Proactive Anomaly Screen for Multiple Endpoints Using Bayesian Latent Class Modeling: A k-Step Ahead Approach

TL;DR

This paper introduces a Bayesian latent class model for proactive anomaly detection in clinical trial data, enabling early identification of conflicting data patterns across multiple endpoints to improve data quality and reduce manual query workload.

Contribution

It presents a novel joint Bayesian latent class approach that incorporates risk factors for proactive anomaly detection in clinical trial data, including dynamic predictions at multiple time points.

Findings

Effective early detection of data anomalies demonstrated in simulations.

Real-world data application shows improved anomaly identification.

Model integration into electronic data systems enables automated alerts.

Abstract

In clinical trials, ensuring the quality and validity of data for downstream analysis and results is paramount, thus necessitating thorough data monitoring. This typically involves employing edit checks and manual queries during data collection. Edit checks consist of straightforward schemes programmed into relational databases, though they lack the capacity to assess data intelligently. In contrast, manual queries are initiated by data managers who manually scrutinize the collected data, identifying discrepancies needing clarification or correction. Manual queries pose significant challenges, particularly when dealing with large-scale data in late-phase clinical trials. Moreover, they are reactive rather than predictive, meaning they address issues after they arise rather than preemptively preventing errors. In this paper, we propose a joint model for multiple endpoints, focusing on primary and key secondary measures, using a Bayesian latent class approach. This model incorporates adjustments for risk monitoring factors, enabling proactive, $k$-step ahead, detection of conflicting or anomalous patterns within the data. Furthermore, we develop individualized dynamic predictions at consecutive time-points to identify potential anomalous values based on observed data. This analysis can be integrated into electronic data capture systems to provide objective alerts to stakeholders. We present simulation results and demonstrate effectiveness of this approach with real-world data.