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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.
Health stats visible at Monitor.