
outcome_missingness_among_survivors |
Identifies ordinary outcome missingness among records with
S = 1. |
Reports the number of missing records, affected subjects, and counts
by analysis time. Details are stored in
diagnostics$outcome_missing_alive_rows and
diagnostics$outcome_missing_alive_by_time. |
missing_covariates |
Evaluates missingness in every mapped covariate. | Reports, for each covariate, the number of missing records, number
of affected subjects, and percentage of affected subjects. The complete
summary is stored in diagnostics$covariate_missing. |
time_coding_and_order |
Determines whether rows are ordered by subject and time and whether the analysis-time grid already uses consecutive integers beginning at zero. | Reports the observed raw time values and whether records are
correctly ordered. DataStandard() can sort the records and
map the retained time grid to 0, 1, ..., n. |
id_coding |
Determines whether subject identifiers are consecutive integers and whether records are correctly ordered. | Reports the identifier class, number of unique subjects, and whether
the coding is canonical. DataStandard() creates an ID audit
map and assigns consecutive integer identifiers. |
treatment_group_availability |
Verifies that both treatment groups are represented at baseline. | Reports the number of unique baseline subjects in treatment groups
0 and 1. Group counts are stored in
diagnostics$treatment_group_counts. |
covariate_variation |
Identifies mapped covariates that are constant or have near-zero variation. | Reports the names of problematic covariates and stores them in
diagnostics$near_zero_variation_covariates. This is a
nonblocking warning, but the covariates should be reviewed before model
fitting. |
retained_sample_after_optional_dropping |
Records the sample size before optional subject-level deletion during standardization. | Initially reports the number of subjects present. After
DataStandard(), the corresponding report is updated with
the original, removed, and retained subject counts and the retained
percentage. |
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.