gsm.qtl
– Introduction to Quality Tolerance Limit (QTL)
Analysis
The gsm.qtl
package is a specialized
module within the Gilead Statistical Monitoring (GSM) framework,
focusing on the Quality Tolerance Limit (QTL) analysis.
It is designed to detect atypical trends of metric at study-level as an
indication of potential data integrity or patient safety concerns using
pre-specified or data-driven thresholds.
Workflows Overview
gsm.qtl
mirrors the modular design of the GSM ecosystem.
A typical workflow includes:
Data Ingestion
Load participant-level trial data (e.g., deviations).Summarization
Aggregate data into study‑level metrics such as rates of deviations, etc.-
Threshold Detection
Compare each study’s metrics to QTL thresholds using:- Z‑score calculations with optional overdispersion adjustments
Flagging
Assign risk levels (Red / Green) based on breaches of the thresholds.Visualization & Reporting
Produce interactive HTML reports with charts, summary tables, and filters for exploration.
Why Use gsm.qtl
?
Modular & Compliant
Leverages the qualified GSM statistical engine with unit testing and familiar reports/outputs.Flexible Thresholds
Supports both static QTLs (predefined in YAML) and data-driven cutoffs (z-scores, quantiles).End-to-End Reporting
Generates clean, reproducible outputs that can be embedded into trial oversight reports.
Statistical Methods for Threshold Flagging
The default statistical method applies normal approximation of binomial distribution to the binary outcome as dynamic thresholds. This method is particularly useful for monitoring binary outcomes, such as proportion of participants with eligibility-related deviations or early study discontinuation, where the goal is to identify studies that deviate significantly from expected rates.
For each study, a proportion metric is calculated as
where num is the number of observed events of interest (e.g., participants with protocol deviations related to eligibility, early study discontinuation) and denom is the relevant denominator (e.g., total participants).
This observed proportion is compared against a predefined QTL threshold that is calculated as the following.
The expected mean of QTL,
,
typically derived from historical trial data (e.g., 0.05). This expected
mean,
,
will be configurable in the workflows from study to study, denoted as
nPropRate
in each analysis yaml
’s
meta
field.
To account for natural variability, a tolerance margin is added to the threshold based on a normal approximation to the binomial distribution:
Here,
,
which will also be configurable denoted as nNumDeviations
in the yaml
’s meta
field, controls the
leniency of the threshold.
An excursion from the QTL limit is flagged when
This approach is conceptually similar to a one-sided control limit around the expected rate . Studies exceeding this adjusted limit are flagged for further review as potential outliers in the monitored metric.