Details for function DataCheck

Columns in the itemized checks

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.