detect_outliers_zscore

Internal function to detect potential outliers in numeric columns of both
datasets using the z-score method. Values with |z| &gt; 3 are flagged.
Results are returned as advisory notes for the user.

internal

A general-purpose toolkit for comparing any two data frames
with optional 'CDISC' (Clinical Data Interchange Standards Consortium)
validation for clinical trial data. Core comparison functions work on
arbitrary datasets: variable-level and observation-level comparison, data
type checking, metadata attribute analysis (types, labels, lengths,
formats), missing value handling, key-based row matching, tolerance-based
numeric comparisons, and group-wise comparisons. Optional z-score
outlier detection is available when enabled. When working with clinical
data, the package additionally
validates 'SDTM' (Study Data Tabulation Model) and 'ADaM' (Analysis Data
Model) datasets against CDISC standards (SDTM IG 3.3/3.4, ADaM IG
1.1/1.2/1.3), automatically detecting domains and flagging non-conformant
variables. Generates unified comparison reports in text or HTML format
with interactive dashboards. For CDISC standards, see
<https://www.cdisc.org/standards>.

Siddharth Lokineni

clinCompare

Dataset Comparison with 'CDISC' Validation for Clinical Trial
Data

detect_outliers_zscore function

<dl><dt>df1</dt>
<dd>First data frame (base).</dd>
<dt>df2</dt>
<dd>Second data frame (compare).</dd>
<dt>threshold</dt>
<dd>Numeric z-score threshold (default 3).</dd></dl>

Arguments

Detect Outliers Using Z-Score Method — detect_outliers_zscore

<dl>

<dt>df1</dt>
<dd>First data frame (base).</dd>


<dt>df2</dt>
<dd>Second data frame (compare).</dd>


<dt>threshold</dt>
<dd>Numeric z-score threshold (default 3).</dd>

</dl>

detect_outliers_zscore: Detect Outliers Using Z-Score Method

Description

Usage

Value

Arguments